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"Intrusion detection systems"
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Network anomaly detection : a machine learning perspective
\"This book discusses detection of anomalies in computer networks from a machine learning perspective. It introduces readers to how computer networks work and how they can be attacked by intruders in search of fame, fortune, or challenge. The reader will learn how one can look for patterns in captured network traffic data to look for anomalous patterns that may correspond to attempts at unauthorized intrusion. The reader will be given a technical and sophisticated description of such algorithms and their applications in the context of intrusion detection in networks\"-- Provided by publisher.
Book
Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks
Intrusion in wireless sensor networks (WSNs) aims to degrade or even eliminating the capability of these networks to provide its functions. In this paper, an enhanced intrusion detection system (IDS) is proposed by using the modified binary grey wolf optimizer with support vector machine (GWOSVM-IDS). The GWOSVM-IDS used 3 wolves, 5 wolves and 7 wolves to find the best number of wolves. The proposed method aims to increase intrusion detection accuracy and detection rate and reduce processing time in the WSN environment through decrease false alarms rates, and the number of features resulted from the IDSs in the WSN environment. Indeed, the NSL KDD’99 dataset is used to demonstrate the performance of the proposed method and compare it with other existing methods. The proposed methods are evaluated in terms of accuracy, the number of features, execution time, false alarm rate, and detection rate. The results showed that the proposed GWOSVM-IDS with seven wolves overwhelms the other proposed and comparative algorithms.
Journal Article
Intrusion detection system for automotive Controller Area Network (CAN) bus system: a review
The modern vehicles nowadays are managed by networked controllers. Most of the networks were designed with little concern about security which has recently motivated researchers to demonstrate various kinds of attacks against the system. In this paper, we discussed the vulnerabilities of the Controller Area Network (CAN) within in-vehicle communication protocol along with some potential attacks that could be exploited against it. Besides, we present some of the security solutions proposed in the current state of research in order to overcome the attacks. However, the main goal of this paper is to highlight a holistic approach known as intrusion detection system (IDS) which has been a significant tool in securing networks and information systems over the past decades. To the best of our knowledge, there is no recorded literature on a comprehensive overview of IDS implementation specifically in the CAN bus network system. Thus, we proposed an in-depth investigation of IDS found in the literature based on the following aspects: detection approaches, deployment strategies, attacking techniques, and finally technical challenges. In addition, we also categorized the anomaly-based IDS according to these methods, e.g., frequency-based, machine learning-based, statistical-based, and hybrid-based as part of our contributions. Correspondingly, this study will help to accelerate other researchers to pursue IDS research in the CAN bus system.
Journal Article
A deep learning approach for effective intrusion detection in wireless networks using CNN
Security is playing a major role in this Internet world due to the rapid growth of Internet users. The various intrusion detection systems were developed by many researchers in the past to identify and detect the intruders using data mining techniques. However, the existing systems are not able to achieve sufficient detection accuracy when using the data mining. For this purpose, we propose a new intrusion detection system to provide security in data communication by identifying and detecting the intruders effectively in wireless networks. Here, we propose a new feature selection algorithm called conditional random field and linear correlation coefficient-based feature selection algorithm to select the most contributed features and classify them using the existing convolutional neural network. The experiments have been conducted for evaluating the proposed intrusion detection system that achieves 98.88% as overall detection accuracy. The tenfold cross-validation has been done for evaluating the performance of the proposed model.
Journal Article
An integrated rule based intrusion detection system: analysis on UNSW-NB15 data set and the real time online dataset
Intrusion detection system (IDS) has been developed to protect the resources in the network from different types of threats. Existing IDS methods can be classified as either anomaly based or misuse (signature) based or sometimes combination of both. This paper proposes a novel misuse based intrusion detection system to detect five categories such as: Exploit, DOS, Probe, Generic and Normal in a network. Further, most of the related works on IDS are based on KDD99 or NSL-KDD 99 data set. These data sets are considered obsolete to detect recent types of attacks and have no significance. In this paper UNSW-NB15 data set is considered as the offline dataset to design own integrated classification based model for detecting malicious activities in the network. Performance of the proposed integrated classification based model is considerably high compared to other existing decision tree based models to detect these five categories. Moreover, this paper generates its own real time data set at NIT Patna CSE lab (RTNITP18) which acts as the working example of proposed intrusion detection model. This RTNITP18 dataset is considered as a test data set to evaluate the performance of the proposed intrusion detection model. The performance analysis of the proposed model with UNSW-NB15 (benchmark data set) and real time data set (RTNITP18) shows higher accuracy, attack detection rate, mean F-measure, average accuracy, attack accuracy, and false alarm rate in comparison to other existing approaches. Proposed IDS model acts as the dog watcher to detect different types of threat in the network.
