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This paper proposes BotDigger, a fuzzy logic-based botnet detection system. In this system, we derive a set of logical rules based on a well known botnet characteristics. Utilizing these rules, an adaptive logic algorithm will be applied on network traffic traces searching for botnet footprints and associating a trust level for each host present in the sampled data. Future work will focus on evaluating...
Network defense is an elusive art. The arsenal to defend our devices from attack is constantly lagging behind the latest methods used by attackers to break into them and subsequently into our networks. To counteract this trend, we developed a distributed, scalable approach that harnesses the power of collaborative end-host detectors or sensors. Simulation results reveal order of magnitude improvements...
The increasing number of applications offering their services over peer-to-peer (P2P) platforms is changing the properties of the traffic within computer networks. Their massive use raises a few imperative challenges for network administrators and Internet service providers, regarding the quality of service and security of their networks. It such scenario, it is important to develop mechanisms to...
The emergence of Distributed Denial of Service (DDoS) attack increases the destructive force of Denial of Service (DoS) attack drastically. Besides bringing more terrible threats, the attack from far and near and the employment of internet protocol (IP) spoofing make the abnormal traffic detection harder and harder. This paper proposes a mechanism defined as AMHI (Address Matching and Hash Inspection)...
Traffic classification and identification is a fertile research area. Beyond Quality of Service, service differentiation, and billing, one of the most important applications of traffic classification is in the field of network security. This paper proposes a packet-level traffic classification approach based on Hidden Markov Model (HMM). Classification is performed by using real network traffic and...
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