Lightweight Internet of Things Botnet Detection Using One-Class Classification

Malik, Kainat; Rehman, Faisal; Maqsood, Tahir; Mustafa, Saad; Khalid, Osman; Akhunzada, Adnan

doi:10.3390/s22103646

Cited by 12 publications

(4 citation statements)

References 29 publications

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“…As a further advancement, Malik et al propose a solution with one-class KNN [ 131 ] as the primary one-class classifier, which has shown the best performance among one-class classifiers, achieving an F1-score of 98% to 99% on different IoT datasets. The model in the paper is trained on real-world IoT datasets collected from a consumer IoT gadget network, include traffic generated by three types of IoT botnets, Mirai, Bash lite, and Torii, capturing normal and malware traffic.…”

Section: Iot Botnet Detectionmentioning

confidence: 99%

Systematic Literature Review of IoT Botnet DDOS Attacks and Evaluation of Detection Techniques

Gelgi,

Guan,

Arunachala

et al. 2024

Sensors

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Internet of Things (IoT) technology has become an inevitable part of our daily lives. With the increase in usage of IoT Devices, manufacturers continuously develop IoT technology. However, the security of IoT devices is left behind in those developments due to cost, size, and computational power limitations. Since these IoT devices are connected to the Internet and have low security levels, one of the main risks of these devices is being compromised by malicious malware and becoming part of IoT botnets. IoT botnets are used for launching different types of large-scale attacks including Distributed Denial-of-Service (DDoS) attacks. These attacks are continuously evolving, and researchers have conducted numerous analyses and studies in this area to narrow security vulnerabilities. This paper systematically reviews the prominent literature on IoT botnet DDoS attacks and detection techniques. Architecture IoT botnet DDoS attacks, evaluations of those attacks, and systematically categorized detection techniques are discussed in detail. The paper presents current threats and detection techniques, and some open research questions are recommended for future studies in this field.

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Section: Iot Botnet Detectionmentioning

confidence: 99%

Systematic Literature Review of IoT Botnet DDOS Attacks and Evaluation of Detection Techniques

Gelgi,

Guan,

Arunachala

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Especially, SMOTE produces a extra few instances to attain class stability, although DRNN learns hierarchical feature representation in the stable network traffic data to execute differentiated categorization. In [13], one class classifier-based ML solution is introduced for the recognition of IoT botnets from heterogeneous surroundings. This technique is a lightweight approach that employs choosing the optimal feature using famous filter and wrapper techniques to select features.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning

Maghrabi,

Shabanah,

Althaqafi

et al. 2024

IEEE Access

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Nowadays, the Internet of Things (IoT) has become a rapid development; it can be employed by cyber threats in IoT devices. A correct system to recognize malicious attacks at IoT platforms became of major importance to minimize security threats in IoT devices. Botnet attacks have more severe and common attacks and it is threaten IoT devices. These threats interrupt IoT alteration by interrupting networks and services for IoT devices. Several existing methods present themselves to determine unknown patterns in IoT networks for improving security. Recent analysis presents DL and ML methods for classifying and detecting botnet attacks from the IoT environment. Consequently, this paper develops a Bald Eagle Search Optimization with a Hybrid Deep Learning based botnet detection (BESO-HDLBD) algorithm in an IoT platform. The presented BESO-HDLBD approach aims to resolve the security issue by identifying the botnets in the IoT environment. To reduce the high dimensionality problem, the BESO-HDLBD method uses the BESO system for the feature selection process. For botnet detection purposes, the BESO-HDLBD algorithm uses HDL, which is an integration of convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and attention concept. The desire for the HDL technique in botnet detection utilises the intricate nature of botnet attacks that frequently contain difficult and developing patterns. Combining CNNs permits for effectual feature extraction from spatial data, BiLSTM networks capture temporal dependencies, and attention mechanisms improve the model's capability to concentrate on fundamental patterns. The selection of hyperparameters of the HDL approach takes place using the dragonfly algorithm (DFA). The experimental analysis of the BESO-HDLBD system could be examined under a benchmark botnet dataset. The obtained outcome infers a better outcome of the BESO-HDLBD technique compared to the recent detection system with respect to distinct estimation measures.

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“…Malik et al [19] developed a one-class classifier-based ML technique for identifying IoT botnets in a heterogeneous condition. This introduced a one-class classifier that relies on one-class KNN, to identify the IoT botnets at the earlier phase with higher accuracy.…”

Section: Related Workmentioning

confidence: 99%

Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things Environment

Alrowais,

Eltahir,

Aljameel

et al. 2023

IEEE Access

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Nowadays, there are ample amounts of Internet of Things (IoT) devices interconnected to the networks, and with technological improvement, cyberattacks and security threads, for example, botnets, are rapidly evolving and emerging with high-risk attacks. A botnet is a network of compromised devices that are controlled by cyber attackers, frequently employed to perform different cyberattacks. Such attack disrupts IoT evolution by disrupting services and networks for IoT devices. Detecting botnets in an IoT environment includes finding abnormal patterns or behaviors that might indicate the existence of these malicious networks. Several researchers have proposed deep learning (DL) and machine learning (ML) approaches for identifying and categorizing botnet attacks in the IoT platform. Therefore, this study introduces a Botnet Detection using the Chaotic Binary Pelican Optimization Algorithm with Deep Learning (BNT-CBPOADL) technique in the IoT environment. The main aim of the BNT-CBPOADL method lies in the correct detection and categorization of botnet attacks in the IoT environment. In the BNT-CBPOADL method, Z-score normalization is applied for pre-processing. Besides, the CBPOA technique is derived for feature selection. The convolutional variational autoencoder (CVAE) method is applied for botnet detection. At last, the arithmetical optimization algorithm (AOA) is employed for the optimal hyperparameter tuning of the CVAE algorithm. The experimental valuation of the BNT-CBPOADL technique is tested on a Bot-IoT database. The experimentation outcomes inferred the supremacy of the BNT-CBPOADL method over other existing techniques with maximum accuracy of 99.50%.

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Lightweight Internet of Things Botnet Detection Using One-Class Classification

Cited by 12 publications

References 29 publications

Systematic Literature Review of IoT Botnet DDOS Attacks and Evaluation of Detection Techniques

Systematic Literature Review of IoT Botnet DDOS Attacks and Evaluation of Detection Techniques

Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning

Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things Environment

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