Detection of denial-of-service attacks based on computer vision techniques
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A Denial-of-Service (DoS) attack is an intrusive attempt, which aims to force a designated resource (e.g., network bandwidth, processor time or memory) to be unavailable to its intended users. This attack is launched either by deliberately exploiting system vulnerabilities of a victim (e.g., a host, a router, or an entire network) or by flooding a victim with large volume of useless network traffic. Since 1990s, DoS attacks have emerged as a type of the most severe network intrusive behaviours and have posed serious threats to the infrastructures of computer networks and various network-based services. This thesis aims to provide an intelligent and effective solution for DoS attack detection. Unlike the related works based on machine learning and statistical analysis, this thesis suggests to treat network traffic records as images and to redefine the DoS attack detection problem as a computer vision task. To achieve the aforementioned objectives, this thesis first conducts a detailed literature review on the state of the art in DoS attack detection. Then, it analyses and chooses the most appropriate mechanisms for DoS attack detection. Afterwards, it designs a general system framework for DoS attack detection with respect to the chosen mechanisms. Furthermore, two Multivariate Correlation Analysis (MCA) approaches are proposed based on two techniques, namely Euclidean distance and triangle area. These two proposed MCA approaches provide accurate description for network traffic records and facilitate conversion of network traffic into the respective images. In addition, this thesis proposes a DoS attack detection system, in which the images of network traffic are served as the observed objects and the task of DoS attack detection is reformulated as a computer vision problem, namely image retrieval. This proposed DoS attack detection system applies a widely used dissimilarity measure, namely the Earth Mover’s Distance (EMD), to object classification. The EMD takes cross-bin matching into account and provides a more accurate evaluation on the dissimilarity between distributions than some other well-known dissimilarity measures, such as Minkowski-form distance Lp and X² statistics. The merits of the EMD facilitate the capability of our proposed system with effective detection. Last but not least, our intelligent and effective solutions, including the two proposed MCA approaches and the EMD-based DoS attack detection system, are evaluated using the KDD Cup 99 dataset. The evaluation results illustrate that our proposed MCA approaches provide accurate characterisation for network traffic, and the proposed detection system can detect unknown DoS attacks and outperforms two state-of-the-art approaches.
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