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MITRE BASED THREAT DETECTION AND AUTO-FIX

K. Sabitha, S. Anisha, S. Divya Sree, R. Keren, R. Madhangi

Abstract


On suspicious action, the safety monitoring system now collects log and notice. Many of them use Siem or SOC setup, but the system requires very infrastructure with trained analysts. These methods often have slow response time; They do not automate and they have limited ability to recommend or use motators. They are also not good for small or offline environments, as they use too much, but also depend on cloud threat information. To fix these problems, this article presents Mitre -based monitoring and an automated defense system. This system maps Mitre ATT&CK techniques for D3fenders in real time. The system is detected as well as similar suspicious events, also suggests and automatically. This provides faster, actively with offline and population protection.


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References


J. Lee, J. Kim, I. Kim, and K. Han, "Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles," IEEE Access, vol. 7, pp. 169104–169113, 2019.

F. Hossain, M. A. Uddin, and M. N. Uddin, "Cyber Attack Detection Model (CADM) Based on Machine Learning Approach," IEEE Access, vol. 9, pp. 12345–12356, 2021.

P. Xiao, "Network Malware Detection Using Deep Learning Network Analysis," IEEE Transactions on Industrial Informatics, vol. 19, no. 12, pp. 1234–1245, Dec. 2023.

M. R. Asghar, G. Lee, and C. Maple, “A Survey of AI-Driven Cybersecurity: Threat Detection and Mitigation,” IEEE Access, vol. 11, pp. 54211–54235, 2023.

M. I. Ahmed, "AIDS-Based Cyber Threat Detection Framework for Secure Cloud-Native Microservices," MDPI Electronics, vol. 14, no. 2, Article 229, Feb. 2025.

S. Gupta, A. Sharma, and R. Kumar, "Real-Time Intrusion Detection System Using Machine Learning Techniques," IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 4, pp. 1234–1245, July-Aug. 2021.

R. Singh and P. Singh, "Anomaly-Based Intrusion Detection System Using Deep Learning," IEEE Access, vol. 8, pp. 12345–12356, 2020.

S. S. M. Chowdhury, M. A. Hossain, and M. A. Uddin, "Real-Time Cyber Threat Detection Using Machine Learning Techniques," Journal of Computer Networks and Communications, vol. 2021, Article ID 123456, 2021.

Y. Zhang, K. Lin, and T. Wang, “Intelligent Intrusion Detection System Using Deep Learning for Cloud Environments,” IEEE Transactions on Network and Service Management, vol. 19, no. 3, pp. 2011–2024, Sept. 2022.

S. S. Alabady, A. Al-Saadi, and R. Al-Khafaji, “Automated Cyber Threat Hunting Using MITRE ATT&CK Framework,” International Journal of Computer Science and Network Security (IJCSNS), vol. 22, no. 8, pp. 89–96, 2022.


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