Securing internet of things using machine and deep learning methods: a survey

Cluster Computing, Год журнала: 2024, Номер 27(7), С. 9065 - 9089

Опубликована: Апрель 16, 2024

Abstract The Internet of Things (IoT) is a vast network devices with sensors or actuators connected through wired wireless networks. It has transformative effect on integrating technology into people’s daily lives. IoT covers essential areas such as smart cities, homes, and health-based industries. However, security privacy challenges arise the rapid growth applications. Vulnerabilities node spoofing, unauthorized access to data, cyberattacks denial service (DoS), eavesdropping, intrusion detection have emerged significant concerns. Recently, machine learning (ML) deep (DL) methods significantly progressed are robust solutions address these issues in devices. This paper comprehensively reviews research focusing ML/DL approaches. also categorizes recent studies based highlights their opportunities, advantages, limitations. These insights provide potential directions for future challenges.

Язык: Английский

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A Literature Review on Security in the Internet of Things: Identifying and Analysing Critical Categories

Computers, Год журнала: 2025, Номер 14(2), С. 61 - 61

Опубликована: Фев. 11, 2025

With the proliferation of IoT-based applications, security requirements are becoming increasingly stringent. Given diversity such systems, selecting most appropriate solutions and technologies to address challenges is a complex activity. This paper provides an exhaustive evaluation existing related IoT domain, analysing studies published between 2021 2025. review explores evolving landscape security, identifying key focus areas, challenges, proposed as presented in recent research. Through this analysis, categorizes efforts into six main areas: emerging (35.2% studies), securing identity management (19.3%), attack detection (17.9%), data protection (8.3%), communication networking (13.8%), risk (5.5%). These percentages highlight research community’s indicate areas requiring further investigation. From leveraging machine learning blockchain for anomaly real-time threat response optimising lightweight algorithms resource-limited devices, researchers propose innovative adaptive threats. The underscores integration advanced enhance system while also highlighting ongoing challenges. concludes with synthesis threats each identified category, along their solutions, aiming support decision-making during design approach applications guide future toward comprehensive efficient frameworks.

Язык: Английский

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Temporal Convolutional Network Approach to Secure Open Charge Point Protocol (OCPP) in Electric Vehicle Charging

IEEE Access, Год журнала: 2025, Номер 13, С. 15272 - 15289

Опубликована: Янв. 1, 2025

Язык: Английский

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

Discover Internet of Things, Год журнала: 2025, Номер 5(1)

Опубликована: Янв. 22, 2025

Язык: Английский

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STFNIoT:Lightweight IoT Intrusion Detection Based on Explainable Analysis Using Spatiotemporal Fusion Networks

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Янв. 28, 2025

With the widespread popularity of IoT applications, devices are increasingly becoming targets cyber attacks. Existing intrusion detection systems usually face computing resource limitations and accuracy challenges when facing complex, high-dimensional attack traffic data. Therefore, this paper proposes a lightweight framework STFNIoT based on interpretable analysis spatiotemporal fusion networks, which combines principal component (PCA) deep learning models to address above problems. PCA performs data dimensionality reduction reduce feature redundancy while retaining key information. Subsequently, network(STFN) is used for learning. STFN contains two components: convolutional neural network (CNN) extracting spatial features bidirectional long short-term memory (BiLSTM) capturing time-dependent features, thereby efficiently relationship between devices. In addition, integrates SHAP interpretability algorithm, can intuitively reveal decision-making process model enhance transparency reliability system. Experimental results show that achieves 100%, 97.70% 97.15% in binary, hexaclass multiclass tasks Edge-IIoTset dataset, respectively, significantly improving performance compared with existing methods. modular design effectively reduces computational overhead suitable resource-constrained environments. This study provides an efficient explainable method.

Язык: Английский

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Optimizing Intrusion Detection for IoT: A Systematic Review of Machine Learning and Deep Learning Approaches With Feature Selection and Data Balancing

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Год журнала: 2025, Номер 15(2)

Опубликована: Март 28, 2025

ABSTRACT As the Internet of Things (IoT) continues expanding its footprint across various sectors, robust security systems to mitigate associated risks are more critical than ever. Intrusion Detection Systems (IDS) fundamental in safeguarding IoT infrastructures against malicious activities. This systematic review aims guide future research by addressing six pivotal questions that underscore development advanced IDS tailored for environments. Specifically, concentrates on applying machine learning (ML) and deep (DL) technologies enhance capabilities. It explores feature selection methodologies aimed at developing lightweight solutions both effective efficient scenarios. Additionally, assesses different datasets balancing techniques, which crucial training models perform accurately reliably. Through a comprehensive analysis existing literature, this highlights significant trends, identifies current gaps, suggests studies optimize frameworks ever‐evolving landscape.

