Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends

Sensors, Год журнала: 2024, Номер 24(6), С. 1968 - 1968

Опубликована: Март 20, 2024

With its exponential growth, the Internet of Things (IoT) has produced unprecedented levels connectivity and data. Anomaly detection is a security feature that identifies instances in which system behavior deviates from expected norm, facilitating prompt identification resolution anomalies. When AI IoT are combined, anomaly becomes more effective, enhancing reliability, efficacy, integrity systems. AI-based systems capable identifying wide range threats environments, including brute force, buffer overflow, injection, replay attacks, DDoS assault, SQL back-door exploits. Intelligent Intrusion Detection Systems (IDSs) imperative devices, help detect anomalies or intrusions network, as increasingly employed several industries but possesses large attack surface presents entry points for attackers. This study reviews literature on infrastructure using machine learning deep learning. paper discusses challenges detecting systems, highlighting increasing number attacks. It recent work deep-learning schemes networks, summarizing available literature. From this survey, it concluded further development current needed by varied datasets, real-time testing, making scalable.

Язык: Английский

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Deep Learning Models with Transfer Learning and Ensemble for Enhancing Cybersecurity in IoT Use Cases

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 28, 2025

Internet of Things (IoT) applications have made inroads into different domains, providing unique solutions—Internet technology offers seamless integration physical and digital worlds. However, the broad nature technologies protocols used in IoT has increased vulnerability from malicious attackers. Hence, protecting cyber-attacks is imperative. Researchers implemented intrusion detection systems to overcome this issue improve cybersecurity scenarios. With new threats cybercrime emerging, a continuous effort required enhance security applications. To address pressing need, we present our study that proposes deep learning-based framework bolster at use cases level by exploiting power transfer learning ensembling it models pre-trained larger datasets. Deep attain high performance with help hyperparameter tuning, achieve through PSO proposed system. Our ensemble system shows how individual can outperform using best-performing as constituents approach. We introduce an algorithm called — Optimized Ensemble Learning-Based Intrusion Detection (OEL-ID). This leverages corresponding optimization strategies boost for improved cyber Using UNSW-NB15 benchmark dataset, empirical demonstrates method, compared some existing models, obtained accuracy 98.89%, which, turn, provided highest comparative accuracy. Therefore, be allows significant system's underlying

Язык: Английский

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Smart and Sustainable Wireless Electric Vehicle Charging Strategy with Renewable Energy and Internet of Things Integration

Sustainability, Год журнала: 2024, Номер 16(6), С. 2487 - 2487

Опубликована: Март 17, 2024

This study addresses the challenges associated with electric vehicle (EV) charging in office environments. These include (1) reliance on manual cable connections, (2) constrained options, (3) safety concerns management, and (4) lack of dynamic capabilities. research focuses an innovative wireless power transfer (WPT) system specifically designed for use parking areas. incorporates renewable energy resources (RERs) uses transformative Internet Things (IoT). It employs a mix solar systems battery storage solutions to facilitate sustainable efficient supply EVs. The integration IoT technology allows automatic initiation as soon EV is parked. Additionally, implementation Blynk application offers users real-time access information regarding operational status photovoltaic levels their further enhanced RFID technologies provide updates availability slots implement strict security protocols user authentication protection. also includes case focusing this settings. achieves 95.9% IRR, lower NPC USD 1.52 million, 56.7% contribution by RERs, it reduces annual carbon emissions 173,956 kg CO2.

Язык: Английский

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Classification of intrusion cyber‐attacks in smart power grids using deep ensemble learning with metaheuristic‐based optimization

Expert Systems, Год журнала: 2024, Номер unknown

Опубликована: Фев. 12, 2024

Abstract The most advanced power grid design, known as a ‘smart grid’, integrates information and communication technology (ICT) with conventional system to enable remote management of electricity distribution. intelligent cyber‐physical architecture enables bidirectional, real‐time data sharing between suppliers consumers through smart meters metering infrastructure (AMI). Data protection issues, such tampering, firmware exploitation, the leakage sensitive arise due grid's substantial reliance on ICT. To maintain reliable efficient distribution, these issues must be identified resolved quickly. Intrusion detection is essential for providing secure services alerting administrators in case adversary attacks. This paper proposes an intrusion classification scheme that identifies several types cyber attacks modern grids. Grey‐Wolf metaheuristic optimization‐based feature selection used learn non‐linear, overlapping, complex electrical properties. An extended deep‐stacked ensemble technique by putting predictions from weak learners (CNNs) into meta‐learner (MLP). outcomes this approach are explained confirmed using explainable AI (XAI). publicly available dataset Mississippi State University Oak Ridge National Laboratory (MSU‐ORNL) conduct experiments. experimental results show proposed method achieved peak accuracy 96.6% while scrutinizing original MSU‐ORNL set maximum 99% when analysing selected set. Therefore, may protect systems against security

