Forensic Analysis Model for Investigating Cybercrime Over the Network

Abstract-Despite significant investments in security protocols, the frequency of cybersecurity incidents continues to rise, with traditional methods proving ineffective against complex cyber-attacks. This research aims to address this issue by using a publicly accessible dataset on Advanced Persistent Threats (APTs) to develop a data-driven approach for identifying APT phases within the Cyber Kill Chain framework. APTs are sophisticated and targeted attack strategies that can bypass conventional intrusion detection systems, posing a major challenge for security professionals. The study incorporates several machine learning classifiers, including Naïve Bayes, Bayes Net, KNN, Random Forest, and Support Vector Machine (SVM), to analyze the dataset and identify APT phases, offering a promising method for improving cybersecurity detection and response.