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International Journal of Science, Strategic Management and Technology

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DETECTION AND ATTRIBUTION OF AI-GENERATED CYBER ATTACKS USING BEHAVIORAL AND SEMANTIC FINGERPRINTING

AUTHORS:
Rohini Mate
Fatima Zaki
Dr.Priti M Bihade
Monali Shrawankar
Sanskruti Jaiswal
Kaushal Singh Rawat
Mentor
Affiliation
Department of Computer Science and Engineering (Cyber Security)/ G H Raisoni College of Engineering and Management/ Rashtrasant Tukadoji Maharaj Nagpur University, N
DOI: 10.55041/ijsmt.v2i3.427
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

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.

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 04
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Apr 03 2026
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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
[1] F. Heiding, S. Lermen, A. Kao, B. Schneier, and A. Vishwanath,"Evaluating Large Language Models' Capability to Launch Fully Automated Spear Phishing Campaigns," 2025.

[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
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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