# data[].attributes.headline
An Effective Cost-Sensitive XGBoost Method for
Malicious URLs Detection in Imbalanced Dataset

# data[].attributes.subject
An Effective Cost-Sensitive XGBoost Method for
Malicious URLs Detection in Imbalanced Dataset

# data[].attributes.body
Problem: Imbalanced Class, produced imbalanced ratio between number of positive lables and negative labels is ignored.

Solution: Cost Sensitive XGBoost

Recommendation:

1. Explore improvement of classifiers in terms of training time and rate of malicious URL identification.

2. Explore other techniques like explainable AI, reinforcement learning, domain transfer learning, and multi model learning.

# data[].attributes.body
compatibility with various programming languages

popularity stems from its exceptional performance, often referred to as the "2P's" - speed of processing (speed is attributed to parallelization) and high-quality outputs

incorporates regularization and auto-pruning to prevent overfitting

handles missing values seamlessly through a sparsity-aware algorithm

# data[].attributes.body
Extracting/collecting the features of a URL link using word-based features via word segmentation

# data[].attributes.body
Solutions based on literatures:

DIA-XGBoost

SMOTE-XGBoost

# data[].attributes.headline
xgboost ensembled with other ml models such as random forest or support vector machine 

# data[].attributes.subject
xgboost ensembled with other ml models such as random forest or support vector machine 

# data[].attributes.body
occurs when a machine learning model learns the details and noise in the training data to the extent that it performs well on the training data but poorly testing data

# data[].attributes.body
be able to handle more complex attacks/ URLs and mas efficient in theory mag classify

# data[].attributes.body
URL must be classified (ex. spam, malicious, phishing, and benign)

# data[].attributes.body
Instead of doing one task at a time, multiple tasks are done simultaneously. In the case of XGBoost, when it's working on creating decision trees (a key part of how it learns from data), it doesn't do it all in one go. Instead, it splits the work into smaller parts and works on them at the same time.

# data[].attributes.headline
URL Redirection Attack Mitigation in Social Communication Platform using Data Imbalance Aware Machine Learning Algorithm

# data[].attributes.subject
URL Redirection Attack Mitigation in Social Communication Platform using Data Imbalance Aware Machine Learning Algorithm

# data[].attributes.body
Problem: Poor classification accuracy

Solution: DIA-XGBoost

Recommendation:

1. Present another algo for multiple URL detection and train the model on complicated malicious URLs.

2. Improve classification and detection.

3. Improve concept drift problems in detection of malicious URLs.

# data[].attributes.body
sensitive to: