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https://drive.google.com/open?id=1EqN2-6ZpJUeCiSpFBGWrvftMZVSbeXSl

This article summarizes the ways in which policing has evolved with the use of technology. They are saying that cities are becoming "smarter" with the growth of technology, but the same can be said for policing too. Data collection has become a large factor when it comes to policing, and can improve the management of pedestrian and everyday traffic. We now have a much larger capacity to analyze massive amounts of technological data, meaning that police officers everyday can rely on technological tactics to do their jobs. For examples, things like automated license plate readers can identify multiple plates at once instead of the officers typing in each individual one to the computer.

Interoperability: is the ability of different systems, devices, applications or products to connect and communicate in a coordinated way, without effort from the end user.

WAN: A wide-area network (WAN) is the technology that connects your offices, data centers, cloud applications, and cloud storage together.

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Predicting future trends and events has become a large part of how we operate as humans everyday. In my opinion, humans are becoming more predictable, which could eventually lead to our downfall. With this being said, predictive analysis could definitely have a positive impact on the world of policing, with regards to catching criminals before they are able to commit a crime. With proper data, statistics, and modelling, we can predict future outcomes and performances, and because people are becoming more predictable, criminals may not see that police will begin to use this tactic, and could get caught simply because they were predicted to do something.
Creating actionable intelligence from massive amounts of data could be seen as contentious or controversial because data does not work that simply. Each data point means something different, and it takes up millions of pieces of data to create something online, and so analyzing massive amounts of data is nearly impossible, at least for humans, but humans are the ones who have to make the technology in order to analyze data, so I would argue that this is still a very grey area for the world of policing and intelligence.

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In mid-2019, an investigative journalism/tech non-profit called MuckRock and Open the Government (OTG), a non-partisan advocacy group, began submitting freedom of information requests to law enforcement agencies across the United States. The goal: to smoke out details about the use of an app rumoured to offer unprecedented facial recognition capabilities to anyone with a smartphone.

Co-founded by Michael Morisy, a former Boston Globe editor, MuckRock specializes in FOIs and its site has grown into a publicly accessible repository of government documents obtained under access to information laws.

As responses trickled in, it became clear that the MuckRock/OTG team had made a discovery about a tech company called Clearview AI. Based on documents obtained from Atlanta, OTG researcher Freddy Martinez began filing more requests, and discovered that as many as 200 police departments across the U.S. were using Clearview's app, which compares images taken by smartphone cameras to a sprawling database of 3 billion open-source photographs of faces linked to various forms of personal information (e.g., Facebook profiles). It was, in effect, a point-click-and-identify system that radically transformed the work of police officers.

The documents soon found their way to a New York Times reporter named Kashmir Hill, who, in January 2020, published a deeply investigated feature about Clearview, a tiny and secretive start-up with backing from Peter Thiel, the Silicon Valley billionaire behind Paypal and Palantir Technologies. Among the story's revelations, Hill disclosed that tech giants like Google and Apple were well aware that such an app could be developed using artificial intelligence algorithms feeding off the vast storehouse of facial images uploaded to social media platforms and other publicly accessible databases. But they had opted against designing such a disruptive and easily disseminated surveillance tool.

The Times story set off what could best be described as an international chain reaction, with widespread media coverage about the use of Clearview's app, followed by a wave of announcements from various governments and police agencies about how Clearview's app would be banned. The reaction played out against a backdrop of news reports about China's nearly ubiquitous facial recognition-based surveillance networks.

Canada was not exempt. To Surveil and Predict, a detailed examination of "algorithmic policing" published this past fall by the University of Toronto's Citizen Lab, noted that officers with law enforcement agencies in Calgary, Edmonton and across Greater Toronto had tested Clearview's app, sometimes without the knowledge of their superiors. Investigative reporting by the Toronto Star and Buzzfeed News found numerous examples of municipal law enforcement agencies, including the Toronto Police Service, using the app in crime investigations. The RCMP denied using Clearview even after it had entered into a contract with the company - a detail exposed by Vancouver's The Tyee.

With federal and provincial privacy commissioners ordering investigations, Clearview and the RCMP subsequently severed ties, although Citizen Lab noted that many other tech companies still sell facial recognition systems in Canada. "I think it is very questionable whether [Clearview] would conform with Canadian law," Michael McEvoy, British Columbia's privacy commissioner, told the Star in February.

There was fallout elsewhere. Four U.S. cities banned police use of facial recognition outright, the Citizen Lab report noted. The European Union in February proposed a ban on facial recognition in public spaces but later hedged. A U.K. court in April ruled that police facial recognition systems were "unlawful," marking a significant reversal in surveillance-minded Britain. And the European Data Protection Board, an EU agency, informed Commission members in June that Clearview's technology violates Pan-European law enforcement policies. As Rutgers University law professor and smart city scholar Ellen Goodman notes "There's been a huge blowback" against the use of data-intensive policing technologies.

There's nothing new about surveillance or police investigative practices that draw on highly diverse forms of electronic information, from wire taps to bank records and images captured by private security cameras. Yet during the past decade or so, dramatic advances in big data analytics, biometrics and AI, stoked by venture capital and law enforcement agencies eager to invest in new technology, have given rise to a fast-growing data policing industry. As the Clearview story showed, regulation and democratic oversight have lagged far behind the technology.

U.S. startups like PredPol and HunchLab, now owned by ShotSpotter, have designed so-called "predictive policing" algorithms that use law enforcement records and other geographical data (e.g. locations of schools) to make statistical guesses about the times and locations of future property crimes. Palantir's law-enforcement service aggregates and then mines huge data sets consisting of emails, court documents, evidence repositories, gang member databases, automated licence plate readers, social media, etc., to find correlations or patterns that police can use to investigate suspects.

Yet as the Clearview fallout indicated, big data policing is rife with technical, ethical and political landmines, according to Andrew Ferguson, a University of the District Columbia law professor. As he explains in his 2017 book, The Rise of Big Data Policing, analysts have identified an impressive list: biased, incomplete or inaccurate data, opaque technology, erroneous predictions, lack of governance, public suspicions about surveillance and over-policing, conflicts over access to proprietary algorithms, unauthorized use of data and the muddied incentives of private firms selling law enforcement software.

