Cross layer-based intrusion detection based on network behavior for IoT

Amouri, Amar; Alaparthy, Vishwa Teja; Morgera, S.D.

doi:10.1109/wamicon.2018.8363921

Cited by 50 publications

(18 citation statements)

References 11 publications

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“…Amouri, Alaparthy, and Morgera [28] developed an IDS for IoT networks by applying supervised machine learning. The IDS attempts to profile the benign behaviour of the nodes and identify any anomalies on the network traffic.…”

Section: B Machine Learning Idssmentioning

confidence: 99%

A Supervised Intrusion Detection System for Smart Home IoT Devices

Anthi

Williams

Slowinska

et al. 2019

IEEE Internet Things J.

428

167

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The proliferation in Internet of Things (IoT) devices, which routinely collect sensitive information, is demonstrated by their prominence in our daily lives. Although such devices simplify and automate every day tasks, they also introduce tremendous security flaws. Current insufficient security measures employed to defend smart devices make IoT the 'weakest' link to breaking into a secure infrastructure, and therefore an attractive target to attackers. This paper proposes a three layer Intrusion Detection System (IDS) that uses a supervised approach to detect a range of popular network based cyber-attacks on IoT networks. The system consists of three main functions: 1) classify the type and profile the normal behaviour of each IoT device connected to the network, 2) identifies malicious packets on the network when an attack is occurring, and 3) classifies the type of the attack that has been deployed. The system is evaluated within a smart home testbed consisting of 8 popular commercially available devices. The effectiveness of the proposed IDS architecture is evaluated by deploying 12 attacks from 4 main network based attack categories such as: Denial of Service (DoS), Man-In-The-Middle (MITM)/Spoofing, Reconnaissance, and Replay. Additionally, the system is also evaluated against 4 scenarios of multi-stage attacks with complex chains of events. The performance of the system's three core functions result in an F-measure of: 1) 96.2%, 2) 90.0%, and 3) 98.0%. This demonstrates that the proposed architecture can automatically distinguish between IoT devices on the network, whether network activity is malicious or benign, and detect which attack was deployed on which device connected to the network successfully.

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Section: B Machine Learning Idssmentioning

confidence: 99%

A Supervised Intrusion Detection System for Smart Home IoT Devices

Anthi

Williams

Slowinska

et al. 2019

IEEE Internet Things J.

428

167

View full text Add to dashboard Cite

show abstract

“…Amouri et al [99] proposed a NIDS based on the protocol model approach and machine learning. This system consists of two detection stages.…”

Section: Idss Designed For Iot Systemsmentioning

confidence: 99%

Intrusion detection systems for IoT-based smart environments: a survey

Elrawy¹,

2018

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One of the goals of smart environments is to improve the quality of human life in terms of comfort and efficiency. The Internet of Things (IoT) paradigm has recently evolved into a technology for building smart environments. Security and privacy are considered key issues in any real-world smart environment based on the IoT model. The security vulnerabilities in IoT-based systems create security threats that affect smart environment applications. Thus, there is a crucial need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. This article presents a comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms. This article also provides deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT architecture. This work demonstrates that despite previous studies regarding the design and implementation of IDSs for the IoT paradigm, developing efficient, reliable and robust IDSs for IoT-based smart environments is still a crucial task. Key considerations for the development of such IDSs are introduced as a future outlook at the end of this survey.

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“…A cross layer-based intrusion detection scheme [57] based on network behavior is proposed for IoT. The scheme consists of two detection levels namely local and global.…”

Section: ) Machine Learning-based Schemesmentioning

confidence: 99%

Security Analysis of Network Anomalies Mitigation Schemes in IoT Networks

2020

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The Internet of Things (IoT) is on the rise and it is giving a new shape to several fields such as smart cities, smart homes, smart health, etc. as it facilitates the connection of physical objects to the internet. However, this advancement comes along with new challenges in terms of security of the devices in the IoT networks. Some of these challenges come as network anomalies. Hence, this has prompted the use of network anomaly mitigation schemes as an integral part of the defense mechanisms of IoT networks in order to protect the devices from malicious users. Thus, several schemes have been proposed to mitigate network anomalies. This paper covers a review of different network anomaly mitigation schemes in IoT networks. The schemes' objectives, operational procedures, and strengths are discussed. A comparison table of the reviewed schemes, as well as a taxonomy based on the detection methodology, is provided. In contrast to other surveys that presented qualitative evaluations, our survey provides both qualitative and quantitative evaluations. The UNSW-NB15 dataset was used to conduct a performance evaluation of some classification algorithms used for network anomaly mitigation schemes in IoT. Finally, challenges and open issues in the development of network anomaly mitigation schemes in IoT are discussed.

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Cross layer-based intrusion detection based on network behavior for IoT

Cited by 50 publications

References 11 publications

A Supervised Intrusion Detection System for Smart Home IoT Devices

A Supervised Intrusion Detection System for Smart Home IoT Devices

Intrusion detection systems for IoT-based smart environments: a survey

Security Analysis of Network Anomalies Mitigation Schemes in IoT Networks

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