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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SIM-FED: Secure IoT malware detection model with federated learning

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 116, P. 109139 - 109139

Published: March 30, 2024

Language: Английский

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Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 63584 - 63597

Published: Jan. 1, 2024

The Internet of Things (IoT) represents a swiftly expanding sector that is pivotal in driving the innovation today's smart services. However, inherent resource-constrained nature IoT nodes poses significant challenges embedding advanced algorithms for cybersecurity, leading to an escalation cyberattacks against these nodes. Contemporary research Intrusion Detection Systems (IDS) predominantly focuses on enhancing IDS performance through sophisticated algorithms, often overlooking their practical applicability. This paper introduces Deep-IDS, innovative and practically deployable Deep Learning (DL)-based IDS. It employs Long-Short-Term-Memory (LSTM) network comprising 64 LSTM units trained CIC-IDS2017 dataset. Its streamlined architecture renders Deep-IDS ideal candidate edge-server deployment, acting as guardian between Denial Service (DoS), Distributed (DDoS), Brute Force (BRF), Man-in-the-Middle (MITM), Replay (RP) Attacks. A distinctive aspect this trade-off analysis intrusion detection rate false alarm rate, facilitating real-time Deep-IDS. system demonstrates exemplary 96.8% overall classification accuracy 97.67%. Furthermore, achieves precision, recall, F1-scores 97.67%, 98.17%, 97.91%, respectively. On average, requires 1.49 seconds identify mitigate attempts, effectively blocking malicious traffic sources. remarkable efficacy, swift response time, design, novel defense strategy not only secure but also interconnected sub-networks, thereby positioning IoT-enhanced computer networks.

Language: Английский

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A Deep Learning-Based Framework for Strengthening Cybersecurity in Internet of Health Things (IoHT) Environments

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(11), P. 4729 - 4729

Published: May 30, 2024

The increasing use of IoHT devices in healthcare has brought about revolutionary advancements, but it also exposed some critical vulnerabilities, particularly cybersecurity. is characterized by interconnected medical sharing sensitive patient data, which amplifies the risk cyber threats. Therefore, ensuring data’s integrity, confidentiality, and availability essential. This study proposes a hybrid deep learning-based intrusion detection system that uses an Artificial Neural Network (ANN) with Bidirectional Long Short-Term Memory (BLSTM) Gated Recurrent Unit (GRU) architectures to address cybersecurity threats IoHT. model was tailored meet complex security demands rigorously tested using Electronic Control ECU-IoHT dataset. results are impressive, achieving 100% accuracy, precision, recall, F1-Score binary classifications maintaining exceptional performance multiclass scenarios. These findings demonstrate potential advanced AI methodologies safeguarding environments, providing high-fidelity while minimizing false positives.

Language: Английский

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IoT Intrusion Detection System Based on Machine Learning

Electronics, Journal Year: 2023, Volume and Issue: 12(20), P. 4289 - 4289

Published: Oct. 17, 2023

With the rapid development of Internet Things (IoT), number IoT devices is increasing dramatically, making it increasingly important to identify intrusions on these devices. Researchers are using machine learning techniques design effective intrusion detection systems. In this study, we propose a novel system that efficiently detects network anomalous traffic. To reduce feature dimensions data, employ binary grey wolf optimizer (BGWO) heuristic algorithm and recursive elimination (RFE) select most relevant subset for target variable. The synthetic minority oversampling technique (SMOTE) used oversample class mitigate impact data imbalance classification results. preprocessed then classified XGBoost, hyperparameters model optimized Bayesian optimization with tree-structured Parzen estimator (BO-TPE) achieve highest performance. validate effectiveness proposed method, conduct multiclass experiments five commonly datasets. results show our method outperforms state-of-the-art methods in four out It noteworthy achieves perfect accuracy, precision, recall, an F1 score 1.0 BoT-Iot WUSTL-IIOT-2021 datasets, further validating approach.

Language: Английский

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Class imbalance data handling with optimal deep learning-based intrusion detection in IoT environment

Soft Computing, Journal Year: 2024, Volume and Issue: 28(5), P. 4519 - 4529

Published: Feb. 5, 2024

Language: Английский

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Machine learning security and privacy: a review of threats and countermeasures

EURASIP Journal on Information Security, Journal Year: 2024, Volume and Issue: 2024(1)

Published: April 23, 2024

Abstract Machine learning has become prevalent in transforming diverse aspects of our daily lives through intelligent digital solutions. Advanced disease diagnosis, autonomous vehicular systems, and automated threat detection triage are some prominent use cases. Furthermore, the increasing machine critical national infrastructures such as smart grids, transport, natural resources makes it an attractive target for adversaries. The to systems is aggravated due ability mal-actors reverse engineer publicly available models, gaining insight into algorithms underpinning these models. Focusing on landscape we have conducted in-depth analysis critically examine security privacy threats factors involved developing adversarial attacks. Our highlighted that feature engineering, model architecture, targeted system knowledge crucial formulating one successful attack can lead other attacks; instance, poisoning attacks membership inference backdoor We also reviewed literature concerning methods techniques mitigate whilst identifying their limitations including data sanitization, training, differential privacy. Cleaning sanitizing datasets may challenges, underfitting affecting performance, whereas does not completely preserve model’s Leveraging surfaces mitigation techniques, identify potential research directions improve trustworthiness systems.

Language: Английский

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Harnessing Machine Learning Intelligence Against Cyber Threats

Advances in business strategy and competitive advantage book series, Journal Year: 2024, Volume and Issue: unknown, P. 319 - 352

Published: Aug. 28, 2024

The spread of cyberthreats in the digital age presents serious concerns to national security, stability economy, and personal privacy. Traditional security methods are unable keep up with increasing sophistication size cyberattacks. With facilitating quick identification mitigation cyberthreats, machine learning (ML) has revolutionary potential improve cybersecurity measures. But applying ML this field also brings important moral legal issues, particularly light international cybercrimes. This chapter comprehensively explores learning's dual nature cybersecurity, emphasizing both its advantages disadvantages. It talk about state cyber threats today, how is being incorporated into ramifications using investigations.

Language: Английский

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Detection and Classification of Novel Attacks and Anomaly in IoT Network using Rule based Deep Learning Model

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(8)

Published: Nov. 17, 2024

Language: Английский

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Developing Deep Learning-Based Network Intrusion Detection Systems (NIDS) for Iot Networks

Sustainable civil infrastructures, Journal Year: 2025, Volume and Issue: unknown, P. 1105 - 1113

Published: Jan. 1, 2025

Language: Английский

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