Uncovering Threats: Data Mining Techniques for Cyber Security
Keywords:
Intrusion detection framework, Artificial intelligence, Data mining, Cyber security, Cyber resilienceAbstract
To monitor criminal activities such as theft, data alteration, and system interference on one or multiple computers, we create a framework for intrusion detection. Traditional Intrusion Detection Systems (IDS) often struggle to identify the dynamic and sophisticated nature of digital attacks. However, by employing effective techniques, including different forms of artificial intelligence, we can enhance detection rates, minimize false positives, and offer cost-effective solutions. In particular, data mining enables ongoing pattern examination, classification, aggregation, and real-time data processing. This research study presents a focused literature review on advanced intrusion detection methods utilizing data mining and artificial intelligence. We identify relevant publications based on citation frequency or emerging trends to deliver an analysis, synthesis, and concise summary of their contents. Additionally, we highlight the critical importance of data in the realms of data mining and artificial intelligence.
References
Rajasekaran, M., Thanabal, M. S., & Meenakshi, A. (2024). Association rule hiding using enhanced elephant herding optimization algorithm. Automatika, 65(1), 98–107. https://doi.org/10.1080/00051144.2023.2277998
Liu, S., You, S., Yin, H., Lin, Z., Liu, Y., Yao, W., & Sundaresh, L. (2020). Model-free data authentication for cyber security in power systems. IEEE transactions on smart grid, 11(5), 4565–4568. https://doi.org/10.1109/TSG.2020.2986704
Wu, Q., & Shao, Z. (2005). Network anomaly detection using time series analysis. http://dx.doi.org/10.1109/ICAS-ICNS.2005.69
Feldman, R., & Dagan, I. (1995). Knowledge discovery in textual databases (KDT). [presentation]. KDD (Vol. 95, pp. 112–117). https://cdn.aaai.org/KDD/1995/KDD95-012.pdf
Homayoun, S., Dehghantanha, A., Ahmadzadeh, M., Hashemi, S., & Khayami, R. (2020). Know Abnormal, find evil: frequent pattern mining for ransomware threat hunting and intelligence. IEEE transactions on emerging topics in computing, 8(2), 341–351. https://doi.org/10.1109/TETC.2017.2756908
Iqbal, F., Fung, B. C. M., Debbabi, M., Batool, R., & Marrington, A. (2019). Wordnet-based criminal networks mining for cybercrime investigation. IEEE access, 7, 22740–22755. https://doi.org/10.3390/diagnostics14131344
De Boer, M. H. T., Bakker, B. J., Boertjes, E., Wilmer, M., Raaijmakers, S., & van der Kleij, R. (2019). Text mining in cybersecurity: exploring threats and opportunities. Multimodal technologies and interaction, 3(3). https://doi.org/10.3390/mti3030062
Ye, Y., Li, T., Adjeroh, D., & Iyengar, S. (2017). A Survey on malware detection using data mining techniques. ACM computing surveys, 50, 1–40. http://dx.doi.org/10.1145/3073559
Manoj, K. S., & Aithal, P. S. (2020). Data mining and machine learning techniques for cyber security intrusion detection. University Library of Munich, Germany. https://www.academia.edu/download/74487288/C5979029320.pdf
Kolhar, M., Kazi, R. N. A., Mohapatra, H., & Al Rajeh, A. M. (2024). AI-driven real-time classification of ECG signals for cardiac monitoring using I-Alexnet architecture. Diagnostics, 14(13). https://doi.org/10.3390/diagnostics14131344
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