DDoS attack detection in IEEE 802.16 based networks

Abstract Achieving high data rate transmission, WiMAX has acquired noticeable attention by communication industry. One of the vulnerabilities of the WiMAX network which leads to DDoS attack is sending a high volume of ranging request messages to base station (BS) in the initial network entry process. In the initial network entry process, BS and subscriber station (SS) exchange management messages. Since some of these messages are not authenticated, malicious SSs can attack the network by exploiting this vulnerability which may increase the traffic load of the BS and prevent it from serving the SSs. So, detecting such attacks is one of the most important issues in such networks. In this research, an artificial neural network (ANN) based approach is proposed in order to detect DDoS attacks in IEEE 802.16 networks. Although lots of studies have been devoted to the detection of DDoS attack, some of them focus just on some statistical features of the traffic and some other focus on packets’ headers. The proposed approach exploits both qualitative and quantitative methods. It detects the attack by feeding some features of the network traffic under attack to an appropriate ANN structure. To evaluate the method, first a typical attacked network is implemented in OPNet simulator, and then by using the proposed system, the efficiency of the method is evaluated. The results show that by choosing suitable time series we can classify 93 % of normal traffic and 91 % of attack traffic.

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