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

Evaluation of Machine Learning Algorithms for Intrusion Detection System

Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate

Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

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

An Efficient Intrusion Detection Approach Utilizing Various WEKA Classifiers

Detection of Intrusion is an essential expertise business segment as well as a dynamic area of study and expansion caused by its requirement. Modern day intrusion detection systems still have these limitations of time sensitivity. The main requirement is to develop a system which is able of handling large volume of network data to detect attacks more accurately and proactively. Research conducted by on the KDDCUP99 dataset resulted in a various set of attributes for each of the four major attack types. Without reducing the number of features, detecting attack patterns within the data is more difficult for rule generation, forecasting, or classification. The goal of this research is to present a new method that Compare results of appropriately categorized and inaccurately categorized as proportions and the features chosen. In this research paper we explained our approach “An Efficient Intrusion Detection Approach Utilizing Various WEKA Classifiers” which is proposed to enhance the competence of recognition of intrusion employing different WEKA classifiers on processed KDDCUP99 dataset. During the experiment we employed Adaboost, J48, JRip, NaiveBayes and Random Tree classifiers to categorize the different attacks from the processed KDDCUP99. Keywords: Classifier, Data Mining, IDS, Network Security, Attacks, Cyber Securit

Review on Intrusion Detection System Based on The Goal of The Detection System

An extensive review of the intrusion detection system (IDS) is presented in this paper. Previous studies review the IDS based on the approaches (algorithms) used or based on the types of the intrusion itself. The presented paper reviews the IDS based on the goal of the IDS (accuracy and time), which become the main objective of this paper. Firstly, the IDS were classified into two types based on the goal they intend to achieve. These two types of IDS were later reviewed in detail, followed by a comparison of some of the studies that have earlier been carried out on IDS. The comparison is done based on the results shown in the studies compared. The comparison shows that the studies focusing on the detection time reduce the accuracy of the detection compared to other studies