Integrating Network Forensics with Data Mining for Advanced Cybercrime Investigation
DOI:
https://doi.org/10.15662/IJEETR.2022.0405002Keywords:
Network Forensics, Data Mining, Cybercrime Investigation, Anomaly Detection, Intrusion Detection, Pattern Recognition, Machine Learning, Digital Forensics,, Classification Algorithms, Forensic AnalyticsAbstract
Cybercrime is evolving at an unprecedented pace, necessitating sophisticated investigative techniques that can keep up with the dynamic nature of digital threats. Traditional network forensics—focused on capturing, recording, and analyzing network events—provides valuable insights into the origin, nature, and timeline of cyberattacks. However, as the volume and complexity of data increase, the limitations of manual or rule-based forensic analysis become evident. To overcome these challenges, this paper proposes the integration of network forensics with data mining techniques for advanced cybercrime investigation. Data mining enables automated pattern discovery, anomaly detection, and correlation across vast datasets, enhancing the depth and efficiency of forensic analysis.
This paper explores how data mining models such as clustering, classification, and association rule mining can be leveraged to augment forensic capabilities. The literature review highlights recent trends in combining these domains, while the research methodology outlines a hybrid framework tested on simulated cyberattack datasets. Key findings demonstrate improved detection of complex attack patterns, reduced false positives, and faster incident response. The proposed workflow details stages from data collection and preprocessing to model application and forensic interpretation.
While the integration offers significant advantages—including real-time analysis and scalability—it also introduces challenges such as data privacy concerns, model interpretability, and computational overhead. The discussion evaluates these trade-offs and identifies strategies for practical implementation in law enforcement and enterprise environments. The paper concludes by emphasizing the need for continuous model training and the potential of integrating AI and machine learning for future advancements. This research contributes to building a more proactive and intelligent approach to cybercrime investigation, supporting the growing demand for digital justice.
References
1. Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31.
2. Casey, E. (2011). Digital Evidence and Computer Crime: Forensic Science, Computers, and the Internet. Academic Press.
3. Conti, M., Dehghantanha, A., Franke, K., & Watson, S. (2016). Internet of Things security and forensics: Challenges and opportunities. Future Generation Computer Systems, 78, 544–546.
4. Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques. Morgan Kaufmann.
5. Liao, H. J., Lin, C. H. R., Lin, Y. C., & Tung, K. Y. (2013). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36(1), 16-24.
6. Sangkatsanee, P., Wattanapongsakorn, N., & Charnsripinyo, C. (2011). Practical real-time intrusion detection using machine learning approaches. Computer Communications, 34(18), 2227–2235.
7. Zuech, R., Khoshgoftaar, T. M., & Wald, R. (2015). Intrusion detection and Big Heterogeneous Data: A Survey. Journal of Big Data, 2(1), 3.
