Anomaly Detection at the IoT Edge using Deep Learning

Utomo, Darmawan; Hsiung, Pao-Ann

doi:10.1109/icce-tw46550.2019.8991929

Cited by 14 publications

(6 citation statements)

References 5 publications

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“…In the context of Industrial IoT (IIoT), work has been proposed to detect such anomalies. Utomo et al [50] develop a system performing anomaly detection on power grids sensor readings. Anomaly alerts could be used not only as an indication of an illegal intrusion, but also as a means to ensure grid safety preventing failures and blackouts.…”

Section: The Edge-enabled Approachmentioning

confidence: 99%

Intrusion detection systems for IoT: Opportunities and challenges offered by edge computing

Spadaccino¹,

Cuomo²

2022

ITU J-FET

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Key components of current cybersecurity methods are the Intrusion Detection Systems (IDSs), where different techniques and architectures are applied to detect intrusions. IDSs can be based either on cross-checking monitored events with a database of known intrusion experiences, known as signature-based, or on learning the normal behavior of the system and reporting whether anomalous events occur, named anomaly-based. This work is dedicated to the application of IDS to the Internet of Things (IoT) networks, where also edge computing is used to support IDS implementation. Specific attention is given to IDSs which leverage device classification at the edge. New challenges that arise when deploying an IDS in an edge scenario are identified and remedies are proposed.

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Section: The Edge-enabled Approachmentioning

confidence: 99%

Intrusion detection systems for IoT: Opportunities and challenges offered by edge computing

Spadaccino¹,

Cuomo²

2022

ITU J-FET

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“…This section will focus on the important roles of anomaly detection systems in industries, smart grids, and smart cities. Health [20] Smart Cities [21] Smart Grids [22] Smart Home [23] [24]…”

Section: Significance Of Anomaly Detection In the Iotmentioning

confidence: 99%

“…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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“…Utomo et al [70] develop a system performing anomaly detection on power grids sensor readings. Anomaly alerts could be used not only as an indication of an illegal intrusion, but also as a means to ensure grid safety preventing failures and blackouts.…”

Section: The Edge-enabled Approachmentioning

confidence: 99%

Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning

Spadaccino¹,

Cuomo²

2020

Preprint

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Key components of current cybersecurity methods are the Intrusion Detection Systems (IDSs) were different techniques and architectures are applied to detect intrusions. IDSs can be based either on cross-checking monitored events with a database of known intrusion experiences, known as signature-based, or on learning the normal behavior of the system and reporting whether some anomalous events occur, named anomaly-based. This work is dedicated to the application to the Internet of Things (IoT) network where edge computing is used to support the IDS implementation. New challenges that arise when deploying an IDS in an edge scenario are identified and remedies are proposed. We focus on anomaly-based IDSs, showing the main techniques that can be leveraged to detect anomalies and we present machine learning techniques and their application in the context of an IDS, describing the expected advantages and disadvantages that a specific technique could cause.

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Anomaly Detection at the IoT Edge using Deep Learning

Cited by 14 publications

References 5 publications

Intrusion detection systems for IoT: Opportunities and challenges offered by edge computing

Intrusion detection systems for IoT: Opportunities and challenges offered by edge computing

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

Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning

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