Integrating Data Mining Techniques in Computer Forensics for Enhanced Cybercrime Investigation and Incident Response

Authors

  • Joy Winston James

Keywords:

Computer Forensics, Data Mining, Cybercrime Investigation, Incident Response, Digital Evidence, Artificial Intelligence

Abstract

Hacking is getting smarter, which makes things harder for law enforcement. Cybercriminals take advantage of flaws in technology and use complicated plans to stay hidden. So, computer forensics is important for looking into cybercrimes, finding the people who did them, and keeping digital proof safe. Due to the huge amount and variety of digital data, traditional methods are not enough. Data mining is a field of AI that gives us powerful tools for looking through huge sets of data and finding hidden patterns. This essay looks at how data mining methods can be used in computer forensics and shows how they can help with hacking investigations and responding to incidents. Case studies and real-life examples are looked at to show what this combination can do and what problems it might cause.

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References

Alrashdi, I., Alqazzaz, A., Aloufi, E., Alharthi, R., Zohdy, M., & Ming, H. (2019). AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning. https://doi.org/10.1109/ccwc.2019.8666450

Apruzzese, G., Andreolini, M., Ferretti, L., Marchetti, M., & Colajanni, M. (2022). Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems. Digital Threats, 3(3), 1–19. https://doi.org/10.1145/3469659

Buczak, A. L., & Guven, E. (2016). A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Communications Surveys and Tutorials/IEEE Communications Surveys and Tutorials, 18(2), 1153–1176. https://doi.org/10.1109/comst.2015.2494502

Dasgupta, D., Akhtar, Z., & Sen, S. (2020). Machine learning in cybersecurity: a comprehensive survey. Journal of Defense Modeling and Simulation, 19(1), 57–106. https://doi.org/10.1177/1548512920951275

Hemalatha, J., Roseline, S., Geetha, S., Kadry, S., & Damaševičius, R. (2021a). An Efficient DenseNet-Based Deep Learning Model for Malware Detection. Entropy, 23(3), 344. https://doi.org/10.3390/e23030344

Hemalatha, J., Roseline, S., Geetha, S., Kadry, S., & Damaševičius, R. (2021b). An Efficient DenseNet-Based Deep Learning Model for Malware Detection. Entropy, 23(3), 344. https://doi.org/10.3390/e23030344

Hemalatha, J., Roseline, S., Geetha, S., Kadry, S., & Damaševičius, R. (2021c). An Efficient DenseNet-Based Deep Learning Model for Malware Detection. Entropy, 23(3), 344. https://doi.org/10.3390/e23030344

Husak, M., Komarkova, J., Bou-Harb, E., & Celeda, P. (2019). Survey of Attack Projection, Prediction, and Forecasting in Cyber Security. IEEE Communications Surveys and Tutorials/IEEE Communications Surveys and Tutorials, 21(1), 640–660. https://doi.org/10.1109/comst.2018.2871866

Ibrishimova, M. D., & Li, K. F. (2019). A Machine Learning Approach to Fake News Detection Using Knowledge Verification and Natural Language Processing. In Advances in intelligent systems and computing (pp. 223–234). https://doi.org/10.1007/978-3-030-29035-1_22

Khan, M. A., Karim, M. R., & Kim, Y. (2019). A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network. Symmetry, 11(4), 583. https://doi.org/10.3390/sym11040583

Khan, Z. F., Alshahrani, S. M., Alghamdi, A., Alangari, S., Altamami, N. I., Alissa, K. A., Alazwari, S., Duhayyim, M. A., & Al-Wesabi, F. N. (2023). Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment. Computer Systems Science and Engineering, 47(1), 855–871. https://doi.org/10.32604/csse.2023.036735

Rashid, M. M., Kamruzzaman, J., Hassan, M. M., Imam, T., & Gordon, S. (2020a). Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques. International Journal of Environmental Research and Public Health/International Journal of Environmental Research and Public Health, 17(24), 9347. https://doi.org/10.3390/ijerph17249347

Rashid, M. M., Kamruzzaman, J., Hassan, M. M., Imam, T., & Gordon, S. (2020b). Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques. International Journal of Environmental Research and Public Health/International Journal of Environmental Research and Public Health, 17(24), 9347. https://doi.org/10.3390/ijerph17249347

Sohn, I. (2021). Deep belief network based intrusion detection techniques: A survey. Expert Systems With Applications, 167, 114170. https://doi.org/10.1016/j.eswa.2020.114170

Sornsuwit, P., & Jaiyen, S. (2019). A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting. Applied Artificial Intelligence, 33(5), 462–482. https://doi.org/10.1080/08839514.2019.1582861

Vinayakumar, R., Soman, K., & Poornachandran, P. (2018). Evaluating deep learning approaches to characterize and classify malicious URL’s. Journal of Intelligent & Fuzzy Systems, 34(3), 1333–1343. https://doi.org/10.3233/jifs-169429

Yan, Z., Zhang, P., & Vasilakos, A. V. (2014). A survey on trust management for Internet of Things. Journal of Network and Computer Applications, 42, 120–134. https://doi.org/10.1016/j.jnca.2014.01.014

Zhang, S., Xie, X., & Xu, Y. (2020). A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity. IEEE Access, 8, 128250–128263. https://doi.org/10.1109/access.2020.3008433

Zhao, D., Traore, I., Sayed, B., Lu, W., Saad, S., Ghorbani, A., & Garant, D. (2013). Botnet detection based on traffic behavior analysis and flow intervals. Computers & Security, 39, 2–16. https://doi.org/10.1016/j.cose.2013.04.007

Chowdhury, S., Khanzadeh, M., Akula, R., Zhang, F., Zhang, S., Medal, H., Marufuzzaman, M. and Bian, L., 2017. Botnet detection using graph-based feature clustering. Journal of Big Data, 4(1), pp.1-23.

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Published

12.06.2024

How to Cite

Joy Winston James. (2024). Integrating Data Mining Techniques in Computer Forensics for Enhanced Cybercrime Investigation and Incident Response. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 4117 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7007

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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