Faculty Publications
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Item Modelling Behavioural Dynamics for Asymmetric Application Layer DDoS Detection(Institute of Electrical and Electronics Engineers Inc., 2021) Praseed, A.; Santhi Thilagam, P.S.Asymmetric application layer DDoS attacks using computationally intensive HTTP requests are an extremely dangerous class of attacks capable of taking down web servers with relatively few attacking connections. These attacks consume limited network bandwidth and are similar to legitimate traffic, which makes their detection difficult. Existing detection mechanisms for these attacks use indirect representations of actual user behaviour and complex modelling techniques, which leads to a higher false positive rate (FPR) and longer detection time, which makes them unsuitable for real time use. There is a need for simple, efficient and adaptable detection mechanisms for asymmetric DDoS attacks. In this work, an attempt is made to model the actual behavioural dynamics of legitimate users using a simple annotated Probabilistic Timed Automata (PTA) along with a suspicion scoring mechanism for differentiating between legitimate and malicious users. This allows the detection mechanism to be extremely fast and have a low FPR. In addition, the model can incrementally learn from run-time traces, which makes it adaptable and reduces the FPR further. Experiments on public datasets reveal that our proposed approach has a high detection rate and low FPR and adds negligible overhead to the web server, which makes it ideal for real time use. © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.Item HTTP request pattern based signatures for early application layer DDoS detection: A firewall agnostic approach(Elsevier Ltd, 2022) Praseed, A.; Santhi Thilagam, P.S.Application Layer DDoS (AL-DDoS) attacks are an extremely dangerous variety of DDoS attacks that started becoming popular recently. They are executed using very few legitimate requests, making them very difficult to detect. Since they are executed using attack generation tools and botnets, AL-DDoS attacks display similarity within a request stream (temporal similarity) and across request streams (spatial similarity). Once a particular request stream has been detected as malicious by an anomaly detection mechanism (ADM), spatial similarity can help in detecting AL-DDoS attacks much earlier by employing a dynamic signature based approach. In this work, we use HTTP request patterns as signatures to build a firewall agnostic Early Detection Module (EDM) for AL-DDoS attacks. We also propose the use of Sample Entropy instead of the popular Shannon's Entropy to identify AL-DDoS attacks. Sample Entropy is able to model both the frequencies and sequence of data items within a request stream, and is a better indicator of temporal similarity than Shannon's Entropy. In this work, we demonstrate that Sample Entropy can be used effectively to detect AL-DDoS attacks. With a Sample Entropy based anomaly detection mechanism, we demonstrate that the use of EDM significantly reduces the detection latency for AL-DDoS attacks. © 2022 Elsevier Ltd