At least one major study found that some police officers were highly skeptical of predictive policing algorithms. Other critics point out that by deploying smart city sensors or other data-enabled systems, like transit smart cards, local governments may be inadvertently providing the police with new intelligence sources. Metrolinx, for example, has released Presto card user information to police while London's Metropolitan Police has made thousands of requests for Oyster card data to track criminals, according to The Guardian. "Any time you have a microphone, camera or a live-feed, these [become] surveillance devices with the simple addition of a court order," says New York civil rights lawyer Albert Cahn, executive director of the Surveillance Technology Oversight Project (STOP).

The authors of the Citizen Lab study, lawyers Kate Robertson, Cynthia Khoo and Yolanda Song, argue that Canadian governments need to impose a moratorium on the deployment of algorithmic policing technology until the public policy and legal frameworks can catch up.

Data policing was born in New York City in the early 1990s when then-police Commissioner William Bratton launched "Compstat," a computer system that compiled up-to-date crime information then visualized the findings in heat maps. These allowed unit commanders to deploy officers to neighbourhoods most likely to be experiencing crime problems.

Originally conceived as a management tool that would push a demoralized police force to make better use of limited resources, Compstat is credited by some as contributing to the marked reduction in crime rates in the Big Apple, although many other big cities experienced similar drops through the 1990s and early 2000s.

The 9/11 terrorist attacks sparked enormous investments in security technology. The past two decades have seen the emergence of a multi-billion-dollar industry dedicated to civilian security technology, everything from large-scale deployments of CCTVs and cybersecurity to the development of highly sensitive biometric devices - fingerprint readers, iris scanners, etc. - designed to bulk up the security around factories, infrastructure and government buildings.

Predictive policing and facial recognition technologies evolved on parallel tracks, both relying on increasingly sophisticated analytics techniques, artificial intelligence algorithms and ever deeper pools of digital data.

The core idea is that the algorithms - essentially formulas, such as decision-trees, that generate predictions - are "trained" on large tranches of data so they become increasingly accurate, for example at anticipating the likely locations of future property crimes or matching a face captured in a digital image from a CCTV to one in a large database of headshots. Some algorithms are designed to use a set of rules with variables (akin to following a recipe). Others, known as machine learning, are programmed to learn on their own (trial and error).

The risk lies in the quality of the data used to train the algorithms - what was dubbed the " garbage-in-garbage-out" problem in a study by the Georgetown Law Center on Privacy and Technology. If there are hidden biases in the training data - e.g., it contains mostly Caucasian faces - the algorithm may misread Asian or Black faces and generate "false positives," a well-documented shortcoming if the application involves a identifying a suspect in a crime.

Similarly, if a poor or racialized area is subject to over-policing, there will likely be more crime reports, meaning the data from that neighbourhood is likely to reveal higher-than-average rates of certain types of criminal activity, a data point that would justify more over-policing and racial profiling. Some crimes are under-reported, and don't influence these algorithms.

Other predictive and AI-based law enforcement technologies, such as "social network analysis" - an individual's web of personal relationships, gleaned, for example, from social media platforms or examined by cross-referencing of lists of gang members - promised to generate predictions that individuals known to police were at risk of becoming embroiled in violent crimes.

This type of sleuthing seemed to hold out some promise. In one study, criminologists at Cardiff University found that "disorder-related" posts on Twitter reflected crime incidents in metropolitan London - a finding that suggests how big data can help map and anticipate criminal activity. In practise, however, such surveillance tactics can prove explosive. This happened in 2016, when U.S. civil liberties groups revealed documents showing that Geofeedia, a location-based data company, had contracts with numerous police departments to provide analytics based on social media posts to Twitter, Facebook, Instagram, etc. Among the individuals targeted by the company's data: protestors and activists. Chastened, the social media firms rapidly blocked Geofeedia's access.

In 2013, the Chicago Police Department began experimenting with predictive models that assigned risk scores for individuals based on criminal records or their connections to people involved in violent crime. By 2019, the CPD had assigned risk scores to almost 400,000 people, and claimed to be using the information to surveil and target "at-risk" individuals (including potential victims) or connect them to social services, according to a January 2020 report by Chicago's inspector general.

These tools can draw incorrect or biased inferences in the same way that overreliance on police checks in racialized neighbourhoods results in what could be described as guilt by address. The Citizen Lab study noted that the Ontario Human Rights Commission identified social network analysis as a potential cause of racial profiling. In the case of the CPD's predictive risk model, the system was discontinued in 2020 after media reports and internal investigations showed that people were added to the list based solely on arrest records, meaning they might not even have been charged, much less convicted of a crime.

Early applications of facial recognition software included passport security systems or searches of mug shot databases. But in 2011, the Insurance Corporation of B.C. offered Vancouver police the use of facial recognition software to match photos of Stanley Cup rioters with driver's licence images - a move that prompted a stern warning from the province's privacy commissioner. In 2019, the Washington Post revealed that FBI and Immigration and Customs Enforcement (ICE) investigators regarded state databases of digitized driver's licences as a "gold mine for facial recognition photos" which had been scanned without consent.

In 2013, Canada's federal privacy commissioner released a report on police use of facial recognition that anticipated the issues raised by Clearview app earlier in 2020. "[S]trict controls and increased transparency are needed to ensure that the use of facial recognition conforms with our privacy laws and our common sense of what is socially acceptable." (Canada's data privacy laws are only now being considered for an update.)

The technology, meanwhile, continues to gallop ahead. New York civil rights lawyer Albert Cahn points to the emergence of "gait recognition" systems, which use visual analysis to identify individuals by their walk; these systems are reportedly in use in China. "You're trying to teach machines how to identify people who walk with the same gait," he says. "Of course, a lot of this is completely untested."

The predictive policing story evolved somewhat differently. The methodology grew out of analysis commissioned by the Los Angeles Police Department in the early 2010s. Two data scientists, Jeff Brantingham and George Mohler, used mathematical modelling to forecast copycat crimes based on data about the location and frequency of previous burglaries in three L.A. neighbourhoods. They published their results and soon set up PredPol to commercialize the technology. Media attention soon followed, as news stories played up the seemingly miraculous power of a Minority Report-like system that could do a decent job anticipating incidents of property crime.

