An Intrusion Detection System for Detecting Compromised Gateways in Clustered IoT Networks

Abhishek, Nalam Venkata; Lim, Teng Joon; Sikdar, Biplab; Tandon, Anshoo

doi:10.1109/cqr.2018.8445985

Cited by 28 publications

(10 citation statements)

References 17 publications

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“…They propose a clustering-based anonymity method to preserve privacy of data gathered from wearable IoT devices and guarantee the usability of the collected data. In [268], the authors propose a centralized detection system based on the data gathered from clusters to detect the malicious gateways of clusters in IoT networks using packet drop probability as a means to monitor the gateways.…”

Section: H Securitymentioning

confidence: 99%

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Shahraki

Taherkordi

Haugen

et al. 2021

IEEE Trans. Netw. Serv. Manage.

109

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Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this paper, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.

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Section: H Securitymentioning

confidence: 99%

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Shahraki

Taherkordi

Haugen

et al. 2021

IEEE Trans. Netw. Serv. Manage.

109

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show abstract

“…In [ 60 ], the researchers proposed PCA to create an anomaly-based statistical and data-mining IDS that depends on the division of the principal components into the most and least significant principal components. PCA used for intrusion detection is based on payload modeling in [ 61 ], statistical modeling in [ 62 ], machine learning in [ 63 ], and data mining in [ 64 ]. Table 3 shows the advantages and disadvantages of anomaly-based IDS approaches employed in IoT as related to the detection modeling techniques [ 7 , 65 ].…”

Section: Intrusion Detection System (Ids) In Iotmentioning

confidence: 99%

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

Albulayhi

Smadi

Sheldon

et al. 2021

Sensors

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This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).

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“…These detection systems are similar in having an agent for collecting data and a processing unit in the attack detection and reporting the intrusions. However, they are different in: i) Source of data: host-based (Asfaw et al 2010), network-based (Maleh et al 2015), hybrid (Ahmad et al 2019), ii) Method of detection (Abhishek et al 2018): signature-based (Wang et al 2018), anomaly-based, and iii) Architecture: centralized, and distributed (Anthi et al 2018;da Costa et al 2019). Signature based approaches can easily detect the known attacks but unable to detect new attacks.…”

Section: Intrusion and Malware Detectionmentioning

confidence: 99%

Peer Review #1 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.1)"

2021

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Background . The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device's lifespan.Methodology. This paper is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 to 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis were conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results.It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.

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An Intrusion Detection System for Detecting Compromised Gateways in Clustered IoT Networks

Cited by 28 publications

References 17 publications

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

Peer Review #1 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.1)"

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