Evaluation of Intelligent Intrusion Detection Models

This paper discusses an evaluation methodology that can be used to assess the performance of intelligent techniques at detecting, as well as predicting, unauthorised activities in networks. The effectiveness and the performance of any developed intrusion detection model will be determined by means of evaluation and validation. The evaluation and the learning prediction performance for this task will be discussed, together with a description of validation procedures. The performance of developed detection models that incorporate intelligent elements can be evaluated using well known standard methods, such as matrix confusion, ROC curves and Lift charts. In this paper these methods, as well as other useful evaluation approaches, are discussed.Peer reviewe

Mining Audit Data to Build Intrusion Detection Models

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

Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms

In this paper we present the design and evaluation of intrusion detection models for MANETs using supervised classification algorithms. Specifically, we evaluate the performance of the MultiLayer Perceptron (MLP), the Linear classifier, the Gaussian Mixture Model (GMM), the Naive Bayes classifier and the Support Vector Machine (SVM). The performance of the classification algorithms is evaluated under different traffic conditions and mobility patterns for the Black Hole, Forging, Packet Dropping, and Flooding attacks. The results indicate that Support Vector Machines exhibit high accuracy for almost all simulated attacks and that Packet Dropping is the hardest attack to detect.Comment: 12 pages, 7 figures, presented at MedHocNet 200

Bayesian Learning Networks Approach to Cybercrime Detection

The growing dependence of modern society on telecommunication and information networks has become inevitable. The increase in the number of interconnected networks to the Internet has led to an increase in security threats and cybercrimes such as Distributed Denial of Service (DDoS) attacks. Any Internet based attack typically is prefaced by a reconnaissance probe process, which might take just a few minutes, hours, days, or even months before the attack takes place. In order to detect distributed network attacks as early as possible, an under research and development probabilistic approach, which is known by Bayesian networks has been proposed. This paper shows how probabilistically Bayesian network detects communication network attacks, allowing for generalization of Network Intrusion Detection Systems (NIDSs). Learning Agents which deploy Bayesian network approach are considered to be a promising and useful tool in determining suspicious early events of Internet threats and consequently relating them to the following occurring activities.Peer reviewe

A Review of Intrusion Detection Technology Based on Deep Rein-forcement Learning

With the rapid development of modern science and technology, all kinds of network attacks are updated constantly. Therefore, the traditional network security defense mechanism needs to be further improved. Through extensive investigation, this paper presents the latest work of network intrusion detection technology based on deep learning. Firstly, this paper introduces the related concepts of network intrusion detection technology. On this basis, we further evaluate the performance of three common deep learning models in intrusion detection, and conclude that DBN algorithm has some strong advantages. Afterwards, it also puts forward several improvement strategies of intrusion detection models

Leveraging Machine Learning for Network Intrusion Detection in Social Internet Of Things (SIoT) Systems

This research investigates the application of machine learning models for network intrusion detection in the context of Social Internet of Things (SIoT) systems. We evaluate Convolutional Neural Network with Generative Adversarial Network (CNN+GAN), Generative Adversarial Network (GAN), and Logistic Regression models using the CIC IoT Dataset 2023. CNN+GAN emerges as a promising approach, exhibiting superior performance in accurately identifying diverse intrusion types. Our study emphasizes the significance of advanced machine learning techniques in enhancing SIoT security by effectively detecting anomalous behaviours within socially interconnected environments. The findings provide practical insights for selecting suitable intrusion detection methods and highlight the need for ongoing research to address evolving intrusion scenarios and vulnerabilities in SIoT ecosystems

ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special track at WSTST 2005, Muroran, JAPA

Comparative Analysis of Selected Filtered Feature Rankers Evaluators for Cyber Attacks Detection

An increase in global connectivity and rapid expansion of computer usage and computer networks has made the security of the computer system an important issue with the industries and cyber communities being faced with new kinds of attacks daily The high complexity of cyberattacks poses a great challenge to the protection of cyberinfrastructures Confidentiality Integrity and availability of sensitive information stored on it Intrusion detection systems monitors network traffic for suspicious Intrusive activity and issues alert when such activity is detected Building Intrusion detection system that is computationally efficient and effective requires the use of relevant features of the network traffics packets identified by feature selection algorithms This paper implemented K-Nearest Neighbor and Na ve Bayes Intrusion detection models using relevant features of the UNSW-NB15 Intrusion detection dataset selected by Gain Ratio Information Gain Relief F and Correlation rankers feature selection technique