Artificial Neural Networks for Misuse Detection

Topics: Artificial intelligence, Artificial neural network, Expert system Pages: 20 (5484 words) Published: August 17, 2005

Misuse detection is the process of attempting to identify instances of network attacks by comparing current activity against the expected actions of an intruder. Most current approaches to misuse detection involve the use of rule-based expert systems to identify indications of known attacks. However, these techniques are less successful in identifying attacks which vary from expected patterns. Artificial neural networks provide the potential to identify and classify network activity based on limited, incomplete, and nonlinear data sources. We present an approach to the process of misuse detection that utilizes the analytical strengths of neural networks, and we provide the results from our preliminary analysis of this approach. Keywords: Intrusion detection, misuse detection, neural networks, computer security.

1. Introduction
Because of the increasing dependence which companies and government agencies have on their computer networks the importance of protecting these systems from attack is critical. A single intrusion of a computer network can result in the loss or unauthorized utilization or modification of large amounts of data and cause users to question the reliability of all of the information on the network. There are numerous methods of responding to a network intrusion, but they all require the accurate and timely identification of the attack.

This paper presents an analysis of the applicability of neural networks in the identification of instances of external attacks against a network. The results of tests conducted on a neural network, which was designed as a proof-of-concept, are also presented. Finally, the areas of future research that are being conducted in this area are discussed.

1.1 Intrusion Detection Systems

1.1.1 Background
The timely and accurate detection of computer and network system intrusions has always been an elusive goal for system administrators and information security researchers. The individual creativity of attackers, the wide range of computer hardware and operating systems, and the ever- changing nature of the overall threat to target systems have contributed to the difficulty in effectively identifying intrusions. While the complexities of host computers already made intrusion detection a difficult endeavor, the increasing prevalence of distributed network-based systems and insecure networks such as the Internet has greatly increased the need for intrusion detection [20].

There are two general categories of attacks which intrusion detection technologies attempt to identify - anomaly detection and misuse detection [1,13]. Anomaly detection identifies activities that vary from established patterns for users, or groups of users. Anomaly detection typically involves the creation of knowledge bases that contain the profiles of the monitored activities. The second general approach to intrusion detection is misuse detection. This technique involves the comparison of a user's activities with the known behaviors of attackers attempting to penetrate a system [17,18]. While anomaly detection typically utilizes threshold monitoring to indicate when a certain established metric has been reached, misuse detection techniques frequently utilize a rule-based approach. When applied to misuse detection, the rules become scenarios for network attacks. The intrusion detection mechanism identifies a potential attack if a user's activities are found to be consistent with the established rules. The use of comprehensive rules is critical in the application of expert systems for intrusion detection. 1.1.2 Current Approaches to Intrusion Detection

Most current approaches to the process of detecting intrusions utilize some form of rule-based analysis. Rule-Based analysis relies on sets of predefined rules that are provided by an administrator, automatically created by the system, or both. Expert systems are the most common form of rule-based intrusion detection approaches [8, 24]. The...

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