Operationally, police forces used PredPol's system by dividing up precincts in 150-square-metre "cells" that police officers were instructed to patrol more intensively during periods when PredPol's algorithm forecast criminal activity. In the post-2009 credit crisis period, the technology seemed to promise that cash-strapped American municipalities would get more bang for their policing buck.

Other firms, from startups to multinationals like IBM, entered the market with innovations, for example, incorporating other types of data, such as socio-economic data or geographical features, from parks and picnic tables to schools and bars, that may be correlated to elevated incidents of certain types of crime. The reported crime data is routinely updated so the algorithm remains current.

Police departments across the U.S. and Europe have invested in various predictive policing tools, as have several in Canada, including Vancouver, Edmonton and Saskatoon. Whether they have made a difference is an open question. As with several other studies, a 2017 review by analysts with the Institute for International Research on Criminal Policy, at Ghent University in Belgium, found inconclusive results: some places showed improved results compared to more conventional policing, while in other cities, the use of predictive algorithms led to reduced policing costs, but little measurable difference in outcomes.

Revealingly, the city where predictive policing really took hold, Los Angeles, has rolled back police use on these techniques. Last spring, the LAPD tore up its contract with PredPol in the wake of mounting community and legal pressure from the Stop LAPD Spying Coalition, which found that individuals who posed no real threat, mostly Black or Latino, were ending up on police watch lists because of flaws in the way the system assigned risk scores.

"Algorithms have no place in policing," Coalition founder Hamid Khan said in an interview this summer with MIT Technology Review. "I think it's crucial that we understand that there are lives at stake. This language of location-based policing is by itself a proxy for racism. They're not there to police potholes and trees. They are there to police people in the location. So location gets criminalized, people get criminalized, and it's only a few seconds away before the gun comes out and somebody gets shot and killed." (Similar advocacy campaigns, including proposed legislation governing surveillance technology and gang databases, have been proposed for New York City.)

There has been one other interesting consequence: police resistance. B.C.-born sociologist Sarah Brayne, an assistant professor at the University of Texas (Austin), spent two-and-a-half years embedded with the LAPD, exploring the reaction of law enforcement officials to algorithmic policing techniques by conducting ride-alongs as well as interviews with dozens of veteran cops and data analysts. In results published last year, Brayne and collaborator Angèle Christin observed "strong processes of resistance fuelled by fear of professional devaluation and threats of performance tracking."

Before shifts, officers were told which grids to drive through, when and how frequently, and the locations of their vehicles were tracked by an on-board GPS devices to ensure compliance. But Brayne found that some would turn off the tracking device, which they regarded with suspicion. Others just didn't buy what the technology was selling. "Patrol officers frequently asserted that they did not need an algorithm to tell them where crime occurs," she noted.

In an interview, Brayne said that police departments increasingly see predictive technology as part of the tool kit, despite questions about effectiveness or other concerns, like racial profiling. "Once a particular technology is created," she observed," there's a tendency to use it." But Brayne added one other prediction, which has to do with the future of algorithmic policing in the post-George Floyd era - "an intersection," as she says, "between squeezed budgets and this movement around defunding the police."

The widening use of big data policing and digital surveillance poses, according to Citizen Lab's analysis as well as critiques from U.S. and U.K. legal scholars, a range of civil rights questions, from privacy and freedom from discrimination to due process. Yet governments have been slow to acknowledge these consequences. Big Brother Watch, a British civil liberties group, notes that in the U.K., the national government's stance has been that police decisions about the deployment of facial recognition systems are "operational."

At the core of the debate is a basic public policy principle: transparency. Do individuals have the tools to understand and debate the workings of a suite of technologies that can have tremendous influence over their lives and freedoms? It's what Andrew Ferguson and others refer to as the "black box" problem. The algorithms, designed by software engineers, rely on certain assumptions, methodologies and variables, none of which are visible, much less legible to anyone without advanced technical know-how. Many, moreover, are proprietary because they are sold to local governments by private companies. The upshot is that these kinds of algorithms have not been regulated by governments despite their use by public agencies.

New York City Council moved to tackle this question in May 2018 by establishing an "automated decision systems" task force to examine how municipal agencies and departments use AI and machine learning algorithms. The task force was to devise procedures for identifying hidden biases and to disclose how the algorithms generate choices so the public can assess their impact. The group included officials from the administration of Mayor Bill de Blasio, tech experts and civil liberties advocates. It held public meetings throughout 2019 and released a report that November. NYC was, by most accounts, the first city to have tackled this question, and the initiative was, initially, well received.

Going in, Cahn, the New York City civil rights lawyer, saw the task force as "a unique opportunity to examine how AI was operating in city government." But he describes the outcome as "disheartening." "There was an unwillingness to challenge the NYPD on its use of (automated decision systems)." Some other participants agreed, describing the effort as a waste.

If institutional obstacles thwarted an effort in a government the size of the City of New York, what does better and more effective oversight look like? A couple of answers have emerged.

In his book on big data policing, Andrew Ferguson writes that local governments should start at first principles, and urges police forces and civilian oversight bodies to address five fundamental questions, ideally in a public forum:

Can you identify the risks that your big data technology is trying to address?

Can you defend the inputs into the system (accuracy of data, soundness of methodology)?

Can you defend the outputs of the system (how they will impact policing practice and community relationships)?

Can you test the technology (offering accountability and some measure of transparency)?

Is police use of the technology respectful of the autonomy of the people it will impact?

These "foundational" questions, he writes, "must be satisfactorily answered before green-lighting any purchase or adopting a big data policing strategy."

In addition to calling for a moratorium and a judicial inquiry into the uses of predictive policing and facial recognition systems, the authors of the Citizen Lab report made several other recommendations, including: the need for full transparency; provincial policies governing the procurement of such systems; limits on the use of ADS in public spaces; and the establishment of oversight bodies that include members of historically marginalized or victimized groups.

