Using Machine Learning to Cluster Malicious Network Flows From Gh0st RAT Variants

Cybercriminals have become more and more creative and efficient in their efforts to successfully bypass network security. Reports of unauthorized network intrusions that have compromised enterprise security, resources, and data, plague experts on a day-to-day basis, and will continue to do so if not prevented by a more efficient detection system or method. Currently, attackers use polymorphism, encryption, and obfuscation, among other techniques, to automate and increase variants in an attempt to evade traditional intrusion detection methods such as rule-based techniques.

To address these growing number of network threats and keep abreast with the changing sophistication of network intrusion methods, Trend Micro looked into network flow clustering — a method that leverages the power of machine learning in strengthening current intrusion detection techniques.

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Source: Trend Micro