Anomaly-based intrusion detection system for IoT application

Discover Internet of Things, Journal Year: 2023, Volume and Issue: 3(1)

Published: May 30, 2023

Abstract Internet-of-Things (IoT) connects various physical objects through the Internet and it has a wide application, such as in transportation, military, healthcare, agriculture, many more. Those applications are increasingly popular because they address real-time problems. In contrast, use of transmission communication protocols raised serious security concerns for IoT devices, traditional methods signature rule-based inefficient securing these devices. Hence, identifying network traffic behavior mitigating cyber attacks important to provide guaranteed security. Therefore, we develop an Intrusion Detection System (IDS) based on deep learning model called Pearson-Correlation Coefficient - Convolutional Neural Networks (PCC-CNN) detect anomalies. The PCC-CNN combines features obtained from linear-based extractions followed by Network. It performs binary classification anomaly detection also multiclass types attacks. is evaluated three publicly available datasets: NSL-KDD, CICIDS-2017, IOTID20. We first train test five different (Logistic Regression, Linear Discriminant Analysis, K Nearest Neighbour, Classification Regression Tree,& Support Vector Machine) PCC-based Machine Learning models evaluate performance. achieve best similar accuracy KNN CART 98%, 99%, respectively, datasets. On other hand, promising performance with better 99.89% low misclassification rate 0.001 our proposed model. integrated promising, (or False alarm rate) 0.02, 0.00 Binary Multiclass intrusion classifiers. Finally, compare discuss comparison PCC-ML models. Our Deep (DL)-based IDS outperforms methods.

Language: Английский

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Enhancing IoT network security through deep learning-powered Intrusion Detection System

Internet of Things, Journal Year: 2023, Volume and Issue: 24, P. 100936 - 100936

Published: Sept. 13, 2023

The rapid growth of the Internet Things (IoT) has brought about a global concern for security interconnected devices and networks. This necessitates use efficient Intrusion Detection System (IDS) to mitigate cyber threats. Deep learning (DL) techniques provides promising approach effectively detect irregularities in network traffic, enhancing IoT reducing In this paper, DL-based IDS is proposed using Feed Forward Neural Networks (FFNN), Long Short-Term Memory (LSTM), Random (RandNN) protect networks from cyberattacks. Each DL model its potential benefit as reported paper. For example, FFNN can handle complex traffic patterns, while LSTM good capturing long-term dependencies present traffic. With random connections flexible dynamics, RandNN uses data ability adapt learn data. These algorithms boost cybersecurity by enabling defense mechanisms against challenging threats ensuring sensitive expand. technique exhibits superior performance when compared with current state-of-the-art DL-IDS CIC-IoT22 dataset. An accuracy 99.93 % achieved model, 99.85 96.42 detecting intrusion. Moreover, models have enhance intrusion detection generating swift responses problems

Language: Английский

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Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

Mathematics, Journal Year: 2024, Volume and Issue: 12(4), P. 571 - 571

Published: Feb. 14, 2024

In the evolving landscape of Internet Things (IoT) and Industrial IoT (IIoT) security, novel efficient intrusion detection systems (IDSs) are paramount. this article, we present a groundbreaking approach to for IoT-based electric vehicle charging stations (EVCS), integrating robust capabilities convolutional neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU) models. The proposed framework leverages comprehensive real-world cybersecurity dataset, specifically tailored IIoT applications, address intricate challenges faced by EVCS. We conducted extensive testing in both binary multiclass scenarios. results remarkable, demonstrating perfect 100% accuracy classification, an impressive 97.44% six-class 96.90% fifteen-class setting new benchmarks field. These achievements underscore efficacy CNN-LSTM-GRU ensemble architecture creating resilient adaptive IDS infrastructures. algorithm, accessible via GitHub, represents significant stride fortifying EVCS against diverse array threats.

Language: Английский

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A deep learning-based intrusion detection approach for mobile Ad-hoc network

Soft Computing, Journal Year: 2023, Volume and Issue: 27(14), P. 9425 - 9439

Published: May 21, 2023

Language: Английский

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Explainable AI for Cybersecurity

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 31 - 97

Published: Jan. 18, 2024

In recent years, the utilization of AI in field cybersecurity has become more widespread. Black-box models pose a significant challenge terms interpretability and transparency, which is one major drawbacks AI-based systems. This chapter explores explainable (XAI) techniques as solution to these challenges discusses their application cybersecurity. The begins with an explanation cybersecurity, including types commonly utilized, such DL, ML, NLP, applications intrusion detection, malware analysis, vulnerability assessment. then highlights black-box AI, difficulty identifying resolving errors, lack inability understand decision-making process. delves into XAI for solutions, interpretable machine-learning models, rule-based systems, model techniques.

Language: Английский

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The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 127440 - 127459

Published: Jan. 1, 2024

Language: Английский

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Designing a modified feature aggregation model with hybrid sampling techniques for network intrusion detection

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(5), P. 5913 - 5931

Published: Feb. 17, 2024

Language: Английский

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An Enhanced LSTM Approach for Detecting IoT-Based DDoS Attacks Using Honeypot Data

International Journal of Computational Intelligence Systems, Journal Year: 2025, Volume and Issue: 18(1)

Published: Feb. 5, 2025

Language: Английский

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

Computers, Journal Year: 2025, Volume and Issue: 14(2), P. 61 - 61

Published: Feb. 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.

Language: Английский

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Design of an Intrusion Detection Model for IoT-Enabled Smart Home

IEEE Access, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 1

Published: Jan. 1, 2023

Machine learning (ML) provides effective solutions to develop efficient intrusion detection system (IDS) for various environments. In the present paper, a diversified study of ensemble machine algorithms has been carried out propose design an and time-efficient IDS Internet Things (IoT) enabled environment. this data captured from network traffic real-time sensors IoT-enabled smart environment analyzed classify predict types attacks. The performance Logistic Regression, Random Forest, Extreme Gradient Boosting, Light Boosting classifiers have benchmarked using open-source largely imbalanced dataset 'DS2OS' that consists 'normal' 'anomalous' traffic. An model "LGB-IDS" proposed LGBM library ML after validating its superiority over other techniques on basis majority voting. is suitably validated certain metrics such as train test accuracy, time efficiency, error-rate, true-positive rate (TPR), false-negative (FNR). experimental results reveal XGB almost equal but efficiency much better than RF, classifiers. main objective paper with high reduced false alarm rate. show achieves accuracy 99.92% comes be higher prevalent algorithms-based models. threat greater 90% less 100%. Time complexity also very low compared algorithms.

Language: Английский

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