Interestingly, the federal government has made advances in this arena, which University of Ottawa law professor and privacy expert Teresa Scassa describes as "really interesting."

The Treasury Board Secretariat in 2019 issued the " Directive on Automated Decision-Making," which came into effect in April 2020, requires federal departments and agencies, except those involved in national security, to conduct "algorithmic impact assessments" (AIA) to evaluate unintended bias before procuring or approving the use of technologies that rely on AI or machine learning. The policy requires the government to publish AIAs, release software codes developed internally and continually monitor the performance of these systems. In the case of proprietary algorithms developed by private suppliers, federal officials have extensive rights to access and test the software.

In a forthcoming paper, Scassa points out that the directive includes due process rules and looks for evidence of whether systemic bias has become embedded in these technologies, which can happen if the algorithms are trained on skewed data. She also observes that not all algorithm-driven systems generate life-altering decisions, e.g., chatbots that are now commonly used in online application processes. But where they are deployed in "high impact" contexts such as policing, e.g., with algorithms that aim to identify individuals caught on surveillance videos, the policy requires "a human in the loop."

The directive, says Scassa, "is getting interest elsewhere," including the U.S. Ellen Goodman, at Rutgers, is hopeful this approach will gain traction with the Biden administration. In Canada, where provincial governments oversee law enforcement, Ottawa's low-key but seemingly thorough regulation points to a way for citizens to shine a flashlight into the black box that is big data policing.

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From facial recognition, to predictive technologies, big data policing is rife with technical, ethical and political landmines

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In mid-2019, an investigative journalism/tech non-profit called MuckRock and Open the Government (OTG), a non-partisan advocacy group, began submitting freedom of information requests to law enforcement agencies across the United States. The goal: to smoke out details about the use of an app rumoured to offer unprecedented facial recognition capabilities to anyone with a smartphone.

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https://securityintelligence.com/investigating-iot-crime-in-the-age-of-connected-devices/

This article summarizes the pros and cons of what we know as the "internet of things". It explains that, yes, the internet and our new and improved security systems are great and handy. However, we must be vigilant when it comes to throwing our entire lives online, and securing our homes with technology. Things like smart homes can be hacked into online, and your life could be at risk as a result. Criminals are beginning to not only target online platforms, but they are targeting people, medical devices, smart homes, kitchen appliances, these people are evolving as technology evolves, and we simply can't keep up. We need to realize that anything connected to the internet can be hacked into, like we learned in class, things like pacemakers can be hacked, which is a scary thought because this is something that controls someone's heart.

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What is Reverse Tabnabbing and What Can You Do to Stop It?

Tabnabbing is a phishing method in which attackers take advantage of victims' unattended browser tabs. After hijacking an inactive tab and redirecting it to malicious URLs, an attacker can perform a phishing attack and execute scripts. With reverse tabnabbing, on...

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Investigating IoT Crime in the Age of Connected Devices

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Tabnabbing is a phishing method in which attackers take advantage of victims' unattended browser tabs. After hijacking an inactive tab and redirecting it to malicious URLs, an attacker can perform a phishing attack and execute scripts. With reverse tabnabbing, on...

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https://www.cam.ac.uk/research/news/use-of-body-worn-cameras-sees-complaints-against-police-virtually-vanish-study-finds
-This article does a nice job of summarizing what it means for officers to wear body cams. Personally, I believe that body cams are a great invention for police use, as they provide proof and transparency of encounters with the public. As the article states, complaints made towards police have decreased as a result of body cams, mostly because the likelihood of successfully lying about a situation is nearly impossible with a camera on you. Body cams also cool down situations that may become hostile because someone knows they are on camera.

Metadata: defined as the data providing information about one or more aspects of the data; it is used to summarize basic information about data that can make tracking and working with specific data easier. Some examples include: Means of creation of the data. Purpose of the data. Time and date of creation.

Bandwidth: The maximum amount of data transmitted over an internet connection in a given amount of time. Bandwidth is often mistaken for internet speed when it's actually the volume of information that can be sent over a connection in a measured amount of time – calculated in megabits per second (Mbps).

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Body-worn cameras are fast becoming standard kit for frontline law enforcers, trumpeted by senior officers and even the US President as a technological 'fix' for what some see as a crisis of police legitimacy. Evidence of effectiveness has, however, been limited in its scope.

Now, new results from one of the largest randomised-controlled experiments in the history of criminal justice research, led by the University of Cambridge's Institute of Criminology, show that the use by officers of body-worn cameras is associated with a startling 93% reduction in citizen complaints against police.

Researchers say this may be down to wearable cameras modifying behaviour through an 'observer effect': the awareness that encounters are recorded improves both suspect demeanour and police procedural compliance. Essentially, the "digital witness" of the camera encourages cooler heads to prevail.

The experiment took place across seven sites during 2014 and early 2015, including police from areas such as the UK Midlands and the Californian coast, and encompassing 1,429,868 officer hours across 4,264 shifts in jurisdictions that cover a total population of two million citizens. The findings are published today in the journal Criminal Justice and Behaviour.

The researchers write that, if levels of complaints offer at least some guide to standards of police conduct - and misconduct - these findings suggest that use of body-worn cameras are a "profound sea change in modern policing".

"Cooling down potentially volatile police-public interactions to the point where official grievances against the police have virtually vanished may well lead to the conclusion that the use of body-worn cameras represents a turning point in policing," said Cambridge criminologist and lead author Dr Barak Ariel.

"There can be no doubt that body-worn cameras increase the transparency of frontline policing. Anything that has been recorded can be subsequently reviewed, scrutinised and submitted as evidence."

"Individual officers become more accountable, and modify their behaviour accordingly, while the more disingenuous complaints from the public fall by the wayside once footage is likely to reveal them as frivolous.

"The cameras create an equilibrium between the account of the officer and the account of the suspect about the same event - increasing accountability on both sides."

However, Ariel cautions that one innovation, no matter how positive, is unlikely to provide a panacea for a deeply rooted issue such as police legitimacy.

