Mitigating DDoS using weight-based geographical clustering
Journal article, Peer reviewed
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Original versionKongshavn, Haugerud H, Yazidi A, Maseng T, Hammer HL. Mitigating DDoS using weight-based geographical clustering. Concurrency and Computation. 2020;32(11) https://doi.org/10.1002/cpe.5679
Distributed denial of service (DDoS) attacks have for the last two decades been among the greatest threats facing the internet infrastructure. Mitigating DDoS attacks is a particularly challenging task as an attacker tries to conceal a huge amount of traffic inside a legitimate traffic flow. This article proposes to use data mining approaches to find unique hidden data structures which are able to characterize the normal traffic flow. This will serve as a mean for filtering illegitimate traffic under DDoS attacks. In this endeavor, we devise three algorithms built on previously uncharted areas within mitigation techniques where clustering techniques are used to create geographical clusters in regions which are likely to contain legitimate traffic. We argue through extensive experimental results that establishing clusters around this narrative is a superior solution to clustering algorithms which rely on bitwise distances between IP addresses. In addition, the DDoS filtering algorithm is deployed in a virtual Linux environment using Nfqueue and tested in a simulated real‐life DDoS attack.