Data Mining Approaches for Intrusion Detection (original) (raw)

Data Mining Approaches for Intrusion Detection Data Mining Approaches for Intrusion Detection

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 Data Mining Framework for Building Intrusion Detection Models

1999

There is often the need to update an installed Intrusion Detection System (IDS) due to new attack methods or upgraded computing environments. Since many current IDSs are constructed by manual encoding of expert knowledge, changes to IDSs are expensive and slow. In this paper, we describe a data mining framework for adaptively building Intrusion Detection (ID) models. The central idea is to utilize auditing programs to extract an extensive set of features that describe each network connection or host session, and apply data mining programs to learn rules that accurately capture the behavior of intrusions and normal activities. These rules can then be used for misuse detection and anomaly detection. New detection models are incorporated into an existing IDS through a meta-learning (or co-operative learning) process, which produces a meta detection model that combines evidence from multiple models. We discuss the strengths of our data mining programs, namely, classification, meta-learning, association rules, and frequent episodes. We report our results of applying these programs to the extensively gathered network audit data for the 1998 DARPA Intrusion Detection Evaluation Program.

Mining Audit Data to Build Intrusion Detection Models

1998

In this paper we discuss a data mining framework for constructing intrusion detection models. The key ideas are to mine system audit data for consistent and useful patterns of program and user behavior, and use the set of relevant system features presented in the patterns to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Our past experiments showed that classifiers can be used to detect intrusions, provided that sufficient audit data is available for training and the right set of system features are selected. 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) as a form of item constraints to compute only the relevant ("useful") patterns, and an iterative level-wise approximate mining procedure to uncover the low frequency (but important) patterns. We report our experiments in using these algorithms on real-world audit data.

Adaptive Intrusion Detection: A Data Mining Approach

Artificial Intelligence Review, 2000

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.

ADAM: a testbed for exploring the use of data mining in intrusion detection

2001

Abstract Intrusion detection systems have traditionally been based on the characterization of an attack and the tracking of the activity on the system to see if it matches that characterization. Recently, new intrusion detection systems based on data mining are making their appearance in the field. This paper describes the design and experiences with the ADAM (Audit Data Analysis and Mining) system, which we use as a testbed to study how useful data mining techniques can be in intrusion detection.

Mining Association Rules to Evade Network Intrusion in Network Audit Data

2014

With the growth of hacking and exploiting tools and invention of new ways of intrusion, intrusion detection and prevention is becoming the major challenge in the world of network security. The increasing network traffic and data on Internet is making this task more demanding. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. The false positive rates make it extremely hard to analyse and react to attacks. Intrusion detection systems using data mining approaches make it possible to search patterns and rules in large amount of audit data. In this paper, we represent a model to integrate association rules to intrusion detection to design and implement a network intrusion detection system. Our technique is used to generate attack rules that will detect the attacks in network audit data using anomaly detection. This shows that the modified association rules algorithm is capable of detecting network ...

Data Mining Approaches For Network Intrusion Detection System

Data mining has been gaining popularity in knowledge discovery field, particularity with the increasing availability of digital documents in various languages from all around the world. Network intrusion detection is the process of monitoring the events occurring in a computing system or network and analyzing them for signs of intrusions. In this paper, intrusion detection & several areas of intrusion detection in which data mining technology applied are discussed. Data mining techniques are used to discover consistent and useful patterns of system features that describe program and user behavior. Data mining can improve variant detection rate, control false alarm rate and reduce false dismissals. By using these set of relevant system features to compute classifiers that recognize anomalies & known intrusion.

Characterizing Intelligent Intrusion Detection and Prevention Systems Using Data Mining

Advances in Secure Computing, Internet Services, and Applications

Intrusion Detection and Prevention Systems (IDPS) are being widely implemented to prevent suspicious threats in computer networks. Intrusion detection and prevention systems are security systems that are used to detect and prevent security threats to computer networks. In order to understand the security risks and IDPS, in this chapter, the authors make a quick review on classification of the IDPSs and categorize them in certain groups. Further, in order to improve accuracy and security, data mining techniques have been used to analyze audit data and extract features that can distinguish normal activities from intrusions. Experiments have been conducted for building efficient intrusion detection and prevention systems by combining online detection and offline data mining. During online data examination, real-time data are captured and are passed through a detection engine that uses a set of rules and parameters for analysis. During offline data mining, necessary knowledge is extracted about the process of intrusion.

Data Mining for Intrusion Detection

Encyclopedia of Data Warehousing and Mining

Today computers control power, oil and gas delivery, communication systems, transportation networks, banking and financial services, and various other infrastructure services critical to the functioning of our society. However, as the cost of the information processing and Internet accessibility falls, more and more organizations are becoming vulnerable to a wide variety of cyber threats. According to a recent survey by CERT/CC (Computer Emergency Response Team/Coordination Center), the rate of cyber attacks has been more than doubling every year in recent times (Figure 1). In addition, the severity and sophistication of the attacks are also growing. For example, Slammer/Sapphire Worm was the fastest computer worm in history. As it began spreading throughout the Internet, it doubled in size every 8.5 seconds and infected at least 75,000 hosts causing network outages and unforeseen consequences such as canceled airline flights, interference with elections, and ATM failures (Moore, 20...

The Use of Data Mining in the Implementation of a Network Intrusion Detection System

This paper focuses on the domain of Network Intrusion Detection Systems, an area where the goal is to detect security violations by passively monitoring network traffic and raising an alarm when an attack occurs. But the problem is that new attacks are being deployed all the time. This particular system has been developed using a range of data mining techniques so as to automatically be able to classify network tracffic as normal or intrusive. Here we evaluate decision trees and their performance based on a large data set used in the 1999 KDD cup contest.