Complaints against police are costly: both financially and in terms of public trust, say researchers. In the US, complaints can be hugely expense - not least through multimillion-dollar lawsuits. In the UK last year, the IPCC reported a continuous rise in complaints across the majority of forces.

Ariel worked with colleagues from RAND Europe and six different police forces: West Midlands, Cambridgeshire, West Yorkshire, the Police Service of Northern Ireland, and Rialto and Ventura in California, to conduct the vast experiment.

Each trial was managed by a local point of contact, either an officer or civilian staff member - all graduates of the Cambridge University Police Executive Programme.

Every week for a year, the researchers randomly assigned each officer shift as either with cameras (treatment) or without (control), with all officers experiencing both conditions.

Across all seven trial sites during the 12 months preceding the study, a total of 1,539 complaints were lodged against police, amounting to 1.2 complaints per officer. By the end of the experiment, complaints had dropped to 113 for the year across all sites - just 0.08 complaints per officer - marking a total reduction of 93%.

Surprisingly, the difference between the treatment and control groups once the experiment began was not statistically significant; nor was the variations between the different sites.

Yet the before/after difference caused by the overall experimental conditions across all forces was enormous. While only around half the officers were wearing cameras at any one time, complaints against police right across all shifts in all participating forces almost disappeared.

Researchers say this may be an example of "contagious accountability": with large scale behavioural change - in officers but also perhaps in the public - seeping into almost all interactions, even during camera-less control shifts, once the experiment had introduced camera protocols to participating forces.

"It may be that, by repeated exposure to the surveillance of the cameras, officers changed their reactive behaviour on the streets - changes that proved more effective and so stuck," said co-author Dr Alex Sutherland of RAND Europe.

"With a complaints reduction of nearly 100% across the board, we find it difficult to consider alternatives to be honest," he said.

Critically, researchers say these behaviour changes rely on cameras recording entire encounters, and officers issuing an early warning that the camera is on - reminding all parties that the 'digital witness' is in play right from the start, and triggering the observer effect.

In fact, results from the same experiment, published earlier this year, suggest that police use-of-force and assaults on officers actually increase if a camera is switched on in the middle of an interaction, as this can be taken as an escalation of the situation by both officer and suspect.

"The jolt of issuing a verbal reminder of filming at the start of an encounter nudges everyone to think about their actions more consciously. This might mean that officers begin encounters with more awareness of rules of conduct, and members of the public are less inclined to respond aggressively," explained Ariel.

"We suspect that this is the 'treatment' that body-worn cameras provide, and the mechanism behind the dramatic reduction in complaints against police we have observed in our research."
Drs Barak Ariel and Alex Sutherland will be giving a public talk on this research and the future of policing at the Cambridge Festival of Ideas on Monday 17 October. Book a free place here: http://www.festivalofideas.cam.ac.uk/events/body-worn-cameras-safety-or-...

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digital technology school of the humanities and social sciences

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-institute of criminology institute of criminology

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school of the humanities and social sciences

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Use of body-worn cameras sees complaints against police 'virtually vanish', study finds

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Body-worn cameras are fast becoming standard kit for frontline law enforcers, trumpeted by senior officers and even the US President as a technological 'fix' for what some see as a crisis of police legitimacy. Evidence of effectiveness has, however, been limited in its scope.

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https://drive.google.com/file/d/1QFFQVv0oTlnmvIFbcMDehLMPiNMk9S9T/view
-This article provides a good overview on open source intelligence and how it can be used in the field of law enforcement. Open source intelligence is anything gathered on the internet that is open to everyone. Police use open source intelligence when doing background checks for their candidates. We must be careful of what we post on the internet freely because hackers will use your open source intelligence to gain access into your life.

https://www.makeuseof.com/tag/crime-terrorism-security-dark-side-social-media/
-This article provides an overview on how terrorists use social media platforms in cybercrime. Millions of people have multiple different social media accounts, meaning that they have many different ways of access into their life. Groups like ISIS have used social media platforms to gain unauthorized access into people's lives, and so it is important to first limit what you post on social media, and keep everything private along with strong passwords.

Lurker: "a person who lurks, in particular a user of an internet message board or chat room who does not participate."

Clickbait: "something (such as a headline) designed to make readers want to click on a hyperlink especially when the link leads to content of dubious value or interest" Clickbait is often used by online hackers in order for people to click on links that will allow access into their online lives.
https://www.merriam-webster.com/dictionary/clickbait

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Open source intelligence, social media and law enforcement.pdf

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Biometrics: Biometrics are biological measurements — or physical characteristics — that can be used to identify individuals. For example, fingerprint mapping, facial recognition, and retina scans are all forms of biometric technology, but these are just the most recognized options.
https://www.kaspersky.com/resource-center/definitions/biometrics

Multi-factor authentication: Multi-factor authentication is a layered approach to securing data and applications where a system requires a user to present a combination of two or more credentials to verify a user's identity for login.
-This is important in today's age of growing technology, where it makes it increasingly harder for hackers to get into your personal information.

From the video that was provided to us in class, it is important for us as people to better manage our identity. Authenticating our identity can be done in different ways, such as two-factor or multi-factor authentication. Having simply one password that access's our entire lives is dangerous and naive.

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Biometrics are rising as an advanced layer to many personal and enterprise security systems. With the unique identifiers of your biology and behaviors, this may seem foolproof. However, biometric identity has made many cautious about its use as standalone authentication.

Modern cybersecurity is focused on reducing the risks for this powerful security solution: traditional passwords have long been a point of weakness for security systems. Biometrics aims to answer this issue by linking proof-of-identity to our bodies and behavior patterns.

In this article, we'll explore the basics of how cybersecurity uses biometrics. To help break things down, we'll answer some common biometrics questions:

What is the meaning of biometric?

What is biometric data?

What is a biometric scanner?

What are the risks of biometric security?

How can we make biometrics more secure?

To begin, let's start off with the basics.

What is Biometrics?

For a quick biometrics definition: Biometrics are biological measurements - or physical characteristics - that can be used to identify individuals. For example, fingerprint mapping, facial recognition, and retina scans are all forms of biometric technology, but these are just the most recognized options.

