Anomaly Detection in Smart Environments: A Comprehensive Survey

Fährmann, Daniel; Martín, Laura; Sánchez, Luis; Damer, Naser

doi:10.1109/access.2024.3395051

Cited by 8 publications

(1 citation statement)

References 211 publications

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“…In addition, ML has recently emerged as a promising method for handling intricate data [ 11 , 12 ]. Furthermore, its extensive utilization in identifying anomalous network activities may be attributed to its ease of use and effectiveness [ 13 , 14 , 15 ]. However, the increasing network traffic poses challenges to the performance of ML in real-time data analysis because excessive irrelevant data are being gathered.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach

Nguyen,

Vo,

Yoo

2024

Sensors

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Intrusion detection systems (IDSs) in wireless sensor networks (WSNs) rely heavily on effective feature selection (FS) for enhanced efficacy. This study proposes a novel approach called Genetic Sacrificial Whale Optimization (GSWO) to address the limitations of conventional methods. GSWO combines a genetic algorithm (GA) and whale optimization algorithms (WOA) modified by applying a new three-population division strategy with a proposed conditional inherited choice (CIC) to overcome premature convergence in WOA. The proposed approach achieves a balance between exploration and exploitation and enhances global search abilities. Additionally, the CatBoost model is employed for classification, effectively handling categorical data with complex patterns. A new technique for fine-tuning CatBoost’s hyperparameters is introduced, using effective quantization and the GSWO strategy. Extensive experimentation on various datasets demonstrates the superiority of GSWO-CatBoost, achieving higher accuracy rates on the WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017 datasets than the existing approaches. The comprehensive evaluations highlight the real-time applicability and accuracy of the proposed method across diverse data sources, including specialized WSN datasets and established benchmarks. Specifically, our GSWO-CatBoost method has an inference time nearly 100 times faster than deep learning methods while achieving high accuracy rates of 99.65%, 99.99%, 99.76%, and 99.74% for WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017, respectively.

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Section: Introductionmentioning

confidence: 99%

Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach

Nguyen,

Vo,

Yoo

2024

Sensors

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Contextual Anomaly Detection in Smart Homes Using Temporal Graph Based Distances

Bodaghi,

Nugent

2024

Lecture Notes in Networks and Systems

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Variance‐driven security optimisation in industrial IoT sensors

Gupta,

Singh,

Kumar

et al. 2024

IET Networks

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The Industrial Internet of Things (IIoT) has transformed industrial operations with real‐time monitoring and control, enhancing efficiency and productivity. However, this connectivity brings significant security challenges. This study addresses these challenges by identifying abnormal sensor data patterns using machine learning‐based anomaly detection models. The proposed framework employs advanced algorithms to strengthen industrial defences against cyber threats and disruptions. Focusing on temperature anomalies, a critical yet often overlooked aspect of industrial security, this research fills a gap in the literature by evaluating machine learning models for this purpose. A novel variance‐based model for temperature anomaly detection is introduced, demonstrating high efficacy with accuracy scores of 0.92 and 0.82 on the NAB and AnoML‐IOT datasets, respectively. Additionally, the model achieved F1 scores of 0.96 and 0.89 on these datasets, underscoring its effectiveness in enhancing IIoT security and optimising cybersecurity for industrial processes. This research not only identifies security vulnerabilities but also presents concrete solutions to improve the security posture of IIoT systems.

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Anomaly Detection in Smart Environments: A Comprehensive Survey

Cited by 8 publications

References 211 publications

Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach

Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach

Contextual Anomaly Detection in Smart Homes Using Temporal Graph Based Distances

Variance‐driven security optimisation in industrial IoT sensors

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