Mr. Ilker Özçelik Profile

Ilker Özçelik

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Proceedings Article | 20 June 2014 Paper

Deceiving entropy-based DoS detection

İlker Özçelik, Richard Brooks

Proceedings Volume 9091, 90911P (2014) https://doi.org/10.1117/12.2054434

KEYWORDS: Internet, Sensors, Computer intrusion detection, Signal processing, Telecommunications, Signal attenuation, Detection and tracking algorithms, Sensor fusion, Target recognition, Current controlled current source

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Denial of Service (DoS) attacks disable network services for legitimate users. A McAfee report shows that eight out of ten Critical Infrastructure Providers (CIPs) surveyed had a significant Distributed DoS (DDoS) attack in 2010.¹ Researchers proposed many approaches for detecting these attacks in the past decade. Anomaly based DoS detection is the most common. In this approach, the detector uses statistical features; such as the entropy of incoming packet header fields like source IP addresses or protocol type. It calculates the observed statistical feature and triggers an alarm if an extreme deviation occurs. However, intrusion detection systems (IDS) using entropy based detection can be fooled by spoofing. An attacker can sniff the network to collect header field data of network packets coming from distributed nodes on the Internet and fuses them to calculate the entropy of normal background traffic. Then s/he can spoof attack packets to keep the entropy value in the expected range during the attack. In this study, we present a proof of concept entropy spoofing attack that deceives entropy based detection approaches. Our preliminary results show that spoofing attacks cause significant detection performance degradation.

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