WITHDRAWN: A deep-RNN and meta-heuristic feature selection approach for IoT malware detection

Radhakrishnan, G.; Srinivasan, K.; Maheswaran, S.; Sundaram, K. Mohana; Palanikkumar, D.; Vidyarthi, Abhay

doi:10.1016/j.matpr.2021.01.207

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…The proposed MPGA‐DCKCNN techniques obtain higher classification performance when compared to the other techniques as shown in Table 9 due to its ability to accurately discriminate between the attack and normal classes. The performance of the deep RNN and metaheuristic feature selection 33 and KVM inspector 34 is also very high when compared to the other techniques. Every classifier offers an above satisfactory performance but they are not efficient when identifying a unique attack.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

“…In the comparative analysis section, the proposed methodology is compared with state‐of‐art techniques such as VMGuard, 9 RNN, 32 deep RNN and metaheuristic feature selection, 33 and KVM inspector 34 . These techniques also include cloud‐based IDSs that take account of the statistical meta‐features.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

“…Our proposed MPGA‐DCKCNN architecture is compared with different state‐of‐art techniques such as IDELM, 20 RF‐SSO, 21 deep RNN and MFS, 33 and VM‐AMD 12 in terms of different performance metrics such as detection rate, FAR, FRPR, and detection time. The results obtained for this comparison using the UNSW‐NB15 dataset are presented in Table 10 and their corresponding graphs are provided in Figures 5 to 7.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

“…The model showed higher detection performance for Conficker (zero‐day malware). Radhakrishnan et al 33 presented a deep RNN and metaheuristic feature selection for malware detection in the Internet of Things (IoT). The method is tested using 271 normal and 282 malware samples using the k ‐fold cross‐validation technique which yielded an accuracy of 99.08%.…”

Section: Review Of Related Workmentioning

confidence: 99%

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Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

Alajlan

Almasri

2022

Trans Emerging Tel Tech

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Cloud computing is one example of a technological revolution that has drastically altered the way traditional commercial operations are conducted. Because cloud platforms are fundamentally revolutionizing the area of computing, they must provide safe transmission and storage of user data by assuring the confidentiality, integrity, and availability of the data. As a result, establishing a secure cloud computing framework is crucial, and security cannot only rely on the user's credentials being verified. To overcome this problem, this article presents a multi‐population genetic algorithm optimized dynamic kernel convolutional neural network to analyze the user's behavior to identify their malicious intent. This article describes a two‐stage malicious detection and prediction scheme. The proposed system's efficiency is measured by its ability to discriminate between nine different types of attacks in the UNSW‐NB15 dataset (abnormal behavior exhibited by users). The average true positive rate, false positive rate, precision, and F‐measure for the nine attack classes were 99.22%, 99.11%, 99.11%, and 99.22%, respectively, when evaluated using the UNSW‐NB15 dataset. The experimental results demonstrate that this technique is effective in detecting malicious behaviors in the cloud environment with higher accuracy.

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Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

Alajlan

Almasri

2022

Trans Emerging Tel Tech

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“…Radhakrishnan et al 44 proposed a LSTM technique to detect IoT malware. First and foremost, all data gathered from VirusTotal Website and their Op‐Code were extracted through a Linux bash code.…”

Section: Organization Of Iot Threat Detection Techniquesmentioning

confidence: 99%

Artificial intelligence empowered threat detection in the Internet of Things: A systematic review

Soltani

Rahmani

Bohlouli

et al. 2022

Concurrency and Computation

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Summary Internet of Things (IoT) is a new phenomenon that proposes novel business opportunities. IoT allows the world to be programmable and might provide several benefits for organizations. Based on the IoT survey, cyber‐security issues are among the most extensive and complicated challenges faced by IoT devices. Threat detection is considered a preventive measure against malware threats, ransomware, and attacks, which become more serious each year because of the dramatic rise in malware attacks. This article investigates threat detection techniques that fall into three categories: malware detection, attack detection, and ransomware detection, published from 2017 to August 2021. We examine solutions, techniques, features, classifiers, and tools proposed by IoT researchers. Some questions are proposed, and answering the questions may help the researchers suggest a more efficient solution in future works. Furthermore, the achievement and disadvantages of each study are discussed. Finally, based on the reviewed studies, some open challenges and practical measures to future directions are suggested, worth further studying and researching threat detection techniques in the IoT.

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A multi-constraint transfer approach with additional auxiliary domains for IoT intrusion detection under unbalanced samples distribution

Liu,

Ma,

Guo

2023

Appl Intell

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WITHDRAWN: A deep-RNN and meta-heuristic feature selection approach for IoT malware detection

Cited by 11 publications

References 24 publications

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

Artificial intelligence empowered threat detection in the Internet of Things: A systematic review

A multi-constraint transfer approach with additional auxiliary domains for IoT intrusion detection under unbalanced samples distribution

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