Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review

Discover Internet of Things, Journal Year: 2025, Volume and Issue: 5(1)

Published: Jan. 22, 2025

Language: Английский

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Anonymizing Big Data Streams Using In-memory Processing: A Novel Model Based on One-time Clustering

Journal of Signal Processing Systems, Journal Year: 2024, Volume and Issue: 96(6-7), P. 333 - 356

Published: May 25, 2024

Language: Английский

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An Intrusion Detection System over the IoT Data Streams Using eXplainable Artificial Intelligence (XAI)

Sensors, Journal Year: 2025, Volume and Issue: 25(3), P. 847 - 847

Published: Jan. 30, 2025

The rise in intrusions on network and IoT systems has led to the development of artificial intelligence (AI) methodologies intrusion detection (IDSs). However, traditional AI or machine learning (ML) methods can compromise accuracy due vast, diverse, dynamic nature data generated. Moreover, many these lack transparency, making it challenging for security professionals make predictions. To address challenges, this paper presents a novel IDS architecture that uses deep (DL)-based methodology along with eXplainable (XAI) techniques create explainable models systems, empowering analysts use effectively. DL are needed train enormous amounts produce promising results. Three different models, i.e., customized 1-D convolutional neural networks (1-D CNNs), (DNNs), pre-trained model TabNet, proposed. experiments performed seven datasets TON_IOT. CNN dataset achieves an impressive 99.24%. Meanwhile, six datasets, most DNN achieve 100% accuracy, further validating effectiveness proposed models. In all least-performing is TabNet. Implementing method real time requires explanation predictions Thus, XAI implemented understand essential features responsible predicting particular class.

Language: Английский

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A systematic review of metaheuristics-based and machine learning-driven intrusion detection systems in IoT

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 96, P. 101984 - 101984

Published: May 24, 2025

Language: Английский

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An IoT Intrusion Detection Approach Based on Salp Swarm and Artificial Neural Network

International Journal of Network Management, Journal Year: 2024, Volume and Issue: 35(1)

Published: Aug. 18, 2024

ABSTRACT The Internet of Things has emerged as a significant and influential technology in modern times. IoT presents solutions to reduce the need for human intervention emphasizes task automation. According Cisco report, there were over 14.7 billion devices 2023. However, number users utilizing this grows, so does potential security breaches intrusions. For instance, insecure devices, such smart home appliances or industrial sensors, can be vulnerable hacking attempts. Hackers might exploit these vulnerabilities gain unauthorized access sensitive data even control remotely. To address prevent issue, work proposes integrating intrusion detection systems (IDSs) with an artificial neural network (ANN) salp swarm algorithm (SSA) enhance environment. SSA functions optimization that selects optimal networks multilayer perceptron (MLP). proposed approach been evaluated using three novel benchmarks: Edge‐IIoTset, WUSTL‐IIOT‐2021, IoTID20. Additionally, various experiments have conducted assess effectiveness approach. comparison is made between several approaches from literature, particularly SVM combined metaheuristic algorithms. Then, identify most crucial features each dataset improve performance. SSA‐MLP outperforms other algorithms 88.241%, 93.610%, 97.698% IoTID20, WUSTL, respectively.

Language: Английский

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A comprehensive survey on intrusion detection algorithms

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 121, P. 109863 - 109863

Published: Nov. 23, 2024

Language: Английский

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A Comprehensive Survey on Generative AI Solutions in IoT Security

Electronics, Journal Year: 2024, Volume and Issue: 13(24), P. 4965 - 4965

Published: Dec. 17, 2024

The influence of Artificial Intelligence in our society is becoming important due to the possibility carrying out analysis large amount data that increasing number interconnected devices capture and send as well making autonomous instant decisions from information machines are now able extract, saving time efforts some determined tasks, specially cyberspace. One key issues concerns security this cyberspace controlled by machines, so system can run properly. A particular situation, given heterogeneous special nature environment, case IoT. limited resources components such a network distributed topology make these types environments vulnerable many different attacks leakages. capability Generative generate contents autonomously learn predict situations be very useful for automatically instantly, significantly enhancing IoT systems. Our aim work provide an overview Intelligence-based existing solutions diverse set try anticipate future research lines field delve deeper.

Language: Английский

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Securing smart agriculture networks using bio-inspired feature selection and transfer learning for effective image-based intrusion detection

Internet of Things, Journal Year: 2024, Volume and Issue: unknown, P. 101422 - 101422

Published: Nov. 1, 2024

Language: Английский

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