CYBER THREAT DETECTION USING AI

Authors

  • Dr.N. Mahendiran assistant professor of pg & research department of computer science, Sri Ramakrishna college of arts & science, Coimbatore. Author

DOI:

https://doi.org/10.70454/IJMRE.2024.40104

Keywords:

Cyberthreat Detection, Artificial Intelligence, Machine Learning, Anomaly Detection

Abstract

This research deals with the improvement of a framework able to detect and read cyber threats in actual time. Using synthetic intelligence strategies inclusive of gadget mastering and deep getting to know, the gadget tries to recognize anomalies and suspicious patterns of ability cyber assaults. The major additives of the proposed gadget are data series mechanisms, facts preprocessing modules, machine learning fashions for chance detection, and a warning mechanism. The studies method uses a systematic method which includes data series, function extraction, version education and assessment levels. Real information units which include network site visitors, machine logs and consumer conduct will be used to teach and validate the effectiveness of artificial intelligence fashions.

References

[1] Artificial Intelligence with Respect to Cyber Security Syed Adnan Jawaid, Department of Computer Science, Washington University of Science and Technology, Vienna, Virginia. VA, Manuscript submitted April 26, 2023; accepted May 15, 2023; published August 14, 2023

[2] Swarm Optimization and Machine Learning Applied to PE, Malware Detection towards Cyber Threat Intelligence, Santosh Jhansi Kattamuri 1,2, Ravi Kiran Varma Penmatsa 2, *, Sujata Chakravarty 1and Venkata Sai Pavan Madabathula

[3] Cyber Threat Monitoring Systems - Comparing Attack Detection Performance of Ensemble Algorithms, Eva Maia, Bruno Reis, Isabel Praça, Adrien Becue, David Lancelin, Samantha Dauguet Demailly & Orlando Sousa

[4] Threats and Opportunities With Ai-Based Cyber Security Intrusion Detection: A Review Bibhu Dash, Meraj Farheen Ansari, Pawankumar Sharma and Azad Ali.

[5] Schultz, M.G.; Eskin, E.; Zadok, F.; Stolfo, S.J. Data Mining Methods for Detection of New Malicious Executables. In Proceedings of the 2001 IEEE Symposium on Security and Privacy, Oakland, CA, USA, 14–16 May 2000.

[6] Namita; Prachi. PE File-Based Malware Detection Using Machine Learning. In Proceedings of International Conference on Artificial Intelligence and Applications; Springer: Singapore, 2021; pp. 113–123.

[7] Wang, J.-H.; Deng, P.S.; Fan, Y.-S.; Jaw, L.-J.; Liu, Y.-C. Virus Detection Using Data Mining Techniques. In Proceedings of the IEEE 37th Annual 2003 International Carnahan Conference on Security Technology, Taipei, Taiwan, 14–16 October 2003.

[8] Sung, A.H.; Xu, J.; Chavez, P.; Mukkamala, S. Static Analyzer of Vicious Executables (SAVE). In Proceedings of the 20th Annual Computer Security Applications Conference, Tucson, AZ, USA, 6–10 December 2004.

[9] Kolter, J.Z.; Maloof, M.A. Learning to Detect Malicious Executables in the Wild. In Proceedings of the 2004 ACM SIGKDD, International Conference on Knowledge Discovery and Data Mining-KDD ’04; ACM Press: New York, NY, USA, 2004.

[10] Kolter, J.Z.; Maloof, M.A. Learning to Detect Malicious Executables in the Wild. In Proceedings of the 2004 ACM SIGKDD, International Conference on Knowledge Discovery and Data Mining-KDD ’04; ACM Press: New York, NY, USA, 2004.

[11] Elovici, Y.; Shabtai, A.; Moskovitch, R.; Tahan, G.; Glezer, C. Applying Machine Learning Techniques for Detection of Malicious, Code in Network Traffic. In Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2007; pp. 44–50

. [12] Ye, Y.; Wang, D.; Li, T.; Ye, D.; Jiang, Q. An Intelligent PE-Malware Detection System Based on Association Mining. J. Comput,Virol. 2008, 4, 323–334.

Downloads

Published

2024-03-30

Issue

Section

Articles

How to Cite

CYBER THREAT DETECTION USING AI. (2024). International Journal of Multidisciplinary Research and Explorer, 4(1), 29-38. https://doi.org/10.70454/IJMRE.2024.40104