EXPLOITING ARTIFICIAL IMMUNE SYSTEMS TO DETECT UNKNOWN DoS ATTACKS IN REAL-TIME

DoS is still one of the most serious attacks on the Internet. Payload-based approaches are effective to known DOS attacks but are unable to be deployed on high-speed networks. To address this issue, flow-based DOS detection schemes have been proposed for highspeed networks as an effective supplement of payload-based solutions. However, existing flow-based solutions have serious limitations in detecting unknown attacks and efficiently identifying real attack flows buried in the background traffic. In addition, existing solutions also have difficulty to adapt to attack dynamics. To address these issues, this paper proposes a flow-based DOS detection scheme based on Artificial Immune systems. We adopt a tree structure to store flow information such that we can effectively extract useful features from flow information for better detecting DoS attacks. We employ Neighborhood Negative Selection (NNS) as the detection algorithm to detect unknown DoS attacks, and identify attack flows from massive traffic. Because the strong tolerance of NNS, the proposed solution is able to quickly adapt attack dynamics. The experimental results show that this solution is able to effectively detect unknown DoS attack flows and identify attack flows from background traffic. Meanwhile, the theoretical analysis demonstrates that this system can extract flow features more effectively.