Researchers claim the shape of an ear, the way someone sits and walks, unique body odors, the veins in one's hands, and even facial contortions are other unique identifiers. These traits further define biometrics.

Types of Biometrics

While they can have other applications, biometrics have been often used in security, and you can mostly label biometrics into three groups:

1. Biological biometrics

2. Morphological biometrics

3. Behavioral biometrics

Biological biometrics use traits at a genetic and molecular level. These may include features like DNA or your blood, which might be assessed through a sample of your body's fluids.

Morphological biometrics involve the structure of your body. More physical traits like your eye, fingerprint, or the shape of your face can be mapped for use with security scanners.

Behavioral biometrics are based on patterns unique to each person. How you walk, speak, or even type on a keyboard can be an indication of your identity if these patterns are tracked.

Biometric Security Explained

Biometric identification has a growing role in our everyday security. Physical characteristics are relatively fixed and individualized - even in the case of twins. Each person's unique biometric identity can be used to replace or at least augment password systems for computers, phones, and restricted access rooms and buildings.

Once biometric data is obtained and mapped, it is then saved to be matched with future attempts at access. Most of the time, this data is encrypted and stored within the device or in a remote server.

Biometrics scanners are hardware used to capture the biometric for verification of identity. These scans match against the saved database to approve or deny access to the system.

In other words, biometric security means your body becomes the "key" to unlock your access.

Biometrics are largely used because of two major benefits:

Convenience of use: Biometrics are always with you and cannot be lost or forgotten.

Difficult to steal or impersonate: Biometrics can't be stolen like a password or key can.

While these systems are not perfect, they offer tons of promise for the future of cybersecurity.

Examples of Biometric Security

In practice, biometric security has already seen effective use across many industries.

Advanced biometrics are used to protect sensitive documents and valuables. Citibank already uses voice recognition, and the British bank Halifax is testing devices that monitor heartbeat to verify customers' identities. Ford is even considering putting biometric sensors in cars.

Biometrics are incorporated in e-Passports throughout the world. In the United States, e-passports have a chip that contains a digital photograph of one's face, fingerprint, or iris, as well as technology that prevents the chip from being read - and the data skimmed - by unauthorized data readers.

As these security systems are rolled out, we are seeing the pros and cons play out in real-time.

Are Biometric Scanners Safe? - Improvements and Concerns

Biometrics scanners are becoming increasingly sophisticated. You can even find biometrics on phone security systems. For example, the facial recognition technology on Apple's iPhone X projects 30,000 infrared dots onto a user's face to authenticate the user by pattern matching. The chance of mistaken identity with the iPhone X biometrics is one in a million, according to Apple.

The LG V30 smartphone combines facial and voice recognition with fingerprint scanning and keeps the data on the phone for greater security. CrucialTec, a sensor manufacturer, links a heart-rate sensor to its fingerprint scanners for two-step authentication. This helps ensure that cloned fingerprints can't be used to access its systems.

The challenge is that biometric scanners, including facial recognition systems, can be tricked. Researchers at the University of North Carolina at Chapel Hill downloaded photos of 20 volunteers from social media and used them to construct 3-D models of their faces. The researchers successfully breached four of the five security systems they tested.

Examples of fingerprint cloning are everywhere. One example from the Black Hat cybersecurity conference demonstrated that a fingerprint can be cloned reliably in about 40 minutes with $10 worth of material, simply by making a fingerprint impression in molding plastic or candle wax.

Germany's Chaos Computer Club spoofed the iPhone's TouchID fingerprint reader within two days of its release. The group simply photographed a fingerprint on a glass surface and used it to unlock the iPhone 5s.

Biometrics - Identity & Privacy Concerns

Biometric authentication is convenient, but privacy advocates fear that biometric security erodes personal privacy. The concern is that personal data could be collected easily and without consent.

Facial recognition is a part of everyday life in Chinese cities, where it's used for routine purchases, and London is famously dotted with CCTV cameras. Now, New York, Chicago, and Moscow are linking CCTV cameras in their cities to facial recognition databases to help local police fight crime. Ramping up the technology, Carnegie Mellon University is developing a camera that can scan the irises of people in crowds from a distance of 10 meters.

In 2018, facial recognition was introduced in Dubai airport, where travelers are photographed by 80 cameras as they pass through a tunnel in a virtual aquarium.

Facial recognition cameras are also at work in other airports throughout the world, including those in Helsinki, Amsterdam, Minneapolis-St. Paul, and Tampa. All that data must be stored somewhere, fueling fears of constant surveillance and misuse of data...

Biometric Data Security Concerns

A more immediate problem is that databases of personal information are targets for hackers. For example, when the U.S. Office of Personnel Management was hacked in 2015, cybercriminals made off with the fingerprints of 5.6 million government employees, leaving them vulnerable to identity theft.

Storing biometric data on a device - like the iPhone's TouchID or Face ID - is considered safer than storing it with a service provider, even when the data is encrypted.

That risk is similar to that of a password database, in which hackers may breach the system and steal data that's not effectively secured. The ramifications, however, are significantly different. If a password is compromised, it can be changed. Biometric data, in contract, remains the same forever.

Ways to Protect Biometric Identity

With the risks to privacy and safety, additional protections must be used in biometric systems.

Unauthorized access becomes more difficult when systems require multiple means of authentication, such as life detection (like blinking) and matching encoded samples to users within encrypted domains.

Some security systems also include additional features, such as age, gender, and height, in biometric data to thwart hackers.

India's Unique ID Authority of India Aadhaar program is a good example. Initiated in 2009, the multi-step authentication program incorporates iris scans, fingerprints from all 10 fingers, and facial recognition.

This information is linked to a unique identification card that is issued to each of India's 1.2 billion residents. Soon, this card will be mandatory for anyone accessing social services in India.

Biometrics make a good replacement for usernames as part of a two-factor authentication strategy. That incorporates:

Something you are (biometrics)

Something you have (like a hardware token) or something you know (like a password)

Two-factor authentication makes a powerful combination, especially as IoT devices proliferate. By layering the protection, secured internet devices become less vulnerable to data breaches.

In addition, using a password manager to store any traditional passwords can give you an additional safeguard.

