A Significant Detection of APT using MD5 Hash Signature and Machine Learning Approach
A Significant Detection of APT using MD5 Hash Signature and Machine Learning Approach
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : R C. Veena, S H. Brahmananda
|DOI : 10.14445/22315381/IJETT-V70I4P208|
How to Cite?
R C. Veena, S H. Brahmananda, "A Significant Detection of APT using MD5 Hash Signature and Machine Learning Approach," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp. 95-106, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P208
The overwhelming penetration of the internet has created day-to-day life easy. Associated with the rich benefits of the internet come new threats and challenges. An Advanced Persistent Threat (APT) is one such threat caused by suspicious agents accessing data or surveillance servers over a prolonged period. APT attacks have been using a variety of specialized tools and techniques. APT hackers and malware are more common and improvised than ever. Attackers have previously aimed at a system for financial and personal benefit. The type of attack includes several other political motives supported by governments or nations. Nations like the United States, India, Russia, and the U.K. are sufferers. APT involves several stages and a definite approach to operational strategy. Besides, techniques and technologies used in APT attacks vary to camouflage the surveillance applications and penetrate unsuspecting networks. This work presents a Machine Learning (ML) Algorithm-based APT Attacks detection framework. MD5 is even more hazardous than previously thought in cryptography techniques. Attackers can impersonate clients to servers that support MD5 hashing for handshake transcripts. The proposed detection framework resulted in highly effective detection of APT attacks at the initial stage based on the MD5 signature using the ML approach. More than 50% of antivirus software has validated the identified MD5 signature as malicious. This detection framework prevents APTs from fast-spreading from compromising a single computer to taking over several systems or the complete infrastructure. The developed system got trained with 76 types of APT signatures. The total number of threats variant used for training is 645. The proposed ML framework has an accuracy of 99% compared to the published accuracy of 96.1%  for early detection of APT from an unknown domain.
APT, MD5 Hashing, Network Security, Hackers, Machine Learning, Threat Hunting.
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