Digital Forensics for Detecting and Investigating Cyber Malicious Activities

Cybersecurity is an urgent concern in this age of rapid expansion of digital infrastructures, especially due to insider threats. These are sophisticated threats where traditional signature-based detection methods have proven much less effective, since these attacks are by people who have legitimate access to sensitive data. In this paper, several ML models, namely Logistic Regression, Random Forest, Support Vector Machine, Decision Trees, and XGBoost, have been experimented with for detecting insider threats in cybersecurity. XGBoost proved to be the best among the compared ML models, with an accuracy of 94.2%. However, the proposed model CNN outperformed all other algorithms and achieved 95.0% accuracy along with the highest precision and F1-score. This confirms that deep learning techniques are much better at capturing complex patterns in cyber activities than ML techniques. While the proposed CNN resulted in excellent performance, several challenges remain to be explored, such as the problem of class imbalance, anomaly detection in real time, and explanation of anomalies. This paper presents a proposal that the integration of advanced machine learning and deep learning models is crucial for improvement in scalable, real-time, and accurate cybersecurity solutions.