Takeaways on Biometrics

In summary, biometrics remains a growing way to verify identity for cybersecurity systems.

The combined protection of your physical or behavioral signatures with other authentications gives some of the strongest known security. At the moment, it is at a minimum better than using a character-based password as a standalone verification.

Biometric technology offers very compelling solutions for security. Despite the risks, the systems are convenient and hard to duplicate. Plus, these systems will continue to develop for a very long time into the future.
Related articles:

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What is Biometrics Security: How It Works

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Biometrics are rising as an advanced layer to many personal and enterprise security systems. With the unique identifiers of your biology and behaviors, this may seem foolproof. However, biometric identity has made many cautious about its use as standalone authentication.

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This week, Lindsay Brazill from DRPS gave us an interesting and informative talk about her experiences so far as an officer, and related some experiences to our current topic of cybercrime. I thought it was important for her to tell us that depending where you are stationed, you may not have backup right away and you will need to learn how to handle individuals involved in crime with proper communication skills. It was also important for me personally to learn about OPC and what the process is like. She explained that some days are fun but some will be like school where you are sitting and learning, studying, etc. Cybercrime is a growing industry for criminals around the world and it is important for police services to keep up and protect themselves and the community from cybercrime. It is also important for police to learn how to use technology in their favour in order to help solve cases that they are dealing with.

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"People are the weakest link in the information security chain": I can definitely agree with this statement. Humans are susceptible to making mistakes with regards to technology, because of careless passwords, not securing their internet, not using VPNs, etc. An example of this would be using public wifi which creates opportunities for hackers to get into your systems. People also often access websites that may give some of their personal information away without them even knowing, or responding to emails that are considered a scam.

Intrusion Detection: An Intrusion Detection System (IDS) is a monitoring system that detects suspicious activities and generates alerts when they are detected. Based upon these alerts, a security operations centre (SOC) analyst or incident responder can investigate the issue and take the appropriate actions to remediate the threat.
https://www.checkpoint.com/cyber-hub/network-security/what-is-an-intrusion-detection-system-ids/

Security Posture: Security posture refers to an organization's overall cybersecurity strength and how well it can predict, prevent and respond to ever-changing cyber threats.
https://www.techtarget.com/searchsecurity/definition/security-posture#:~:text=Security%20posture%20refers%20to%20an,to%20ever%2Dchanging%20cyber%20threats.

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What is an Intrusion Detection System (IDS)? - Check Point Software

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What is an Intrusion Detection System (IDS), and how it makes it difficult to gain access to an organization’s network undetected.

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Internet of Things - "All of the devices and sensors that are connected to networks and the internet, often associated with terms like smart homes, smart buildings, and smart cities".
-Examples of the internet of things have become very helpful with regards to our everyday lives, but they can also have consequences such as the ability to be hacked into. Other examples include heart monitors, a car with different sensors, smart fridges, etc.

Wireless Devices - "Types of devices: Wireless NIC, WAPs, Mobile devices
Types of wireless technologies (cellular, wifi, Bluetooth, RF, NFC, and more)

Background of the evolution of wireless technologies."
-Wireless devices, along with the internet of things have become an important part of our lives. However, we need to keep digital privacy in mind with things like wifi and bluetooth, because they can easily be stolen and hacked.

https://abcnews.go.com/Business/storyid=3454066&page=1
-This article shows how using public wifi can make you more susceptible to being hacked. Public wifi grants easy access for hackers into your life, and they can access personal things on your device if you are using the same wifi. Securing your network, and avoiding public wifi if possible can go a long way in protecting your privacy.

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LAS VEGAS -- Laptop road warriors beware: Wi-Fi hot spots that let you hop onto the Internet anywhere you travel leave you wide open to hackers.

The basic problem: T-Mobile and AT&T t- the largest providers of Wi-Fi hot spots in coffee shops, bookstores and airports - don't require encryption of data traveling wirelessly between laptops and the Internet. Neither do hotels and municipalities with free Wi-Fi hookups in public areas. T-Mobile and AT&T do recommend customers download and use their free encryption software.

"If you're using Wi-Fi in a public place and you're not getting hacked, it's only because there's nobody around bothering to do it," says Robert Graham, CEO of consultancy Errata Security.

Wi-Fi eavesdropping has long been a security concern. Anyone with a Wi-Fi-equipped laptop can download free Wi-Fi monitoring programs. An eavesdropper can sit up to 100 feet away and monitor what you do on the Net, says Rick Farina, security engineer for wireless security firm AirTight Networks.

There are no estimates of how often this happens. No one has ever been arrested for Wi-Fi hacking. But with Wi-Fi now in mainstream use - T-Mobile and AT&T supply hot spots at more than 15,000 locations in the USA, and cities such as New York and San Francisco supply free public access points - intruders are starting to take advantage, said security experts at recent Black Hat and DefCon security conferences.

Wi-Fi hot-spot hacks "are absolutely taking place," says Tom Brennan, technology risk manager for security consultant Access IT Group. "It's easy to do, and the reward is very high."

Brennan cites an example of a tech systems manager on a lunch break in New York's Bryant Park, who used his laptop via the city's free hot-spot hookup. The manager logged onto his company's network to troubleshoot a computer server. An eavesdropper nabbed his username and password. Later, someone used the information to access the server. "People are on the road using wireless, they get breached, and when they go back into their network, they're owned," Brennan said.

Crooks are using off-the-shelf routers, equipped to broadcast Wi-Fi hookups around the home, to spoof the popular paid services. The spoofer broadcasts a bogus T-Mobile or AT&T connection signal, then captures data transmitted by victims, says Pravin Bhagwat, AirTight's chief technology officer.

"If I'm at a location where a particular hot-spot provider does not provide a service, but still I see its service being advertised, that means it's a spoof," says Bhagwat.

Farino estimates 95% of Wi-Fi data traffic is unencrypted.

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LAS VEGAS -- Laptop road warriors beware: Wi-Fi hot spots that let you hop onto the Internet anywhere you travel leave you wide open to hackers. The basic problem: T-Mobile and AT&T t- the largest providers of Wi-Fi hot spots in coffee shops, bookstores and airports - don't require encryption of data traveling wirelessly between laptops and the Internet.

