A graph neural network method for distributed anomaly detection in IoT

Protogerou, Aikaterini; Papadopoulos, Stavros; Drosou, Anastasios; Tzovaras, Dimitrios; Refanidis, Ioannis

doi:10.1007/s12530-020-09347-0

Cited by 75 publications

(34 citation statements)

References 46 publications

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“…Public datasets/software GNN-based solutions Point anomaly Secure water treatment (SWaT), water distribution system (WADI), and critical infrastructure security showdown (CISS) datasets [123], [124], [125] Contextual anomaly SWaT, WADI and BATADAL datasets, as well as the Xcos software and epanetCPA toolbox [124], [125], [126], [127], [128] Collective anomaly LITNET-2020, M2M Using OPC UA, WUSTL-IIoT-2018 and KDD 1999 datasets, as well as the Xcos software and the epanetCPA tool [129], [130], [131] fault flaw overheat defect Fig. 6.…”

Section: Type Of Anomaliesmentioning

confidence: 99%

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Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

132

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The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries towards Industry 4.0. By connecting sensors, instruments and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains such as transportation, energy and factory, as well as for dynamically evolving networks. This paper provides a useful investigation on graph neural networks (GNN) for anomaly detection in IIoTenabled smart transportation, smart energy and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful datasets, challenges and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.

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Section: Type Of Anomaliesmentioning

confidence: 99%

“…Protogerou et al [131] developed a multi-agent system to exploit the collaborative and cooperative nature of intelligent agents for anomaly detection. Each agent will be implemented using a GNN that can learn the representation of physical networks.…”

Section: Collectivementioning

confidence: 99%

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Dai

Tang

2022

IEEE Internet Things J.

132

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“…In [31], by using a test-bed, the range of attacks included sip, ssh, SSL, conn, DNS, and HTTP. In [39], by using a test-bed and CTU-13 datasets, the range of attacks included Infiltration attack, Propagation attack, worm infiltration, and worm propagation attack. In the study conducted by [43], the authors used the self-collection dataset and focused on interval attacks.…”

Section: Analysis Of Type Of Attacks Detectedmentioning

confidence: 99%

Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

et al. 2021

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The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at risk of cyber attacks. The use of deep learning techniques has been adequately adopted by researchers as a solution in securing the IoT environment. Deep learning has also successfully been implemented in various fields, proving its superiority in tackling intrusion detection attacks. Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securing IoT environments. Data from the published studies were retrieved from five databases (IEEE Xplore, Scopus, Web of Science, Science Direct, and MDPI). Out of 2116 identified records, 26 relevant studies were selected to answer the research questions. This review has explored seven deep learning techniques practiced in IoT security, and the results showed their effectiveness in dealing with security challenges in the IoT ecosystem. It is also found that supervised deep learning techniques offer better performance, compared to unsupervised and semi-supervised learning. This analysis provides an insight into how the use of data types and learning methods will affect the performance of deep learning techniques for further contribution to enhancing a novel model for anomaly intrusion detection and prediction.

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“…Table 2 shows the state-of-the-art machine learning algorithms according to three anomaly types. [17] LSTM [22] GNN [8] Multiple [10] AE-ANN [11] LSTM [12] AE-CNN [13] Ensemble [14] Unsupervised AE-CNN [6] Subspace [27] AE [25] AE [18] Self-learning [26] Semi-Supervised TCN [23] AE-LSTM [20] DNN [15] DBN [7]…”

Section: Detection Schemes Based On Machine Learning Algorithmsmentioning

confidence: 99%

A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms

Diro

Chilamkurti

Nguyen

et al. 2021

Sensors

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The Internet of Things (IoT) consists of a massive number of smart devices capable of data collection, storage, processing, and communication. The adoption of the IoT has brought about tremendous innovation opportunities in industries, homes, the environment, and businesses. However, the inherent vulnerabilities of the IoT have sparked concerns for wide adoption and applications. Unlike traditional information technology (I.T.) systems, the IoT environment is challenging to secure due to resource constraints, heterogeneity, and distributed nature of the smart devices. This makes it impossible to apply host-based prevention mechanisms such as anti-malware and anti-virus. These challenges and the nature of IoT applications call for a monitoring system such as anomaly detection both at device and network levels beyond the organisational boundary. This suggests an anomaly detection system is strongly positioned to secure IoT devices better than any other security mechanism. In this paper, we aim to provide an in-depth review of existing works in developing anomaly detection solutions using machine learning for protecting an IoT system. We also indicate that blockchain-based anomaly detection systems can collaboratively learn effective machine learning models to detect anomalies.

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A graph neural network method for distributed anomaly detection in IoT

Cited by 75 publications

References 46 publications

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Graph Neural Networks for Anomaly Detection in Industrial Internet of Things

Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms

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