ARCHIVES
Original Article
The Digital Iliad: A Comparative Analysis of Trojan War Strategy and AI-Driven Malware Detection Frameworks
Dr.John Paul Boopathi.A1
Assistant Professor, Department of English, Sri Krishna Adithya College of Arts and Science, Coimbatore, Tamilnadu, India.
Published Online: March-April 2026
Pages: 38-41
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260602006References
1. Ab Razak, Mohd Faizal, et al. "Trojan Horse Detection Using Machine Learning Algorithms." Journal of Cybersecurity and Privacy, vol.
2, no. 1, 2022, pp. 12-28.
2. Abualhaj, Mohammad M., et al. "Memory-Based Trojan Detection Using Decision Tree Classifiers." Computers & Security, vol. 138,
2024, pp. 103-115.
3. Ahuja, Ravinder, and Swapnil Salunke. "Hybrid Machine Learning and Blockchain Framework for Enhanced Threat Detection."
International Journal of Information Security, vol. 24, no. 2, 2025, pp. 45-62.
4. Azeem, Muhammad, et al. "Comparative Analysis of Machine Learning Classifiers for Malware Classification." IEEE Access, vol. 12,
2024, pp. 14210-25.
5. Chen, Xiao, et al. "The Evolution of Zero-Day Attacks and the Inadequacy of Signature-Based Detection." Journal of Network Security,
vol. 15, no. 3, 2019, pp. 210-25.
6. Homer. The Iliad. Translated by Robert Fagles, Penguin Books, 1990.
7. ---. The Odyssey. Translated by Robert Fagles, Penguin Books, 1996.
8. Kamboj, Sandeep, et al. "Multi-Malware Detection Using File-Based Feature Extraction and Machine Learning." Cybersecurity and
Intelligence, vol. 6, no. 1, 2023, pp. 88-104.
9. Kumar, Pawan, et al. "Ensemble Learning for Malware Detection: A Study on Random Forest and XGBoost." Advanced Computing
Reports, vol. 9, no. 4, 2024, pp. 56-72.
10. Öztürk, Mustafa, and Serkan Hızal. "Evaluation of Machine Learning Models on the CIC-MalMem-2022 Dataset for Obfuscated Malware
Detection." Data Science and Cybersecurity Review, vol. 11, no. 2, 2024, pp. 34-49.
11. Singh, Abhishek, et al. "Advanced Evasion Techniques in Modern Malware: A Survey." Security and Communication Networks, vol.
2019, 2019, pp. 1-18.
12. Singh, Rahul, et al. "Bypassing AI-Based Malware Detectors through API-Call Manipulation." Journal of Forensic Informatics, vol. 13,
no. 1, 2025, pp. 12-29.
13. Song, Jianguo, et al. "Deep Learning for Malware Detection: A 72-Study Systematic Literature Review." Journal of Big Data, vol. 12, no.
1, 2025, pp. 45-70.
14. Talukder, Md. Alamin, and Sharmin Talukder. "Exploratory Data Analysis and Machine Learning for Dynamic Trojan Detection."
International Journal of Computer Science & Engineering, vol. 14, no. 3, 2025, pp. 201-15.
15. Tanikonda, Sumanth, et al. "The Shift toward AI-Driven (AID) Malware: Autonomously Bypassing Security Paradigms." Cyber
Resilience Quarterly, vol. 7, no. 2, 2025, pp. 101-18.
16. Virgil. The Aeneid. Translated by Robert Fitzgerald, Vintage Classics, 1990.
17. Wang, Haoyu, et al. "Taxonomies of Malware and Their Propagation Vectors in Modern Networks." Computing Surveys, vol. 53, no. 4,
2020, pp. 1-35.
2, no. 1, 2022, pp. 12-28.
2. Abualhaj, Mohammad M., et al. "Memory-Based Trojan Detection Using Decision Tree Classifiers." Computers & Security, vol. 138,
2024, pp. 103-115.
3. Ahuja, Ravinder, and Swapnil Salunke. "Hybrid Machine Learning and Blockchain Framework for Enhanced Threat Detection."
International Journal of Information Security, vol. 24, no. 2, 2025, pp. 45-62.
4. Azeem, Muhammad, et al. "Comparative Analysis of Machine Learning Classifiers for Malware Classification." IEEE Access, vol. 12,
2024, pp. 14210-25.
5. Chen, Xiao, et al. "The Evolution of Zero-Day Attacks and the Inadequacy of Signature-Based Detection." Journal of Network Security,
vol. 15, no. 3, 2019, pp. 210-25.
6. Homer. The Iliad. Translated by Robert Fagles, Penguin Books, 1990.
7. ---. The Odyssey. Translated by Robert Fagles, Penguin Books, 1996.
8. Kamboj, Sandeep, et al. "Multi-Malware Detection Using File-Based Feature Extraction and Machine Learning." Cybersecurity and
Intelligence, vol. 6, no. 1, 2023, pp. 88-104.
9. Kumar, Pawan, et al. "Ensemble Learning for Malware Detection: A Study on Random Forest and XGBoost." Advanced Computing
Reports, vol. 9, no. 4, 2024, pp. 56-72.
10. Öztürk, Mustafa, and Serkan Hızal. "Evaluation of Machine Learning Models on the CIC-MalMem-2022 Dataset for Obfuscated Malware
Detection." Data Science and Cybersecurity Review, vol. 11, no. 2, 2024, pp. 34-49.
11. Singh, Abhishek, et al. "Advanced Evasion Techniques in Modern Malware: A Survey." Security and Communication Networks, vol.
2019, 2019, pp. 1-18.
12. Singh, Rahul, et al. "Bypassing AI-Based Malware Detectors through API-Call Manipulation." Journal of Forensic Informatics, vol. 13,
no. 1, 2025, pp. 12-29.
13. Song, Jianguo, et al. "Deep Learning for Malware Detection: A 72-Study Systematic Literature Review." Journal of Big Data, vol. 12, no.
1, 2025, pp. 45-70.
14. Talukder, Md. Alamin, and Sharmin Talukder. "Exploratory Data Analysis and Machine Learning for Dynamic Trojan Detection."
International Journal of Computer Science & Engineering, vol. 14, no. 3, 2025, pp. 201-15.
15. Tanikonda, Sumanth, et al. "The Shift toward AI-Driven (AID) Malware: Autonomously Bypassing Security Paradigms." Cyber
Resilience Quarterly, vol. 7, no. 2, 2025, pp. 101-18.
16. Virgil. The Aeneid. Translated by Robert Fitzgerald, Vintage Classics, 1990.
17. Wang, Haoyu, et al. "Taxonomies of Malware and Their Propagation Vectors in Modern Networks." Computing Surveys, vol. 53, no. 4,
2020, pp. 1-35.
Related Articles
2026
A Strategic Framework for Depth-Dependent Hydroelectric Conversion along the Indian Coastline
2026
Reimagining Development in India: A Critical Analysis of the Viksit Bharat Vision
2026
AI-Enabled Image Description: Bridging the Gap for the Visually Impaired
2026
Perceived Occupational Risks of Emergency Medical Services Personnel
2026
Origin, Growth and recent Development of Integrated Reporting (IR): A theoretical Review
2026