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In machine learning, collinearity is a complex puzzle for both seasoned professionals and newbies alike. Machine learning (ML) algorithms are optimised for predictive accuracy not explainability of predictors on the target. Also, most solutions for addressing collinearity, such as the ‘Variance Inflation Score’, and ‘Pearson’s cross-correlation analysis’ potentially leads to massive information loss in pre-processing.
Scripted network attacks refer to attacks automatically generated using automated scripts e.g DOS attacks and Probe attacks. In network traffic analysis, it is often cumbersome to separate anomalous traffic i.e generated by scripted codes from legitimate traffic as these scripts are usually written to evade most intrusion detection systems. Scripted codes for attacks are written to send X amount of traffic to certain target(s) in X amount of spaced time unit.
Creating feature vectors for separating malicious and benign URLs In this article, we walk through developing a simple feature set representation for identifying malicious URLs. We will create feature vectors for URLs and use these to develop a classification model for identifying malicious URLs. To evaluate how good the features are in separating malicious URLs from benign URLs, we build a Decision-Tree based machine learning model to predict the maliciousness of a given URL.
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