Intrusion Detection System using Bayesian Network Modeling

Computer Network Security has become a critical and important issue due to ever increasing cyber-crimes. Cybercrimes are spanning from simple piracy crimes to information theft in international terrorism. Defence security agencies and other militarily related organizations are highly concerned about the confidentiality and access control of the stored data. Therefore, it is really important to investigate on Intrusion Detection System (IDS) to detect and prevent cybercrimes to protect these systems. This research proposes a novel distributed IDS to detect and prevent attacks such as denial service, probes, user to root and remote to user attacks. In this work, we propose an IDS based on Bayesian network classification modelling technique. Bayesian networks are popular for adaptive learning, modelling diversity network traffic data for meaningful classification details. The proposed model has an anomaly based IDS with an adaptive learning process. Therefore, Bayesian networks have been applied to build a robust and accurate IDS. The proposed IDS has been evaluated against the KDD DAPRA dataset which was designed for network IDS evaluation. The research methodology consists of four different Bayesian networks as classification models, where each of these classifier models are interconnected and communicated to predict on incoming network traffic data. Each designed Bayesian network model is capable of detecting a major category of attack such as denial of service (DoS). However, all four Bayesian networks work together to pass the information of the classification model to calibrate the IDS system. The proposed IDS shows the ability of detecting novel attacks by continuing learning with different datasets. The testing dataset constructed by sampling the original KDD dataset to contain balance number of attacks and normal connections. The experiments show that the proposed system is effective in detecting attacks in the test dataset and is highly accurate in detecting all major attacks recorded in DARPA dataset. The proposed IDS consists with a promising approach for anomaly based intrusion detection in distributed systems. Furthermore, the practical implementation of the proposed IDS system can be utilized to train and detect attacks in live network traffi

Bayesian Networks for Interpretable Cyberattack Detection

The challenge of cyberattack detection can be illustrated by the complexity of the MITRE ATT&CKTM matrix, which catalogues >200 attack techniques (most with multiple sub-techniques). To reliably detect cyberattacks, we propose an evidence-based approach which fuses multiple cyber events over varying time periods to help differentiate normal from malicious behavior. We use Bayesian Networks (BNs) – probabilistic graphical models consisting of a set of variables and their conditional dependencies – for fusion/classification due to their interpretable nature, ability to tolerate sparse or imbalanced data, and resistance to overfitting. Our technique utilizes a small collection of expert-informed cyber intrusion indicators to create a hybrid detection system that combines data-driven training with expert knowledge to form a host-based intrusion detection system (HIDS). We demonstrate a software pipeline for efficiently generating and evaluating various BN classifier architectures for specific datasets and discuss explainability benefits thereof

Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System

In this paper we discuss and analyze some of the intelligent classifiers which allows for automatic detection and classification of networks attacks for any intrusion detection system. We will proceed initially with their analysis using the WEKA software to work with the classifiers on a well-known IDS (Intrusion Detection Systems) dataset like NSL-KDD dataset. The NSL-KDD dataset of network attacks was created in a military network by MIT Lincoln Labs. Then we will discuss and experiment some of the hybrid AI (Artificial Intelligence) classifiers that can be used for IDS, and finally we developed a Java software with three most efficient classifiers and compared it with other options. The outputs would show the detection accuracy and efficiency of the single and combined classifiers used

Makine öğrenmesi teknikleriyle saldırı tespiti: Karşılaştırmalı analiz

İnternet, günlük hayatımızın vazgeçilmez bir parçasıdır. Artan web uygulamaları ve kullanıcı sayısı, veri güvenliği açısından bazı riskleri de beraberinde getirmiştir. Ağ güvenliği için önemli araçlardan biri olan saldırı tespit sistemleri, güvenli iç ağlara yapılan saldırıları ve beklenmeyen erişim taleplerini tespit etmede başarılı bir şekilde kullanılmaktadır. Günümüzde, pek çok araştırmacı, daha etkin saldırı tespit sistemi gerçekleştirilmesi amacıyla çalışma yapmaktadır. Bu amaçla literatürde farklı makine öğrenme teknikleri ile gerçekleştirilmiş pek çok saldırı tespit sistemi vardır. Yapılan bu çalışmada, saldırı tespit sistemlerinde sıklıkla kullanılan makine öğrenme teknikleri araştırılmış, kullandıkları sınıflandırıcılar, veri setleri ve elde edilen başarılar değerlendirilmiştir. Bu amaçla 2007-2013 yılları arasında SCI, SCI Expanded ve EBSCO indekslerince taranan ulusal ve uluslararası dergilerde yayınlanmış 65 makale incelenmiş, sonuçlar, karşılaştırılmalı bir şekilde sunulmuştur. Böylece, gelecekte yapılacak makine öğrenme teknikleri ile saldırı tespiti çalışmalarına bir bakış açısı kazandırılması amaçlanmıştır

An Agent-Based Intrusion Detection System for Local Area Networks

Since it is impossible to predict and identify all the vulnerabilities of a network beforehand, and penetration into a system by malicious intruders cannot always be prevented, intrusion detection systems (IDSs) are essential entities to ensure the security of a networked system. To be effective in carrying out their functions, the IDSs need to be accurate, adaptive, and extensible. Given these stringent requirements and the high level of vulnerabilities of the current days' networks, the design of an IDS has become a very challenging task. Although, an extensive research has been done on intrusion detection in a distributed environment, distributed IDSs suffer from a number of drawbacks e.g., high rates of false positives, low detection efficiency etc. In this paper, the design of a distributed IDS is proposed that consists of a group of autonomous and cooperating agents. In addition to its ability to detect attacks, the system is capable of identifying and isolating compromised nodes in the network thereby introducing fault-tolerance in its operations. The experiments conducted on the system have shown that it has a high detection efficiency and low false positives compared to some of the currently existing systems.Comment: 13 pages, 5 figures, 2 table

From feature selection to building of bayesian classifiers: A network intrusion detection perspective

Abstract: Problem statement: Implementing a single or multiple classifiers that involve a Bayesian Network (BN) is a rising research interest in network intrusion detection domain. Approach: However, little attention has been given to evaluate the performance of BN classifiers before they could be implemented in a real system. In this research, we proposed a novel approach to select important features by utilizing two selected feature selection algorithms utilizing filter approach. Results: The selected features were further validated by domain experts where extra features were added into the final proposed feature set. We then constructed three types of BN namely, Naive Bayes Classifiers (NBC), Learned BN and Expert-elicited BN by utilizing a standard network intrusion dataset. The performance of each classifier was recorded. We found that there was no difference in overall performance of the BNs and therefore, concluded that the BNs performed equivalently well in detecting network attacks. Conclusion/Recommendations: The results of the study indicated that the BN built using the proposed feature set has less features but the performance was comparable to BNs built using other feature sets generated by the two algorithms

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation