Bayesian Classifiers in Intrusion Detection Systems

To be able to identify computer attacks, detection systems that are based on faults are not dependent on data base upgrades unlike the ones based on misuse. The first type of systems mentioned generate a knowledge pattern from which the usual and unusual traffic is distinguished. Within computer networks, different classification traffic techniques have been implemented in intruder detection systems based on abnormalities. These try to improve the measurement that assess the performance quality of classifiers and reduce computational cost. In this research work, a comparative analysis of the obtained results is carried out after implementing different selection techniques such as Info.Gain, Gain ratio and Relief as well as Bayesian (Naïve Bayes and Bayesians Networks). Hence, 97.6% of right answers were got with 13 features. Likewise, through the implementation of both load balanced methods and attributes normalization and choice, it was also possible to diminish the number of features used in the ID classification process. Also, a reduced computational expense was achieved

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

Intrusion detection using probabilistic graphical models

Modern computer systems are plagued by security vulnerabilities and flaws on many levels. Those vulnerabilities and flaws are discovered and exploited by attackers for their various intrusion purposes, such as eavesdropping, data modification, identity spoofing, password based attack, and denial of service attack, etc. The security of our computer systems and data is always at risk because of the open society of the internet. Due to the rapid growth of the internet applications, intrusion detection and prevention have become increasingly important research topics, in order to protect networking systems, such as the Web servers, database servers, cloud servers and so on, from threats. In this thesis, we attempt to build more efficient Intrusion Detection System through three different approaches, from different perspectives and based on different situations. Firstly, we propose Bayesian Model Averaging of Bayesian Network (BNMA) Classifiers for intrusion detection. In this work, we compare our BNMA classifier with Bayesian Network classifier and Naive Bayes classifier, which were shown be good models for detecting intrusion with reasonable accuracy and efficiency in the literature. From the experiment results, we see that BNMA can be more efficient and reliable than its competitors, i.e., the Bayesian network classifier and Naive Bayesian Network classifier, for all different sizes of training dataset. The advantage of BNMA is more pronounced when the training dataset size is small. Secondly, we introduce the Situational Data Model as a method for collecting dataset to train intrusion detection models. Unlike previously discussed static features as in the KDD CUP 99 data, which were collected without time stamps, Situational Data are collected in chronological sequence. Therefore, they can capture not only the dependency relationships among different features, but also relationships of values collected over time for the same features. The experiment results show that the intrusion detection model trained by Situational Dataset outperforms that trained by action-only sequences. Thirdly, we introduce the Situation Aware with Conditional Random Fields Intrusion Detection System (SA-CRF-IDS). The SA-CRF-IDS is trained by probabilistic graphical model Conditional Random Fields (CRF) over the Situational Dataset. The experiment results show that the CRF outperforms HMM with significantly better detection accuracy, and better ROC curve when we run the experiment on the non-Situational dataset. On the other hand, the two training methods have very similar performance when the Situational Dataset is adopted

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure