IoT anomaly detection methods and applications: A survey

Chatterjee, Ashis Kumar; Ahmed, Bestoun S.

doi:10.1016/j.iot.2022.100568

Cited by 134 publications

(38 citation statements)

References 102 publications

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“…where y is the ultimate prediction and Tree i(x) is the i-th decision tree's forecast. (15) where y lr , y hgbc , and y cnn represent the predictions of LR, HGBC, and the CNN, respectively. Each model's contribution is weighted differently based on its performance or some other criterion, and these weights are denoted by the variables w lr , w hgbc , and w cnn as shown in Figure 18.…”

Section: Lr-hgbc-cnnmentioning

confidence: 99%

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models

Rahim

Zhong

Ahmad

et al. 2023

Sensors

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Internet of Things (IoT) devices for the home have made a lot of people’s lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their effectiveness. Performance evaluations were conducted for each of the proposed models, revealing their strengths and areas for improvement. Comparative analysis of the models showed that the LR-HGBC-CNN model consistently outperformed the others in both anomaly detection and face recognition tasks, achieving high accuracy, precision, recall, F1 score, and AUC-ROC values. For anomaly detection, the LR-HGBC-CNN model achieved an accuracy of 94%, a precision of 91%, a recall of 96%, an F1 score of 93%, and an AUC-ROC of 0.96. In face recognition, the LR-HGBC-CNN model demonstrated an accuracy of 88%, precision of 86%, recall of 90%, F1 score of 88%, and an AUC-ROC of 0.92. The models exhibited promising capabilities in detecting anomalies, recognizing faces, and integrating these functionalities within smart home IoT devices. The study’s findings underscore the potential of deep learning approaches for enhancing security and privacy in smart homes. However, further research is warranted to evaluate the models’ generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges.

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Section: Lr-hgbc-cnnmentioning

confidence: 99%

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models

Rahim

Zhong

Ahmad

et al. 2023

Sensors

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“…As effects of sensor tampering are huge on IoT devices and sometimes it may lead to the fall down of the entire IoT network. 5 So, in this work, we developed an effective detection system that detects anomalies, especially for sensor tampering cases.…”

Section: Introductionmentioning

confidence: 99%

“…Sensor tampering is nothing but physically modifying the devices, and connections between the nodes in the network or manipulating the data, etc. As effects of sensor tampering are huge on IoT devices and sometimes it may lead to the fall down of the entire IoT network 5 . So, in this work, we developed an effective detection system that detects anomalies, especially for sensor tampering cases.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection in IoT environment using machine learning

Bilakanti,

Pasam,

Palakollu

et al. 2024

Security and Privacy

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This research paper delves into the security concerns within Internet of Things (IoT) networks, emphasizing the need to safeguard the extensive data generated by interconnected physical devices. The presence of anomalies and faults in the sensors and devices deployed within IoT networks can significantly impact the functionality and outcomes of IoT systems. The primary focus of this study is the identification of anomalies in IoT devices arising sensor tampering, with an emphasis on the application of machine learning techniques. While supervised methods like one‐class SVM, Gaussian Naive Bayes, and XG Boost have proven effective in anomaly detection, there has been a noticeable scarcity of research employing unsupervised methods. This scarcity is mainly attributed to the absence of well‐defined ground truths for model training. This research takes an innovative approach by investigating the utility of unsupervised algorithms, including Isolation Forest and Local Outlier Factor, alongside supervised techniques to enhance the precision of anomaly detection.

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“…Although no unified taxonomy of AD methods exists in the literature, these methods are generally divided into three categories according to a previous study [13]. The first category comprises geometric methods.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional geometric and statistical methods require a deep study of the ground truth [13]. Moreover, the model or threshold is not invariable in engineering applications, and these methods are often perplexed by the time dependency of the model.…”

Section: Introductionmentioning

confidence: 99%

Online detection of analog signal anomaly based on the evaluation of local trends

Liu,

Jiang,

2023

Meas. Sci. Technol.

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Online anomaly detection of analog signals plays an important role in equipment fault diagnosis and predictive maintenance. However, the signal often deviates slightly from those seen previously in the early stage of equipment failure, and the anomaly is invisible to the human eye. This kind of anomaly belongs to the typical contextual anomaly. Whether this anomaly can be effectively detected determines whether the failure of the equipment can be detected in the early stage, which is of great significance for safety in production. This study aimed to propose an online anomaly detection method for the analog signals of the quasi-sine waveform class. The sample similarity in the sliding window was evaluated using a sample trend rather than sample amplitude deviation to detect anomalies based on the principle that the trend of the quasi-sinusoid waveform signal in the adjacent space was similar. Compared with the traditional method, the proposed method was sensitive to contextual anomalies and did not need a complete sample data set for model training. The proposed method was finally validated by three data sets with good results.

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IoT anomaly detection methods and applications: A survey

Cited by 134 publications

References 102 publications

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models

Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models

Anomaly detection in IoT environment using machine learning

Online detection of analog signal anomaly based on the evaluation of local trends

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