A Survey of Network Intrusion Detection Using Machine Learning Techniques (original) (raw)
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Application of Machine Learning Approaches in Intrusion Detection System: A Survey
Network security is one of the major concerns of the modern era. With the rapid development and massive usage of internet over the past decade, the vulnerabilities of network security have become an important issue. Intrusion detection system is used to identify unauthorized access and unusual attacks over the secured networks. Over the past years, many studies have been conducted on the intrusion detection system. However, in order to understand the current status of implementation of machine learning techniques for solving the intrusion detection problems this survey paper enlisted the 49 related studies in the time frame between 2009 and 2014 focusing on the architecture of the single, hybrid and ensemble classifier design. This survey paper also includes a statistical comparison of classifier algorithms, datasets being used and some other experimental setups as well as consideration of feature selection step.
Network Intrusion Classification Employing Machine Learning: A Survey
2019
In this modern era computer network security is a vital issue. Network security is developed by an efficient Intrusion Detection System (IDS). It is used to identify unauthorized access, malicious attacks and give an alert when monitors any kind of unusual activity. Over the past 30 years, there have been lots of work on intrusion detection system using machine learning algorithms. Basically, realizing the present status of application of machine learning algorithms for solving intrusion classification task, this review work gives a proper guideline. This survey work selected 84 papers based on highest citations number from the years of 2009-2018. This thesis work gives an overview of a different intrusion detection systems, a statistical comparison based on different classifier like single, hybrid and ensemble learning. In addition, we have discussed best machine learning classifiers, best datasets and some feature selections process in this thesis work.
Intrusion detection by machine learning: A review
The popularity of using Internet contains some risks of network attacks. Intrusion detection is one major research problem in network security, whose aim is to identify unusual access or attacks to secure internal networks. In literature, intrusion detection systems have been approached by various machine learning techniques. However, there is no a review paper to examine and understand the current status of using machine learning techniques to solve the intrusion detection problems. This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers. Related studies are compared by their classifier design, datasets used, and other experimental setups. Current achievements and limitations in developing intrusion detection systems by machine learning are present and discussed. A number of future research directions are also provided.
A Survey on Network-Based Intrusion Detection Systems Using Machine Learning Algorithms
International Journal of Engineering Applied Sciences and Technology, 2022
Network security is of central significance in the current information world. Due to the rapid increase of network-enabled devices, there is a significant risk of network intrusion more than ever. Hackers and intruders can successfully attack to cause the crash of the networks and web services by the unauthorized intrusion, which may cause a significant loss to an organization in terms of data and money. So, it is high time to create an intrusion detection system that can detect all types of intrusion. Due to the rapid growth and significant results of machine learning (ML) algorithms in several areas, there has recently been much interest in applying them to network security. The network-based intrusion detection system (NIDS) has much promise to be the borderline of defence against intrusions in the current information communication technology (ICT) era, and it's a critical aspect of network security. Due to the dynamic nature of attacks, intrusion detection datasets are avail...
Applied Sciences
The Intrusion Detection System (IDS) is an effective tool utilized in cybersecurity systems to detect and identify intrusion attacks. With the increasing volume of data generation, the possibility of various forms of intrusion attacks also increases. Feature selection is crucial and often necessary to enhance performance. The structure of the dataset can impact the efficiency of the machine learning model. Furthermore, data imbalance can pose a problem, but sampling approaches can help mitigate it. This research aims to explore machine learning (ML) approaches for IDS, specifically focusing on datasets, machine algorithms, and metrics. Three datasets were utilized in this study: KDD 99, UNSW-NB15, and CSE-CIC-IDS 2018. Various machine learning algorithms were chosen and examined to assess IDS performance. The primary objective was to provide a taxonomy for interconnected intrusion detection systems and supervised machine learning algorithms. The selection of datasets is crucial to e...
MAIDEn: A Machine Learning Approach for Intrusion Detection using Ensemble Technique
International Journal of Computer Applications
An Intrusion detection system is a machine or software that monitors the traffic in a network and on detection of a malicious packet, informs the user or a specific acting unit which can take further action and avoid the malicious packet from entering the network. This paper discusses a way to implement an intelligent IDS which classifies the normal traffic in a network with abnormal or attacked ones. This paper explains the method used to generate such a system and the various classifiers used in the generation process. The proposed system of Intrusion Detection, classifies data with three different classifiers and an Ensemble technique which selects the majority of the three classifiers to assign the packet in the network as anomaly or normal. The dataset used to train the classifiers is the NSL-KDD dataset. The IDS proposed serves many applications in the field of Military Systems, Banks and Social Networking websites where data is very sensitive. The paper also explains related work done in this field and briefly explains every classifier, the network attacks and the dataset.
Machine Learning Techniques for Network Intrusion Detection System (NIDS): A Survey
International Journal of Emerging Trends in Engineering Research, 2021
In computer network, security of the network is a major issue and intrusion is the most common threats to security. Cyber attacks detection is becoming more enlightened challenge in detecting these threats accurately. In network security, intrusion detection system (IDS) has played a vital role to detect intrusion. In recent years, numerous methods have been proposed for intrusion detection to detect these security threats. This survey paper study examines recent work in the topic of network security, machine learning based techniques as well as a discussion of the many datasets that are commonly used to evaluate IDS. It also explains how researchers employ Machine Learning Based Techniques to detect intrusions.
A Comparative Analysis of Machine Learning Approaches to Intrusion Detection
Journal of Xi'an University of Architecture & Technology, 2021
Network administrators use a Network Intrusion Detection System (NIDS) to detect network security breaches in their enterprises. However, designing a convenient and dynamic NIDS for unanticipated and unpredictable attacks poses numerous obstacles. Signature-based Intrusion Detection Systems (IDS) are currently insufficient to handle the hazards posed by zero-day attacks to networked systems. On the NSL-KDD dataset, we applied data mining techniques and compared their performance on metrics such as accuracy, precision, and recall.
A Machine Learning Approach for Intrusion Detection using Ensemble Technique-A Survey
An Intrusion detection system is a machine or software that monitors the traffic in a network and on detection of a malicious packet, informs the user or a specific acting unit which can take further action and avoid the malicious packet from entering the network. In network intrusion, there may be multiple computing nodes attacked by intruders. The evidences of intrusions have to gather from all such attacked nodes. An intruder may move between multiple nodes in the network to conceal the origin of attack, or misuse some compromised hosts to launch the attack on other nodes. To detect such intrusion activities spread over the whole network, we present a new intrusion detection system (IDS) that classifies data with three different classifiers and an Ensemble technique that selects the majority of the three classifiers to assign the packet in the network as anomaly or normal. In this paper, we discuss a different ways to implement intelligent IDS, which classifies the normal traffic...
Intrusion Detection Using Machine Learning: A Comparison Study
With the advancement of internet over years, the number of attacks over internet has also increased. A powerful Intrusion Detection System (IDS) is required to ensure the security of a network. The aim of IDS is to monitor the processes prevailing in a network and to analyze them for signs of any possible deviations. Some studies have been done in this field but a deep and exhaustive work has still not been done. This paper proposes an IDS using machine leaning for network with a good union of feature selection technique and classifier by studying the combinations of most of the popular feature selection techniques and classifiers. A set of significant features is selected from the original set of features using feature selection techniques and then the set of significant features is used to train different types of classifiers to make the IDS. Five folds cross validation is done on NSL-KDD dataset to find results. It is finally observed that K-NN classifier produces better performance than others and, among the feature selection methods, information gain ratio based feature selection method is better.