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Network Anomaly Detection Using Co-clustering

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3 Author(s)
Evangelos E. Papalexakis ; Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA ; Alex Beutel ; Peter Steenkiste

Early Internet architecture design goals did not put security as a high priority. However, today Internet security is a quickly growing concern. The prevalence of Internet attacks has increased significantly, but still the challenge of detecting such attacks generally falls on the end hosts and service providers, requiring system administrators to detect and block attacks on their own. In particular, as social networks have become central hubs of information and communication, they are increasingly the target of attention and attacks. This creates a challenge of carefully distinguishing malicious connections from normal ones. Previous work has shown that for a variety of Internet attacks, there is a small subset of connection measurements that are good indicators of whether a connection is part of an attack or not. In this paper we look at the effectiveness of using two different co-clustering algorithms to both cluster connections as well as mark which connection measurements are strong indicators of what makes any given cluster anomalous relative to the total data set. We run experiments with these co-clustering algorithms on the KDD 1999 Cup data set. In our experiments we find that soft co-clustering, running on samples of data, finds consistent parameters that are strong indicators of anomalous detections and creates clusters, that are highly pure. When running hard co-clustering on the full data set (over 100 runs), we on average have one cluster with 92.44% attack connections and the other with 75.84% normal connections. These results are on par with the KDD 1999 Cup winning entry, showing that co-clustering is a strong, unsupervised method for separating normal connections from anomalous ones. Finally, we believe that the ideas presented in this work may inspire research for anomaly detection in social networks, such as identifying spammers and fraudsters.

Published in:

Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on

Date of Conference:

26-29 Aug. 2012