In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Using experiments on the sendmail system call data and the network tcpdump data, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes algorithm. These algorithms can be used to compute the intra- and inter- audit record patterns, which are essential in describing program or user behavior. The discovered patterns can guide the audit data gathering process and facilitate feature selection. To meet the challenges of both efficient learning (mining) and real-time detection, we propose an agent-based architecture for intrusion detection systems where the learning agents continuously compute and provide the updated (detection) models to the detection agents.

A method is introducted for detecting intrusions at the level of privileged processes. Evidence is given that short sequences of system calls executed by running processes are a good discriminator between normal and abnormal operating characteristics of several common UNIX programs. Normal behavior is collected in two ways: Synthetically, by exercising as many normal modes of usage of a program as possible, and in a live user environment by tracing the actual execution of the program. In the former case several types of intrusive behavior were studied; in the latter case, results were analyzed for false positives. 1 Introduction Modern computer systems are plagued by security vulnerabilities. Whether it is the latest UNIX buffer overflow or bug in Microsoft Internet Explorer, our applications and operating systems are full of security flaws on many levels. From the viewpoint of the traditional security paradigm, it should be possible to eliminate such problems through more exten...

As the recent denial-of-service attacks on several major Internet sites have shown us, no open computer network is immune from intrusions. The wireless ad-hoc network is particularly vulnerable due to its features of open medium, dynamic changing topology, cooperative algorithms, lack of centralized monitoring and management point, and lack of a clear line of defense. Many of the intrusion detection techniques developed on a xed wired network are not applicable in this new environment. How to do it dierently and effectively is a challenging research problem. In this paper, we rst examine the vulnerabilities of a wireless ad-hoc network, the reason why we need intrusion detection, and the reason why the current methods cannot be applied directly. We then describe the new intrusion detection and response mechanisms that we are developing for wireless ad-hoc networks. 1. INTRODUCTION A wireless ad-hoc network consists of a collection of \peer" mobile nodes that are capable of communic...

The Intrusion Detection System architectures commonly used in commercial and research systems have a number of problems that limit their congurability, scalability or efficiency. The most common shortcoming in the existing architectures is that they are built around a single monolithic entity that does most of the data collection and processing. In this paper, we review our architecture for a distributed Intrusion Detection System based on multiple independent entities working collectively. We call these entities Autonomous Agents. This approach solves some of the problems previously mentioned. We present the motivation and description of the approach, partial results obtained from an early prototype, a discussion of design and implementation issues, and directions for future work.

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this paper, we examine the vulnerabilities of wireless networks and argue that we must include intrusion detection in the security architecture for mobile computing environment. We have developed such an architecture and evaluated a key mechanism in this architecture, anomaly detection for mobile ad-hoc network, through simulation experiments

This report presents a prototype architecture of a defense mechanism for computer systems. The intrusion detection problem is introduced and some of the key aspects of any solution are explained. Standard intrusion detection systems are built as a single monolithic module. A finer-grained approach is proposed, where small, independent agents monitor the system. These agents are taught how to recognise intrusive behaviour. The learning mechanism in the agents is built using Genetic Programming. This is explained, and some sample agents are described. The exibility, scalability and resilience of the agent approach are discussed. Future issues are also outlined.

In this paper we describe a data mining framework for constructing intrusion detection models. The first key idea is to mine system audit data for consistent and useful patterns of program and user behavior. The other is to use the set of relevant system features presented in the patterns to compute inductively learned classifiers that can recognize anomalies and known intrusions. In order for the classifiers to be effective intrusion detection models, we need to have sufficient audit data for training and also select a set of predictive system features. We propose to use the association rules and frequent episodes computed from audit data as the basis for guiding the audit data gathering and feature selection processes. We modify these two basic algorithms to use axis attribute(s) and reference attribute(s) as forms of item constraints to compute only the relevant patterns. In addition, we use an iterative level-wise approximate mining procedure to uncover the low frequency but important patterns. We use meta-learning as a mechanism to make intrusion detection models more effective and adaptive. We report our extensive experiments in using our framework on real-world audit data.

This report presents a prototype architecture for an active defense mechanism for computer systems. The intrusion detection problem is introduced and some of the key aspects of any solution are explained. Previous attempts to use similar techniques are discussed, and their shortcomings are explained. A new architecture is proposed which uses Genetic Programming to evolve programs to detect anomalous behaviour in a system. This architecture is developed and evaluated. A sample genetic program is used to discuss some of the design aspects of the agents. Cooperative monitoring of NFS requests shows how the approach can be generalised. The discussion details some issues to be addressed and future research directions.

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