Язык: Английский

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Optimizing Smart Home Intrusion Detection With Harmony-Enhanced Extra Trees

IEEE Access, Год журнала: 2024, Номер 12, С. 117761 - 117786

Опубликована: Янв. 1, 2024

In this study, we present an innovative network intrusion detection system (IDS) tailored for Internet of Things (IoT)-based smart home environments, offering a novel deployment scheme that addresses the full spectrum security challenges. Distinct from existing approaches, our comprehensive strategy not only proposes model but also incorporates IoT devices as potential vectors in cyber threat landscape, consideration often neglected previous research. Utilizing harmony search algorithm (HSA), refined extra trees classifier (ETC) by optimizing extensive array hyperparameters, achieving level sophistication and performance enhancement surpasses typical methodologies. Our was rigorously evaluated using robust real-time dataset, uniquely gathered 105 devices, reflecting more authentic complex scenario compared to simulated or limited datasets prevalent literature. commitment collaborative progress cybersecurity is demonstrated through public release source code. The underwent exhaustive testing 2-class, 8-class, 34-class configurations, showcasing superior accuracy (99.87%, 99.51%, 99.49%), precision (97.41%, 96.02%, 96.07%), recall (98.45%, 87.14%, 87.1%), f1-scores (97.92%, 90.65%, 90.61%) firmly establish its efficacy. Thiswork marks significant advancement security, providing scalable effective IDS solution adaptable intricate dynamics modern networks. findings pave way future endeavors realm defense, ensuring homes remain safe havens era digital vulnerability.

Язык: Английский

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Hybrid Machine Learning for IoT-Enabled Smart Buildings

Informatics, Год журнала: 2025, Номер 12(1), С. 17 - 17

Опубликована: Фев. 11, 2025

This paper presents an intrusion detection system (IDS) leveraging a hybrid machine learning approach aimed at enhancing the security of IoT devices edge, specifically for those utilizing TCP/IP protocol. Recognizing critical challenges posed by rapid expansion networks, this work evaluates proposed IDS model with primary focus on optimizing training time without sacrificing accuracy. The begins comprehensive review existing models IDS, highlighting both their strengths and limitations. It then provides overview technologies methodologies implemented in work, including utilization “Botnet Traffic Dataset For Smart Buildings”, newly released public dataset tailored threat detection. is explained detail, followed discussion experimental results that assess model’s performance real-world conditions. Furthermore, evaluated its effectiveness within smart building environments, demonstrating how it can address unique such as resource constraints real-time edge. aims to contribute development efficient, reliable, scalable solutions protect ecosystems from emerging threats.

Язык: Английский

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Online Machine Learning for Intrusion Detection in Electric Vehicle Charging Systems

Mathematics, Год журнала: 2025, Номер 13(5), С. 712 - 712

Опубликована: Фев. 22, 2025

Electric vehicle (EV) charging systems are now integral to smart grids, increasing the need for robust and scalable cyberattack detection. This study presents an online intrusion detection system that leverages Adaptive Random Forest classifier with Windowing drift identify real-time evolving threats in EV infrastructures. The is evaluated using real-world network traffic from CICEVSE2024 dataset, ensuring practical applicability. For binary detection, model achieves 0.9913 accuracy, 0.9999 precision, 0.9914 recall, F1-score of 0.9956, demonstrating highly accurate threat It effectively manages concept drift, maintaining average accuracy 0.99 during events. In multiclass attains 0.9840 0.9831 event 0.96. computationally efficient, processing each instance just 0.0037 s, making it well-suited deployment. These results confirm machine learning methods can secure source code publicly available on GitHub, reproducibility fostering further research. provides a efficient cybersecurity solution protecting networks threats.

Язык: Английский

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Research on Network Intrusion Detection Model Based on Hybrid Sampling and Deep Learning

Sensors, Год журнала: 2025, Номер 25(5), С. 1578 - 1578

Опубликована: Март 4, 2025

This study proposes an enhanced network intrusion detection model, 1D-TCN-ResNet-BiGRU-Multi-Head Attention (TRBMA), aimed at addressing the issues of incomplete learning temporal features and low accuracy in classification malicious traffic found existing models. The TRBMA model utilizes Temporal Convolutional Networks (TCNs) to improve ResNet18 architecture incorporates Bidirectional Gated Recurrent Units (BiGRUs) Multi-Head Self-Attention mechanisms enhance comprehensive features. Additionally, ResNet is adapted into a one-dimensional version that more suitable for processing time-series data, while AdamW optimizer employed convergence speed generalization ability during training. Experimental results on CIC-IDS-2017 dataset indicate achieves 98.66% predicting types, with improvements precision, recall, F1-score compared baseline model. Furthermore, address challenge identification rates types small sample sizes unbalanced datasets, this paper introduces (BS-OSS), variant integrates Borderline SMOTE-OSS hybrid sampling. demonstrate effectively identifies sizes, achieving overall prediction 99.88%, thereby significantly enhancing performance

Язык: Английский

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