DETECTION AND ATTRIBUTION OF AI-GENERATED CYBER ATTACKS USING BEHAVIORAL AND SEMANTIC FINGERPRINTING
DOI: 
SHARE THIS ARTICLE
REVIEW REPORT
CITE THIS ARTICLE
The artificial intelligence technology is getting better fast. This is changing the way we think about cybersecurity. Artificial intelligence is being used by attackers to launch sneaky cyber attacks. These attacks can be things like phishing or social engineering. They can even get into our systems automatically .These intelligence based attacks are very good at making things look real. This makes them hard to catch using the security systems we have now. Our traditional security systems are good at stopping attacks that always look the same.. They are not good at stopping attacks that can change and adapt.
Keywords
Article Information
Article Metrics
HOW TO CITE
Copy
Mate, R., Zaki, F., Bihade, P. M., Shrawankar, M., Jaiswal, S. & Rawat, K. S. (2026). Detection and Attribution of AI-Generated Cyber Attacks using Behavioral and Semantic Fingerprinting. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i3.427
Mate, Rohini, et al.. "Detection and Attribution of AI-Generated Cyber Attacks using Behavioral and Semantic Fingerprinting." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.427.
Mate, Rohini,Fatima Zaki,Priti Bihade,Monali Shrawankar,Sanskruti Jaiswal, and Kaushal Rawat. "Detection and Attribution of AI-Generated Cyber Attacks using Behavioral and Semantic Fingerprinting." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.427.
References
[2] A. Brissett and J. Wall,"Machine Learning and Watermarking for Accurate Detection of AI-Generated Phishing Emails," Electronics, 2025.
[3] S. Dey, W. Sarma, and S. Tiwari,"AI-Powered Phishing Detection Using NLP and Deep Learning," 2023.
[4] S. Jamal, H. Wimmer, and I. H. Sarker,"An Improved Transformer-Based Model for Detecting Phishing, Spam, and Ham," 2023.
[5] M. Salem and S. J. Stolfo,"Modeling User Search Behavior for Masquerade Detection," 2011.
[6] M. Tavallaee et al.,"A Detailed Analysis of the KDD CUP 99 Dataset," 2009.
[7] I. Sharafaldin, A. Lashkari, and A. Ghorbani,"Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization," 2018.
[8] N. Moustafa and J. Slay,"UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems," 2015.
[9] Y. LeCun, Y. Bengio, and G. Hinton,"Deep Learning," Nature, 2015.
[10] J. Devlin et al.,"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," 2019.
Ethics and Compliance
Indexed In
Similar Articles
Previous Article
