A Novel Deep Learning-Based Intrusion Detection System for IoT Networks

Computers, Journal Year: 2023, Volume and Issue: 12(2), P. 34 - 34

Published: Feb. 5, 2023

The impressive growth rate of the Internet Things (IoT) has drawn attention cybercriminals more than ever. growing number cyber-attacks on IoT devices and intermediate communication media backs claim. Attacks IoT, if they remain undetected for an extended period, cause severe service interruption resulting in financial loss. It also imposes threat identity protection. Detecting intrusion real-time is essential to make IoT-enabled services reliable, secure, profitable. This paper presents a novel Deep Learning (DL)-based detection system devices. intelligent uses four-layer deep Fully Connected (FC) network architecture detect malicious traffic that may initiate attacks connected proposed been developed as protocol-independent reduce deployment complexities. demonstrates reliable performance simulated real intrusions during experimental analysis. detects Blackhole, Distributed Denial Service, Opportunistic Sinkhole, Workhole with average accuracy 93.74%. system’s precision, recall, F1-score are 93.71%, 93.82%, 93.47%, respectively, average. innovative learning-based IDS maintains 93.21% which satisfactory improving security networks.

Language: Английский

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Intrusion detection system for large-scale IoT NetFlow networks using machine learning with modified Arithmetic Optimization Algorithm

Internet of Things, Journal Year: 2023, Volume and Issue: 22, P. 100819 - 100819

Published: May 16, 2023

Language: Английский

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Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks

Sensors, Journal Year: 2023, Volume and Issue: 23(9), P. 4430 - 4430

Published: April 30, 2023

Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is critical problem cyber security. In recent years, metaheuristic optimization algorithms deep learning techniques have been applied to IDS improve their accuracy efficiency. Generally, can be used boost the performance of models. Deep methods, such as convolutional neural networks, also ability detect classify intrusions. this paper, we propose new model based on combination methods. First, feature extraction method CNNs developed. Then, selection modified version Growth Optimizer (GO), called MGO. We use Whale Optimization Algorithm (WOA) search process GO. Extensive evaluation comparisons conducted assess quality suggested using public datasets cloud Internet Things (IoT) environments. The shown promising results previously unknown attacks with high rates. MGO performed better than several previous methods all experimental comparisons.

Language: Английский

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Optimized Machine Learning-Based Intrusion Detection System for Fog and Edge Computing Environment

Electronics, Journal Year: 2022, Volume and Issue: 11(19), P. 3007 - 3007

Published: Sept. 22, 2022

As a new paradigm, fog computing (FC) has several characteristics that set it apart from the cloud (CC) environment. Fog nodes and edge (EC) hosts have limited resources, exposing them to cyberattacks while processing large streams sending directly cloud. Intrusion detection systems (IDS) can be used protect against in FC EC environments, large-dimensional features networking data make massive amount of difficult, causing lower intrusion efficiency. Feature selection is typically alleviate curse dimensionality no discernible effect on classification outcomes. This first study present an Effective Seeker Optimization model conjunction with Machine Learning-Enabled Detection System (ESOML-IDS) for environments. The ESOML-IDS primarily designs ESO-based feature (FS) approach choose optimal subset identify occurrence intrusions We also applied comprehensive learning particle swarm optimization (CLPSO) Denoising Autoencoder (DAE) intrusions. development ESO algorithm DAE parameter results improved efficiency effectiveness. experimental demonstrated outcomes over recent approaches.

Language: Английский

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Improved discrete salp swarm algorithm using exploration and exploitation techniques for feature selection in intrusion detection systems

The Journal of Supercomputing, Journal Year: 2023, Volume and Issue: 79(18), P. 21265 - 21309

Published: June 19, 2023

Language: Английский

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Intrusion detection in internet of things using improved binary golden jackal optimization algorithm and LSTM

Cluster Computing, Journal Year: 2023, Volume and Issue: 27(3), P. 2673 - 2690

Published: July 25, 2023

Language: Английский

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Novel memetic of beluga whale optimization with self-adaptive exploration–exploitation balance for global optimization and engineering problems

Soft Computing, Journal Year: 2023, Volume and Issue: 27(19), P. 13951 - 13989

Published: June 6, 2023

Abstract A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous deficiencies that has to be strengthened. Premature convergence a disparity between exploitation exploration are some these challenges. Furthermore, the absence transfer parameter in typical when moving from phase direct impact on algorithm’s performance. This work proposes novel modified (mBWO) incorporates an elite evolution strategy, randomization control factor, transition factor exploitation. The strategy preserves top candidates for subsequent generation so helps generate effective solutions with meaningful differences them prevent settling into local maxima. random mutation improves search offers more crucial ability prevents stagnation optimum. mBWO controlling algorithm away optima region during BWO. Gaussian (GM) acts initial position vector produce new location. Because this, majority altered operators scattered close original position, which is comparable carrying out small region. method can now depart optimal zone because this modification, also increases optimizer’s precision traverses space using placements, lead zone. Transition (TF) used make transitions agents gradually concerning amount time required. undergoes comparison 10 additional optimizers 29 CEC2017 functions. Eight engineering problems addressed by mBWO, involving design welded beams, three-bar trusses, tension/compression springs, speed reducers, best industrial refrigeration systems, pressure vessel challenges, cantilever beam designs, multi-product batch plants. In both constrained unconstrained settings, results preformed superior those other methods.

Language: Английский

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Intrusion Detection using hybridized Meta-heuristic techniques with Weighted XGBoost Classifier

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 232, P. 120596 - 120596

Published: June 5, 2023

Language: Английский

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A Novel IDS with a Dynamic Access Control Algorithm to Detect and Defend Intrusion at IoT Nodes

Sensors, Journal Year: 2024, Volume and Issue: 24(7), P. 2188 - 2188

Published: March 29, 2024

The Internet of Things (IoT) is the underlying technology that has enabled connecting daily apparatus to and enjoying facilities smart services. IoT marketing experiencing an impressive 16.7% growth rate a nearly USD 300.3 billion market. These eye-catching figures have made it attractive playground for cybercriminals. devices are built using resource-constrained architecture offer compact sizes competitive prices. As result, integrating sophisticated cybersecurity features beyond scope computational capabilities IoT. All these contributed surge in intrusion. This paper presents LSTM-based Intrusion Detection System (IDS) with Dynamic Access Control (DAC) algorithm not only detects but also defends against novel approach achieved 97.16% validation accuracy. Unlike most IDSs, model proposed IDS been selected optimized through mathematical analysis. Additionally, boasts ability identify wider range threats (14 be exact) compared other solutions, translating enhanced security. Furthermore, fine-tuned strike balance between accurately flagging minimizing false alarms. Its performance metrics (precision, recall, F1 score all hovering around 97%) showcase potential this innovative elevate detection rate, exceeding 98%. high accuracy instills confidence its reliability. lightning-fast response time, averaging under 1.2 s, positions among fastest intrusion systems available.

Language: Английский

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PSO-ACO-based bi-phase lightweight intrusion detection system combined with GA optimized ensemble classifiers

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(10), P. 14835 - 14890

Published: Aug. 6, 2024

Language: Английский

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