Journal Article
A survey of neural networks usage for intrusion detection systems
In recent years, advancements in the field of the artificial intelligence (AI) gained a huge momentum due to the worldwide appliance of this technology by the industry. One of the crucial areas of AI are neural networks (NN), which enable commercial utilization of functionalities previously not accessible by usage of computers. Intrusion detection system (IDS) presents one of the domains in which neural networks are widely tested for improving overall computer network security and data privacy. This article gives a thorough overview of recent literature regarding neural networks usage in intrusion detection system area, including surveys and new method proposals. Short tutorial descriptions of neural network architectures, intrusion detection system types and training datasets are also provided.
Journal Article
A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection
This paper describes a focused literature survey of self-organizing maps (SOM) in support of intrusion detection. Specifically, the SOM architecture can be divided into two categories, i.e., static-layered architectures and dynamic-layered architectures. The former one, Hierarchical Self-Organizing Maps (HSOM), can effectively reduce the computational overheads and efficiently represent the hierarchy of data. The latter one, Growing Hierarchical Self-Organizing Maps (GHSOM), is quite effective for online intrusion detection with low computing latency, dynamic self-adaptability, and self-learning. The ultimate goal of SOM architecture is to accurately represent the topological relationship of data to identify any anomalous attack. The overall goal of this survey is to comprehensively compare the primitive components and properties of SOM-based intrusion detection. By comparing with the two SOM-based intrusion detection systems, we can clearly understand the existing challenges of SOM-based intrusion detection systems and indicate the future research directions.
Journal Article
Network intrusion detection system using deep neural networks
In recent decades, rapid development in the world of technology and networks has been achieved, also there is a spread of Internet services in all fields over the world. Piracy numbers have increased, also a lot of modern systems were penetrated, so the developing information security technologies to detect the new attack become an important requirement. One of the most important information security technologies is an Intrusion Detection System (IDS) that uses machine learning and deep learning techniques to detect anomalies in the network. The main idea of this paper is to use an advanced intrusion detection system with high network performance to detect the unknown attack package, by using a deep neural network algorithm, also in this model, the attack detection is done by two ways (binary classification and multiclass classification). The proposed system has shown encouraging results in terms of the high accuracy (99.98% with multiclass classification and with binary classification).
Journal Article
Review of intrusion detection systems based on deep learning techniques: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions
This study reviews and analyses the research landscape for intrusion detection systems (IDSs) based on deep learning (DL) techniques into a coherent taxonomy and identifies the gap in this pivotal research area. The focus is on articles related to the keywords ‘deep learning’, ‘intrusion’ and ‘attack’ and their variations in four major databases, namely Web of Science, ScienceDirect, Scopus and the Institute of Electrical and Electronics Engineers’
Xplore
. These databases are sufficiently broad to cover the technical literature. The dataset comprises 68 articles. The largest proportion (72.06%; 49/68) relates to articles that develop an approach for evaluating or identifying intrusion detection techniques using the DL approach. The second largest proportion (22.06%; 15/68) relates to studying/applying articles to the DL area, IDSs or other related issues. The third largest proportion (5.88%; 4/68) discusses frameworks/models for running or adopting IDSs. The basic characteristics of this emerging field are identified from the aspects of motivations, open challenges that impede the technology’s utility, authors’ recommendations and substantial analysis. Then, a result analysis mapping for new directions is discussed. Three phases are designed to meet the demands of detecting distributed denial-of-service attacks with a high accuracy rate. This study provides an extensive resource background for researchers who are interested in IDSs based on DL.
Journal Article
FCM–SVM based intrusion detection system for cloud computing environment
Cloud computing offer various services over the Internet based on pay-per-use concept. Therefore, many organizations have already adopted this system to attract the users with its desirable features. However, due to its design, makes it vulnerable to malicious attacks. This demands an Intrusion Detection System that can detect such attacks with high detection accuracy in cloud environment. This paper proposes a novel intrusion detection system that combines a fuzzy c means clustering (FCM) algorithm with support vector machine (SVM) to improve the accuracy of the detection system in cloud computing environment. The proposed system is implemented and compared with existing mechanisms. The NSL-KDD dataset is used for experiments. Based on performance evaluation and comparative analysis, the results obtained using this new hybrid mechanism (FCM–SVM) show that the proposed system can detect the anomalies with high detection accuracy and low false alarm rates over the existing techniques.
Journal Article