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Abstract
A Security Operations Center (SOC) can be defined as an organized and highly skilled team that uses advanced computer forensics tools to prevent, detect and respond to cybersecurity incidents of an organization. The fundamental aspects of an effective SOC is related to the ability to examine and analyze the vast number of data flows and to correlate several other types of events from a cybersecurity perception. The supervision and categorization of network flow is an essential process not only for the scheduling, management, and regulation of the network’s services, but also for attacks identification and for the consequent forensics’ investigations. A serious potential disadvantage of the traditional software solutions used today for computer network monitoring, and specifically for the instances of effective categorization of the encrypted or obfuscated network flow, which enforces the rebuilding of messages packets in sophisticated underlying protocols, is the requirements of computational resources. In addition, an additional significant inability of these software packages is they create high false positive rates because they are deprived of accurate predicting mechanisms. For all the reasons above, in most cases, the traditional software fails completely to recognize unidentified vulnerabilities and zero-day exploitations. This paper proposes a novel intelligence driven Network Flow Forensics Framework (NF3) which uses low utilization of computing power and resources, for the Next Generation Cognitive Computing SOC (NGC2SOC) that rely solely on advanced fully automated intelligence methods. It is an effective and accurate Ensemble Machine Learning forensics tool to Network Traffic Analysis, Demystification of Malware Traffic and Encrypted Traffic Identification.
Highlights
Network traffic analysis [1] is the method of capture, studying and analyzing network traffic flow for the purpose of performance, security and network services management
The number of misclassifications is related to the False Positive (FP) and False Negative (FN) indices appearing in the confusion Matrix
As shown in the above tables, the ensemble method appears to have the same or a slightly lesser performance across all datasets, compared to the winner algorithm. This fact does not detract in any case from the value of the proposed method considering that the proposed ensemble processing approach builds a robust predictive model that reduces the overfit
Summary
Network traffic analysis [1] is the method of capture, studying and analyzing network traffic flow for the purpose of performance, security and network services management. An alternative is the statistical analysis method of the traffic behavior that is ordered based on characteristics such as interpacket arrival, session, timestamp and so on. Malware is a kind of malicious software used to gain access to network infrastructures without permission, to collect personal information or disrupt computer operation and facilities. It can use any event-handling procedures such as source code, dynamic scripts, or any other active content. The malicious process often tries to destabilize the entire system by bypassing the antivirus software and obfuscate active procedures, network services, and threads from suspicious URLs or registry values [3]
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