Enhancing Smart IoT Malware Detection: A GhostNet-based Hybrid Approach

Almazroi, Abdulwahab Ali; Ayub, Nasir

doi:10.3390/systems11110547

Cited by 3 publications

(1 citation statement)

References 46 publications

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“…The Naïve Bayes method by 38 ensures accuracy and resilience without imposing specific restrictions. A statistical study by 39 yields excellent results but operates at peak efficiency solely on Windows OS. Li et al 40 , employing CNN with minimal processing expense, acknowledges its unsuitability for complex designs.…”

Section: Related Workmentioning

confidence: 99%

Deep learning hybridization for improved malware detection in smart Internet of Things

Almazroi,

Ayub

2024

Sci Rep

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The rapid expansion of AI-enabled Internet of Things (IoT) devices presents significant security challenges, impacting both privacy and organizational resources. The dynamic increase in big data generated by IoT devices poses a persistent problem, particularly in making decisions based on the continuously growing data. To address this challenge in a dynamic environment, this study introduces a specialized BERT-based Feed Forward Neural Network Framework (BEFNet) designed for IoT scenarios. In this evaluation, a novel framework with distinct modules is employed for a thorough analysis of 8 datasets, each representing a different type of malware. BEFSONet is optimized using the Spotted Hyena Optimizer (SO), highlighting its adaptability to diverse shapes of malware data. Thorough exploratory analyses and comparative evaluations underscore BEFSONet’s exceptional performance metrics, achieving 97.99% accuracy, 97.96 Matthews Correlation Coefficient, 97% F1-Score, 98.37% Area under the ROC Curve(AUC-ROC), and 95.89 Cohen’s Kappa. This research positions BEFSONet as a robust defense mechanism in the era of IoT security, offering an effective solution to evolving challenges in dynamic decision-making environments.

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Section: Related Workmentioning

confidence: 99%

Deep learning hybridization for improved malware detection in smart Internet of Things

Almazroi,

Ayub

2024

Sci Rep

Self Cite

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An intelligent ransomware attack detection and classification using dual vision transformer with Mantis Search Split Attention Network

K.,

K.B.

2024

Computers and Electrical Engineering

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A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things

Ahmad,

Wan,

Ahmad

et al. 2024

Mathematics

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The proliferation of Internet of Things (IoT) devices and their integration into critical infrastructure and business operations has rendered them susceptible to malware and cyber-attacks. Such malware presents a threat to the availability and reliability of IoT devices, and a failure to address it can have far-reaching impacts. Due to the limited resources of IoT devices, traditional rule-based detection systems are often ineffective against sophisticated attackers. This paper addressed these issues by designing a new framework that uses a machine learning (ML) algorithm for the detection of malware. Additionally, it also employed sequential detection architecture and evaluated eight malware datasets. The design framework is lightweight and effective in data processing and feature selection algorithms. Moreover, this work proposed a classification model that utilizes one support vector machine (SVM) algorithm and is individually tuned with three different optimization algorithms. The employed optimization algorithms are Nuclear Reactor Optimization (NRO), Artificial Rabbits Optimization (ARO), and Particle Swarm Optimization (PSO). These algorithms are used to explore a diverse search space and ensure robustness in optimizing the SVM for malware detection. After extensive simulations, our proposed framework achieved the desired accuracy among eleven existing ML algorithms and three proposed ensemblers (i.e., NRO_SVM, ARO_SVM, and PSO_SVM). Among all algorithms, NRO_SVM outperforms the others with an accuracy rate of 97.8%, an F1 score of 97%, and a recall of 99%, and has fewer false positives and false negatives. In addition, our model successfully identified and prevented malware-induced attacks with a high probability of recognizing new evolving threats.

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Enhancing Smart IoT Malware Detection: A GhostNet-based Hybrid Approach

Cited by 3 publications

References 46 publications

Deep learning hybridization for improved malware detection in smart Internet of Things

Deep learning hybridization for improved malware detection in smart Internet of Things

An intelligent ransomware attack detection and classification using dual vision transformer with Mantis Search Split Attention Network

A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things

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