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Digital Privacy - "is a collective definition that encompasses three sub-related categories; information privacy, communication privacy, and individual privacy. ... While our data is exposed through digital mediums, such as social media, we also more sensitized to privacy issues."
-Digital privacy has become one of the most talked about topics in the world of the internet over the past couple of decades. I feel that digital privacy is something that we don't think about enough, especially because most of our lives are spent online nowadays.

Computer Hacker - "any skilled computer expert that uses their technical knowledge to overcome a problem. While "hacker" can refer to any skilled computer programmer, the term has become associated in popular culture with a "security hacker", someone who, with their technical knowledge, uses bugs or exploits to break into computer systems."
-Relating to digital privacy, computer hackers have become more prevalent in today's society, and we need to be more aware of the content we post on the internet.

https://www.bbc.com/news/uk-england-gloucestershire-63637883 - This article shows an example of a school being hacked and access to students records were compromised. This is a good example of how our digital privacy needs to continue to improve everyday.

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Highly confidential documents from 14 schools have been leaked online by hackers, the BBC can reveal.

One of those was Pates Grammar School in Gloucestershire, targeted by a hacking group called Vice Society.

The documents, seen by the BBC, include children's SEN information, child passport scans, staff pay scales and contract details, taken in 2021 & 2022.

A spokesperson for Pates Grammar School said it took the security of its systems and data extremely seriously.

The Vice Society has been behind a high-profile string of attacks on schools across the UK and the USA in recent months.

It allegedly stole 500 gigabytes of data from the entire Los Angeles Unified School District, according to technology website Wired.

The FBI in America has already released an alert on the group's activities.

When data is stolen, Vice Society makes demands for money before leaking the documents if payment is not made.

The documents stolen from Pates Grammar School were comprehensive, with hackers taking documents using generic search terms.

One folder marked "passports" contains passport scans for pupils and parents on school trips going back to 2011, whereas another marked "contract" contains contractual offers made to staff alongside teaching documents on muscle contractions.

Another folder marked "confidential" contains documents on the headmaster's pay, and student bursary fund recipients.

Alongside information from Pates, the BBC found confidential documents purporting to be from the following establishments on Vice Society's website.

Every school on this list has been contacted for comment.

Carmel College, St Helens

Durham Johnston Comprehensive School (hacked in 2021, documents posted online in January 2022)

Frances King School of English, London/Dublin

Frances King said it hadn't notified parents and pupils, but the hack didn't affect teaching and it was reported to their IT company.

Gateway College, Hamilton, Leicester

Holy Family RC + CE College, Heywood

Lampton School, Hounslow, London

Lampton School issued a statement that read: "Teachers were aware of the breach but we did not inform them of the data that was stolen. The ICO did not tell us to notify the data subjects. We blocked remote access to all but a small number of staff with two-factor authentication, and all our passwords have been reset."

Mossbourne Federation, London

Mossbourne Federation said: " Parents, pupils, staff and all concerned were immediately notified and kept up-to-date during the recovery process. We have fully recovered from the cyber-attack and have returned to normal operations."

Pilton Community College, Barnstaple

Samuel Ryder Academy, St Albans

School of Oriental and African Studies, London

St Paul's Catholic College, Sunbury-on-Thames

Test Valley School, Stockbridge

The De Montfort School, Evesham

The De Montfort School declined to comment.

The School of Oriental and African Studies confirmed it was hacked in September 2022, with staff contracts and budget details leaked among some 18,680 other files.

"We notified staff and students of the incident, and while we were able to prevent the incident escalating, it resulted in a small, limited data breach of files on internal storage.

"The individuals affected have been contacted, and we are continuing to offer support as required," a spokesperson said.

Hackers leaked the information on the dark web, a section of the internet often used by criminals.

The dark web is not indexed on regular search engines, and requires specialist browsing software to access it.

Pates' hacking timeline

The hack at Pates is estimated to have taken place on 28 September, when the school emailed parents to say its IT systems and phone lines were down. A few days later the school emailed again with Gmail accounts it had created for parents to contact.

On 7 October, the headteacher emailed again to say its systems were "accessed by an unauthorised third party." Teaching materials, which relied on Microsoft Teams, were affected, and the school said it had notified the Information Commissioners Office (ICO) and police.

At that time, the headmaster wrote: "There is currently no evidence that data has been stolen or published."

Five days later, the school emailed parents again.

The headmaster wrote: "Regrettably, it now appears that some of our data was taken by the criminal organisation and placed on its dark web site, which is not easily accessible and only available to a limited audience with the technical knowledge and ability to access this specific site.

"If we learn that any significant data has been affected in this way, you will be informed and provided with guidance and assistance."

The ICO and Gloucestershire Police confirmed they were investigating the alleged breaches in 2022.

A spokesperson for Pates Grammar School said: "We are currently working closely with cyber-security specialists to conduct a thorough assessment and analysis of this data.

"We are working with highly experienced forensic investigators to secure our systems and resolve the issue.

"We have successfully restored key systems, minimised the disruption to staff and students, and continue to keep the relevant authorities informed of any new developments."

Ross Brewer, chief revenue officer of cyber-security risk management company SimSpace, said: 'We see the education and healthcare sectors being heavily targeted due to their primary focus being on education and care, not cybersecurity.

"They are typically under resourced in the IT function and are easy prey for the hackers that have no heart and are purely motivated by greed.

"The personal information that can be obtained is highly valuable or in some cases embarrassing. Organisations need to train their teams in the safe cyber range environment, so they know what to look for, how to identify gaps in their protection, and how to continually improve their digital hygiene."

Follow BBC West on Facebook, Twitter and Instagram. Send your story ideas to: bristol@bbc.co.uk

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Schools hit by cyber attack and documents leaked

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Highly confidential documents from 14 schools have been leaked online by hackers, the BBC can reveal. One of those was Pates Grammar School in Gloucestershire, targeted by a hacking group called Vice Society. The documents, seen by the BBC, include children's SEN information, child passport scans, staff pay scales and contract details, taken in 2021 & 2022.