Anomaly Detection in IoT: Methods, Techniques and Tools

Vigoya, Laura; López-Vizcaíno, Manuel; Iglesias, Diego Fernández; Díaz, Víctor Manuel Carneiro

doi:10.3390/proceedings2019021004

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…As previously anticipated, considering previous works on the topic, different custom datasets are build and adopted to design protection systems, by also considering the MQTT protocol. Particularly, in literature, data of DHT11 temperature sensors connected to MQTT public services are adopted [ 38 ], as well as temperature, inRow and coolant sensors [ 39 ]. Although such works are promising, related datasets are not publicly available.…”

Section: Related Workmentioning

confidence: 99%

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Vaccari

Chiola

Aiello

et al. 2020

Sensors

172

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IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

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Section: Related Workmentioning

confidence: 99%

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Vaccari

Chiola

Aiello

et al. 2020

Sensors

172

View full text Add to dashboard Cite

show abstract

“…In this work, the performance of the proposed system was evaluated based on the Anomaly Detection Rate (ADR) metric, which identifies the rate of abnormal events and observations in a period of time [ 57 ]. This metric is considered to be one of most important metrics, since the main problem in wireless networks is the integrity of exchanged data.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…The received data is classified based on SVM algorithm. Compared to rule-based processing, machine learning algorithms have the ability to detect new forms of attacks in addition to the known ones [ 57 ], since a model is build to recognize normal data, and any deviation from such pattern is detected as abnormal data. When an attack is detected, IDC publishes notifications to be received by subscribed users.…”

Section: Ads For Smart Hospital Iot Systemsmentioning

confidence: 99%

Efficient Anomaly Detection for Smart Hospital IoT Systems

Said

Yahyaoui

Abdellatif

2021

Sensors

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In critical Internet of Things (IoT) application domains, such as the Defense Industry and Healthcare, false alerts have many negative effects, such as fear, disruption of emergency services, and waste of resources. Therefore, an alert must only be sent if triggered by a correct event. Nevertheless, IoT networks are exposed to intrusions, which affects event detection accuracy. In this paper, an Anomaly Detection System (ADS) is proposed in a smart hospital IoT system for detecting events of interest about patients’ health and environment and, at the same time, for network intrusions. Providing a single system for network infrastructure supervision and e-health monitoring has been shown to optimize resources and enforce the system reliability. Consequently, decisions regarding patients’ care and their environments’ adaptation are more accurate. The low latency is ensured, thanks to a deployment on the edge to allow for a processing close to data sources. The proposed ADS is implemented and evaluated while using Contiki Cooja simulator and the e-health event detection is based on a realistic data-set analysis. The results show a high detection accuracy for both e-health related events and IoT network intrusions.

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“…In addition to the classification of the outlier detection techniques, authors in [27] proposed a methodology for outlier detection in IoT through a systematic analysis of the data set based on five stages. The first stage is to define a scenario of generating a labelled dataset using mathematical modelling on a real IoT system.…”

Section: Outlier Detection Techniques In Iotmentioning

confidence: 99%

A Survey of Outlier Detection Techniques in IoT: Review and Classification

Samara

Bennis

Abouaïssa

et al. 2022

JSAN

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The Internet of Things (IoT) is a fact today where a high number of nodes are used for various applications. From small home networks to large-scale networks, the aim is the same: transmitting data from the sensors to the base station. However, these data are susceptible to different factors that may affect the collected data efficiency or the network functioning, and therefore the desired quality of service (QoS). In this context, one of the main issues requiring more research and adapted solutions is the outlier detection problem. The challenge is to detect outliers and classify them as either errors to be ignored, or important events requiring actions to prevent further service degradation. In this paper, we propose a comprehensive literature review of recent outlier detection techniques used in the IoTs context. First, we provide the fundamentals of outlier detection while discussing the different sources of an outlier, the existing approaches, how we can evaluate an outlier detection technique, and the challenges facing designing such techniques. Second, comparison and discussion of the most recent outlier detection techniques are presented and classified into seven main categories, which are: statistical-based, clustering-based, nearest neighbour-based, classification-based, artificial intelligent-based, spectral decomposition-based, and hybrid-based. For each category, available techniques are discussed, while highlighting the advantages and disadvantages of each of them. The related works for each of them are presented. Finally, a comparative study for these techniques is provided.

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Anomaly Detection in IoT: Methods, Techniques and Tools

Cited by 5 publications

References 3 publications

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Efficient Anomaly Detection for Smart Hospital IoT Systems

A Survey of Outlier Detection Techniques in IoT: Review and Classification

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