An Intrusion Detection System Using BoT-IoT

Alosaimi, Shema; Almutairi, Saad

doi:10.3390/app13095427

Cited by 23 publications

(11 citation statements)

References 29 publications

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“…Therefore, the BHMML-CADC technique is an effective tool for the automated cyber attack detection process. In Table 4 and Figure 14, detailed classification results of the BHMML-CADC technique are clearly illustrated [33][34][35]. The experimental values highlight that the ensemble learning model achieves worse results.…”

Section: Or Peer Review 13 Of 21mentioning

confidence: 99%

Blockchain-Assisted Machine Learning with Hybrid Metaheuristics-Empowered Cyber Attack Detection and Classification Model

Albakri,

Alabdullah,

Alhayan

2023

Sustainability

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Cyber attack detection is the process of detecting and responding to malicious or unauthorized activities in networks, computer systems, and digital environments. The objective is to identify these attacks early, safeguard sensitive data, and minimize the potential damage. An intrusion detection system (IDS) is a cybersecurity tool mainly designed to monitor system activities or network traffic to detect and respond to malicious or suspicious behaviors that may indicate a cyber attack. IDSs that use machine learning (ML) and deep learning (DL) have played a pivotal role in helping organizations identify and respond to security risks in a prompt manner. ML and DL techniques can analyze large amounts of information and detect patterns that may indicate the presence of malicious or cyber attack activities. Therefore, this study focuses on the design of blockchain-assisted hybrid metaheuristics with a machine learning-based cyber attack detection and classification (BHMML-CADC) algorithm. The BHMML-CADC method focuses on the accurate recognition and classification of cyber attacks. Moreover, the BHMML-CADC technique applies Ethereum BC for attack detection. In addition, a hybrid enhanced glowworm swarm optimization (HEGSO) system is utilized for feature selection (FS). Moreover, cyber attacks can be identified with the design of a quasi-recurrent neural network (QRNN) model. Finally, hunter–prey optimization (HPO) algorithm is used for the optimal selection of the QRNN parameters. The experimental outcomes of the BHMML-CADC system were validated on the benchmark BoT-IoT dataset. The wide-ranging simulation analysis illustrates the superior performance of the BHMML-CADC method over other algorithms, with a maximum accuracy of 99.74%.

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Section: Or Peer Review 13 Of 21mentioning

confidence: 99%

Blockchain-Assisted Machine Learning with Hybrid Metaheuristics-Empowered Cyber Attack Detection and Classification Model

Albakri,

Alabdullah,

Alhayan

2023

Sustainability

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“…The study of S. Alosaimi et al [18] demonstrates an appreciable multi-layer security system using DL for IoT networks. However, it is confined to different types of DDoS attacks only.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This paper uses the sliding window approach to produce sequential data, which resembles the network packet stream the IoT nodes handle [36]. It has been done using the mathematical principle defined in Equation (18).…”

Section: Sequence Generationmentioning

confidence: 99%

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

Alazab,

Awajan,

Alazzam

et al. 2024

Sensors

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

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“…It analyzes and scans network traffic to find patterns and signs that denote an effective assault. Conventional types of NIDSs focus on matching incoming traffic signs to a predetermined list of renowned malicious signs (Albulayhi et al, 2021; Alosaimi & Almutairi, 2023; Bhati et al, 2021). This generally results in a high detection rate (DR) for renowned assaults (Jasper Gnana Chandran et al, 2023; Mafarja et al, 2020; Rajesh, Shajin, & Kodeeswara Kumaran, 2022).…”

Section: Introductionmentioning

confidence: 99%

Intrusion detection system using metaheuristic fireworks optimization based feature selection with deep learning on Internet of Things environment

Jayasankar,

Kiruba Buri,

Maheswaravenkatesh

2023

Journal of Forecasting

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Internet of Things (IoT), cloud computing, and other significant advancements in communication have created new security challenges. Due to these advancements and the ineffectiveness of the current security measures, cyber‐attacks are also increasing quickly. Recently, several artificial intelligence (AI)–based solutions have been presented for various secure applications, such as intrusion detection. This article proposes an intrusion detection system using dynamic search fireworks optimization–based feature selection with optimal deep recurrent neural network (DFWAFS‐ODRNN) model in IoT environment. The presented DFWAFS‐ODRNN model follows a two‐stage process, namely, feature selection and intrusion classification. In the first phase, the DFWAFS‐ODRNN model elects an optimal subset of features using the dynamic search fireworks optimization algorithm (DFWAFS) technique. Next, in the second stage, the intrusions are identified and categorized using the DRNN model. At last, the hyperparameters of the DRNN are optimally chosen by the Nadam optimizer. A detailed simulation analysis of the DFWAFS‐ODRNN model is validated on benchmark intrusion detection system (IDS) dataset, and the outcomes show the efficacy of intrusion detection. The proposed model efficiently detects the intrusion detection with an accuracy of 96.11%.

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An Intrusion Detection System Using BoT-IoT

Cited by 23 publications

References 29 publications

Blockchain-Assisted Machine Learning with Hybrid Metaheuristics-Empowered Cyber Attack Detection and Classification Model

Blockchain-Assisted Machine Learning with Hybrid Metaheuristics-Empowered Cyber Attack Detection and Classification Model

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

Intrusion detection system using metaheuristic fireworks optimization based feature selection with deep learning on Internet of Things environment

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