Adversarial Attacks Against Network Intrusion Detection in IoT Systems

Qiu, Han; Tian, Dong; Zhang, Tianwei; Lu, Jialiang; Memmi, Gérard; Qiu, Meikang

doi:10.1109/jiot.2020.3048038

Cited by 209 publications

(52 citation statements)

References 22 publications

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“…In the year 2019 Qiu et al [26] have proposed a novel adversarial network attack to see if the deep learning-based IDS are equally prone to adversarial attacks. They have reported that with this adversarial attack, the accuracy of Kitsune is compromised.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Approach for Improvement in Kitsune NID

Alabdulatif¹,

Rizvi²

2022

Intelligent Automation &Amp; Soft Computing

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Section: Literature Reviewmentioning

confidence: 99%

“…The scope of this research can further be extended if the investigation on another dataset would also be explored. Moreover, the proposed adversarial attack can be validated to other well-known variants of deep learning models [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Approach for Improvement in Kitsune NID

Alabdulatif¹,

Rizvi²

2022

Intelligent Automation &Amp; Soft Computing

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“…Data poisoning is performed by changing the training data in the damaged edge devices, while model poisoning uses some predefined rules to generates updates to the poisoned model. Zhao Hitaj et al [118] 2017 Differential privacy at different granularity Ibitoye et al [119] 2019 The study uses two deep learning approaches, including, a typical Feedforward Neural Network (FNN) and a Self-normalizing Neural Network (SNN) Hassan et al [120] 2020 A robust decision boundary optimization approach Song et al [121] 2020 The use of deep neural networks Qiu et al [122] 2021 The use of saliency maps to identify the critical features…”

Section: B Poisoning Attackmentioning

confidence: 99%

“…Song et al [121] proposed federated defense against adversarial attacks using deep neural networks. Qiu et al [122] proposed an adversarial attack against deep learning-based network intrusion detection systems to attack one state-of-the-art Kitsune [59]. The proposed attack uses saliency maps to identify the critical features.…”

Section: E Adversarial Attackmentioning

confidence: 99%

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Ferrag¹,

et al. 2021

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In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated deep learning with three deep learning approaches, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). For each deep learning model, we study the performance of centralized and federated learning under three new real IoT traffic datasets, namely, the Bot-IoT dataset, the MQTTset dataset, and the TON_IoT dataset. The goal of this article is to provide important information on federated deep learning approaches with emerging technologies for cyber security. In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks.

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“…Machine learning-based solutions of IDS systems for IoT environment have been introduced [30], [38]. Qiu et al [30] have proposed network intrusion detection system (NIDS) for IoT environment using deep learning AutoEncoder technique. The proposed NIDS was able to achieve 94% accuracy in detecting DoS/DDoS attacks.…”

Section: Dos/ddos Attack and Its Counter Measures In Iot Networkmentioning

confidence: 99%

Ping Flood Attack Pattern Recognition Using a K-Means Algorithm in an Internet of Things (IoT) Network

et al. 2021

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Security is the main challenge in Internet of Things (IoT) systems. The devices on the IoT networks are very heterogeneous, many of them have limited resources, and they are connected globally, which makes the IoT much more challenging to secure than other types of networks. Denial of service (DoS) is the most popular method used to attack IoT networks, either by flooding services or crashing services. Intrusion detection system (IDS) is one of countermeasures for DoS attack. Unfortunately, the existing IDSs are still suffering from detection accuracy problem due to difficulty of recognizing features of the DoS attacks. Thus, we need to determine specific features that representing well the traffic attacks, so the IDS will be able to distinguish normal traffic from the attacks. In this work, we investigate ping flood attack pattern recognition on IoT networks. Experiments were conducted using wireless communication with three different scenarios: normal traffic, attack traffic, and combined normal-attack traffic. Each scenario created an associated dataset. The datasets were then grouped into two clusters: normal and attack. The K-Means algorithm was used to produce the clustering results. The average number of packets in the attack cluster was 95 931 packets, and the average in the normal cluster was 4,068 packets. The accuracy level of the clustering results was calculated using a confusion matrix. The accuracy of the clustering using the implemented K-Means algorithm was 99.94%. The rates from the confusion matrix were true negative (98.62%), true positive (100.00%), false negative (0.00%), and false positive (1.38%).INDEX TERMS Internet of Things (IoT), pattern recognition, ping flood, K-means, clustering.

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Adversarial Attacks Against Network Intrusion Detection in IoT Systems

Cited by 209 publications

References 22 publications

Machine Learning Approach for Improvement in Kitsune NID

Machine Learning Approach for Improvement in Kitsune NID

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Ping Flood Attack Pattern Recognition Using a K-Means Algorithm in an Internet of Things (IoT) Network

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