PROJECT TITLE :
A Support Vector Machine-Based Framework for Detection of Covert Timing Channels
Covert channels exploit facet channels among existing network resources to transmit secret messages. They are integrated into the weather of network resources that were not even designed for the aim of communication. This means that ancient security measures like firewalls cannot detect them. Their ability to evade detection makes covert channels a grave security concern. Hence, it's imperative to detect and disrupt them. However, a generic mechanism that can be used to detect a giant selection of covert channels is missing. In this paper, we propose a support vector machine (SVM)-based framework for reliable detection of covert communications. The machine learning framework utilizes the fingerprints derived from the traffic under investigation to classify the traffic as covert or overt. We tend to trained our classifier using the fingerprints from four well-liked and diverse covert timing channel algorithms and tested each of them independently. We tend to have shown that the machine learning framework has nice potential to blindly detect covert channels, even when the covert message size is reduced.
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