Abstract

Abstract Intrusions pose a serious security risk in a network environment. Although systems can be hardened against many types of intrusions, often intrusions are successful making systems for detecting these intrusions critical to the security of these system. New intrusion types, of which detection systems are unaware, are the most difficult to detect. Current signature based methods and learning algorithms which rely on labeled data to train, generally can not detect these new intrusions. In addition, labeled training data in order to train misuse and anomaly detection systems is typically very expensive. We present a new type of clustering-based intrusion detection algorithm, unsupervised anomaly detection, which trains on unlabeled data in order to detect new intrusions. In our system, no manually or otherwise classified data is necessary for training. Our method is able to detect many different types of intrusions, while maintaining a low false positive rate as verified over the KDD CUP 1999 dataset.. 1 Introduction A network intrusion attack can be any use of a network that compromises its stability or the security of information that is stored on computers connected to it. A very wide range of activity falls under this definition, including attempts to destabilize the network as a whole, gain unauthorized access to files or privileges, or simply mishandling and misuse of software. Added security measures can not stop all such attacks. The goal of intrusion detection is to build a system which would automatically scan network activity and detect such intrusion attacks. Once an attack is detected, the system administrator is informed and can take corrective action.

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