The recognition of malware in network traffic is an important research problem. However, existing solutions addressing this problem rely heavily on the source code and misrecognise vulnerabilities (i.e. incur a high f...
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The recognition of malware in network traffic is an important research problem. However, existing solutions addressing this problem rely heavily on the source code and misrecognise vulnerabilities (i.e. incur a high false positive rate (FPR)) in some cases. In this paper, we initially use the K-means clustering algorithm to extract malware patterns under user to root attacks in network traffic. Since the traditional K-means algorithm needs to determine the number of clusters in advance and it is easily affected by the initial cluster centres, we propose an improved K-means clustering algorithm (nikclustering algorithm) for cluster analysis. Furthermore, we propose the use of self-similarity and our improved clustering algorithm to recognise buffer overflow vulnerabilities for malware in network traffic. This motivates us to design and implement a recognition approach for buffer overflow vulnerabilities based on self-similarity and our improved clustering algorithm, called Reliable Self-Similarity with Improved K-means Clustering (RSS-IKClustering). Extensive experiments conducted on two different datasets demonstrate that the RSS-IKClustering can achieve much fewer false positives than other notable approaches while increasing accuracy. We further apply our RSS-IKClustering approach on a public dataset (Center for Applied Internet Data Analysis), which also exhibited a high accuracy and low FPR of 96% and 1.5%, respectively.
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