Feed-forward neural network for Network Intrusion Detection

Rosay, Arnaud; Carlier, Florent; Leroux, Pascal

doi:10.1109/vtc2020-spring48590.2020.9129472

Cited by 18 publications

(16 citation statements)

References 14 publications

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“…Alheeti et al [20] proposed an intelligent IDS using back-propagation neural networks to detect DoS attacks in external vehicular networks using the Kyoto 2006+ dataset, but did not consider other attack types. Rosay et al [21] proposed a multi-layer perceptron (MLP) based network IDS for cyber-attack detection in IoT and connected vehicles. The proposed model has been implemented on an automotive microprocessor, and its performance is evaluated on the two variants of the CICIDS2017 dataset.…”

Section: B External Network Intrusion Detectionmentioning

confidence: 99%

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

Yang

Moubayed

Shami

2022

IEEE Internet Things J.

174

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Modern vehicles, including connected vehicles and autonomous vehicles, nowadays involve many electronic control units connected through intra-vehicle networks to implement various functionalities and perform actions. Modern vehicles are also connected to external networks through vehicle-toeverything technologies, enabling their communications with other vehicles, infrastructures, and smart devices. However, the improving functionality and connectivity of modern vehicles also increase their vulnerabilities to cyber-attacks targeting both intra-vehicle and external networks due to the large attack surfaces. To secure vehicular networks, many researchers have focused on developing intrusion detection systems (IDSs) that capitalize on machine learning methods to detect malicious cyberattacks. In this paper, the vulnerabilities of intra-vehicle and external networks are discussed, and a multi-tiered hybrid IDS that incorporates a signature-based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks. Experimental results illustrate that the proposed system can detect various types of known attacks with 99.99% accuracy on the CAN-intrusion-dataset representing the intra-vehicle network data and 99.88% accuracy on the CICIDS2017 dataset illustrating the external vehicular network data. For the zero-day attack detection, the proposed system achieves high F1-scores of 0.963 and 0.800 on the above two datasets, respectively. The average processing time of each data packet on a vehicle-level machine is less than 0.6 ms, which shows the feasibility of implementing the proposed system in real-time vehicle systems. This emphasizes the effectiveness and efficiency of the proposed IDS.

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Section: B External Network Intrusion Detectionmentioning

confidence: 99%

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

Yang

Moubayed

Shami

2022

IEEE Internet Things J.

174

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“…This strategy produces satisfactory attack detection if the inspected traffic has predefined characteristics of malicious activity. Alternatively, artificial intelligence can be used to detect malicious signatures in application DoS attacks [35][36][37][38]. However, these approaches have limitations, given the existing traffic variations concerning the HTTP requests.…”

Section: Related Workmentioning

confidence: 99%

SDToW: A Slowloris Detecting Tool for WMNs

et al. 2020

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Denial of service (DoS) attacks play a significant role in contemporary cyberspace scenarios. A variety of different DoS attacks pollute networks by exploring various vulnerabilities. A group of DoS called application DoS attacks explore application vulnerabilities. This work presents a tool that detects and blocks an application DoS called Slowloris on wireless mesh networks (WMNs). Our tool, called SDToW, is designed to effectively use the structure of the WMNs to block the Slowloris attack. SDToW uses three different modules to detect and block the attack. Each module has its specific tasks and thus optimizes the overall detection and block efficiency. Our solution blocks the attacker on its first WMN hop, reducing the malicious traffic on the network and avoiding further attacks from the blocked user. The comparison results show that SDToW performs with 66.7% less processing consumption and 89.1% less memory consumption than Snort. Our solution does not limit the number of parallel connections per user. Hence, by avoiding this limitation, SDToW has a lower incidence of false positive errors than Snort.

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“…The proposed confidence averaging ensemble model also achieves the highest F1-score of 99.925%, which is slightly higher than the F1-score of the concatenation model (99.899%). The two ensemble models also outperform other recent methods in the literature [4] [12]. Additionally, the total training time of the confidence averaging is also much lower than the concatenation approach.…”

Section: B Experimental Results and Discussionmentioning

confidence: 65%

“…ML and DL models have been widely used in IoV intrusion detection tasks. Rosay et al [4] proposed a DL-based IDS for connected vehicles using Multi-Layer Perceptron (MLP). The MLP model was evaluated on an automotive microprocessor using the CICIDS2017 dataset.…”

Section: Related Workmentioning

confidence: 99%

“…The lack of fundamental security measures enables cyber-attackers to inject malicious messages to IVNs and launch different types of attacks, like Denial of Service (DoS), fuzzy, and spoofing attacks. On the other hand, the emerging cellular connections between connected vehicles and external networks have made these vehicles vulnerable to various conventional cyber-attacks [4]. Therefore, it is crucial to develop Intrusion Detection Systems (IDSs) to protect IoV systems and smart vehicles by identifying cyber-attacks [5].…”

Section: Introductionmentioning

confidence: 99%

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A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles

Yang¹,

Shami²

2022

Preprint

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Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats. Due to the lack of authentication and encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs) are essential approaches to protect modern vehicle systems from network attacks. In this paper, a transfer learning and ensemble learning-based IDS is proposed for IoV systems using convolutional neural networks (CNNs) and hyper-parameter optimization techniques. In the experiments, the proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two well-known public benchmark IoV security datasets: the Car-Hacking dataset and the CICIDS2017 dataset. This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.

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Feed-forward neural network for Network Intrusion Detection

Cited by 18 publications

References 14 publications

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

SDToW: A Slowloris Detecting Tool for WMNs

A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles

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