Язык: Английский

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A novel deep learning-based intrusion detection system for IoT DDoS security

Internet of Things, Год журнала: 2024, Номер 28, С. 101336 - 101336

Опубликована: Авг. 29, 2024

Язык: Английский

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Two-step data clustering for improved intrusion detection system using CICIoT2023 dataset

e-Prime - Advances in Electrical Engineering Electronics and Energy, Год журнала: 2024, Номер 9, С. 100673 - 100673

Опубликована: Июль 5, 2024

The issue of network security is an important and delicate when it comes to the privacy organizations individuals, especially sensitive information transmitted across these networks. importance intrusion detection systems, which a very component protecting reducing damage resulting from attacks penetrations has increased due adoption most recent regulations on advanced web services, whether government banking e-mail, or e-marketing. goal this paper construct system using deep learning algorithms based new dataset named CICIoT2023. proposed model addresses challenges associated with datasets in terms high dimensionality by adopting methods reduce their size improve efficiency. A clustering technique for method combination between optimization algorithm static tools was proposed. evaluated determine its efficiency several evaluation measures. results show that comparison earlier research conducted same datasets, suggested performs better attack detection. As result, offers level trust.

Язык: Английский

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Hybrid CNN–BiLSTM–DNN Approach for Detecting Cybersecurity Threats in IoT Networks

Computers, Год журнала: 2025, Номер 14(2), С. 58 - 58

Опубликована: Фев. 10, 2025

The Internet of Things (IoT) ecosystem is rapidly expanding. It driven by continuous innovation but accompanied increasingly sophisticated cybersecurity threats. Protecting IoT devices from these emerging vulnerabilities has become a critical priority. This study addresses the limitations existing threat detection methods, which often struggle with dynamic nature environments and growing complexity cyberattacks. To overcome challenges, novel hybrid architecture combining Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), Deep (DNN) proposed for accurate efficient detection. model’s performance evaluated using IoT-23 Edge-IIoTset datasets, encompass over ten distinct attack types. framework achieves remarkable 99% accuracy on both outperforming state-of-the-art solutions. Advanced optimization techniques, including model pruning quantization, are applied to enhance deployment efficiency in resource-constrained environments. results highlight robustness its adaptability diverse scenarios, address key prior approaches. research provides robust solution detection, establishing foundation advancing security addressing evolving landscape cyber threats while driving future innovations field.

Язык: Английский

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Enhancing cybersecurity using optimized anti-interference dynamic integral neural network-based intrusion detection system

Knowledge and Information Systems, Год журнала: 2025, Номер unknown

Опубликована: Март 13, 2025

Язык: Английский

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Detecting Cyber Threats With a Graph-Based NIDPS

Advances in logistics, operations, and management science book series, Год журнала: 2023, Номер unknown, С. 36 - 74

Опубликована: Дек. 29, 2023

This chapter explores the topic of a novel network-based intrusion detection system (NIDPS) that utilises concept graph theory to detect and prevent incoming threats. With technology progressing at rapid rate, number cyber threats will also increase accordingly. Thus, demand for better network security through NIDPS is needed protect data contained in networks. The primary objective this explore based four different aspects: collection, analysis engine, preventive action, reporting. Besides analysing existing NIDS technologies market, various research papers journals were explored. authors' solution covers basic structure an system, from collecting processing generating alerts reports. Data collection methods like packet-based, flow-based, log-based collections terms scale viability.

Язык: Английский

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DDoS attack detection in IoT environment using optimized Elman recurrent neural networks based on chaotic bacterial colony optimization

Cluster Computing, Год журнала: 2023, Номер 27(4), С. 4469 - 4490

Опубликована: Ноя. 27, 2023

Язык: Английский

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