An overview of neural networks use in anomaly Intrusion Detection Systems

Sani, Yusuf; Mohamedou, Ahmed; Ali, Khalid; Farjamfar, Anahita; Azman, Mohamed; Shamsuddin, Solahuddin

doi:10.1109/scored.2009.5443289

Cited by 27 publications

(10 citation statements)

References 7 publications

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“…These data can be analyzed using an intelligence model that is used to improve the security of vehicles and improve road safety infrastructure. Yusuf Sani et al [26] presented an overview of a Neural Network in the use of an anomaly intrusion detection system. The paper gives an overall review of how a neural network works and the way it improves the efficiency of detecting anomalies compared to other systems.…”

Section: Literature Reviewmentioning

confidence: 99%

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Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

Basavaraj

Tayeb

2022

JSAN

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With the emergence of networked devices, from the Internet of Things (IoT) nodes and cellular phones to vehicles connected to the Internet, there has been an ever-growing expansion of attack surfaces in the Internet of Vehicles (IoV). In the past decade, there has been a rapid growth in the automotive industry as network-enabled and electronic devices are now integral parts of vehicular ecosystems. These include the development of automobile technologies, namely, Connected and Autonomous Vehicles (CAV) and electric vehicles. Attacks on IoV may lead to malfunctioning of Electronic Control Unit (ECU), brakes, control steering issues, and door lock issues that can be fatal in CAV. To mitigate these risks, there is need for a lightweight model to identify attacks on vehicular systems. In this article, an efficient model of an Intrusion Detection System (IDS) is developed to detect anomalies in the vehicular system. The dataset used in this study is an In-Vehicle Network (IVN) communication protocol, i.e., Control Area Network (CAN) dataset generated in a real-time environment. The model classifies different types of attacks on vehicles into reconnaissance, Denial of Service (DoS), and fuzzing attacks. Experimentation with performance metrics of accuracy, precision, recall, and F-1 score are compared across a variety of classification models. The results demonstrate that the proposed model outperforms other classification models.

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Section: Literature Reviewmentioning

confidence: 99%

“…The detailed information of the various types of neural network models is explained and described in [26].…”

mentioning

confidence: 99%

Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

Basavaraj

Tayeb

2022

JSAN

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“…using remotely a cluster of GPUs of a cloud provider) without the need of installing specialized hardware inside the system targeted by the IDS. While CNNs have been extensively applied for attack recognition, they have been hardly used as a means of detecting network anomalies [112]. In theory, it could be possible to train a CNN on data having labels "normal" and "anomaly", however the wide variety of anomalies attacks requires a huge training set that includes all possible anomalies that can be detected by the system.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning

Spadaccino¹,

Cuomo²

2020

Preprint

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Key components of current cybersecurity methods are the Intrusion Detection Systems (IDSs) were different techniques and architectures are applied to detect intrusions. IDSs can be based either on cross-checking monitored events with a database of known intrusion experiences, known as signature-based, or on learning the normal behavior of the system and reporting whether some anomalous events occur, named anomaly-based. This work is dedicated to the application to the Internet of Things (IoT) network where edge computing is used to support the IDS implementation. New challenges that arise when deploying an IDS in an edge scenario are identified and remedies are proposed. We focus on anomaly-based IDSs, showing the main techniques that can be leveraged to detect anomalies and we present machine learning techniques and their application in the context of an IDS, describing the expected advantages and disadvantages that a specific technique could cause.

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“…Self-Organizing Maps (SOM) [11,12], -means [13,14], and expectation maximization [15] are three commonly used clustering algorithms in anomaly detection. The classification-based algorithm mainly includes neural networks [16,17], Bayesian networks [18,19], and support vector machines [20][21][22]. The main drawback of these algorithms is the high training cost and the complexity of the implementation.…”

Section: Introductionmentioning

confidence: 99%

An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps

Liu

Chen

Zhou

et al. 2016

Mathematical Problems in Engineering

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Virtual machines (VM) on a Cloud platform can be influenced by a variety of factors which can lead to decreased performance and downtime, affecting the reliability of the Cloud platform. Traditional anomaly detection algorithms and strategies for Cloud platforms have some flaws in their accuracy of detection, detection speed, and adaptability. In this paper, a dynamic and adaptive anomaly detection algorithm based on Self-Organizing Maps (SOM) for virtual machines is proposed. A unified modeling method based on SOM to detect the machine performance within the detection region is presented, which avoids the cost of modeling a single virtual machine and enhances the detection speed and reliability of large-scale virtual machines in Cloud platform. The important parameters that affect the modeling speed are optimized in the SOM process to significantly improve the accuracy of the SOM modeling and therefore the anomaly detection accuracy of the virtual machine.

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An overview of neural networks use in anomaly Intrusion Detection Systems

Cited by 27 publications

References 7 publications

Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks

Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning

An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps

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