Analyze and Forecast the Cyber Attack Detection Process Using Machine Learning Techniques

Abstract

This project, titled “Analyze and Forecast the Cyber Attack Detection Process using Machine Learning Techniques”, addresses the growing global concern of cybercrime. Cyberattacks cause significant financial losses and their frequency is steadily increasing worldwide. Identifying the criminals and understanding their strategies is essential to strengthen defense mechanisms. Machine learning techniques are applied to analyze real-world data and predict cyber-attack patterns. Five different ML techniques were compared, yielding similar accuracy in detection performance. Among them, the Support Vector Machine (SVM) linear model achieved the highest accuracy rate. The first model provided insights into the types of attacks likely to occur against victims. Logistic Regression proved effective in identifying malicious actors with high success rates. The second model compared offender and victim attributes for predictive identification. Findings show education and wealth reduce victimization risk, making the project valuable for cybercrime departments.