Intelligent Mirai Malware Detection in IoT Devices

Palla, Tarun Ganesh; Tayeb, Shahab

doi:10.1109/aiiot52608.2021.9454215

Cited by 10 publications

(6 citation statements)

References 24 publications

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“…Not all malware types are covered in this research. A novel approach [21] ANN based on a machine learning algorithm is used to detect Mirai and Benign in IoT devices. The Mirai and Benign datasets are used in Matlab2018b implementation and training.…”

Section: Background Studymentioning

confidence: 99%

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Deep Neural Networks for Enhanced Security: Detecting Metamorphic Malware in IoT Devices

Habib,

Shirazi,

Aurangzeb

et al. 2024

IEEE Access

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Today Internet of Things (IoT) has become a key part of the modern world as it enables web-based IoT devices to collect, transfer, and analyze the data of individuals, companies, and industries. IoT provides numerous services and applications via a massive number of interconnected devices and has become an innovative attack vector for cyber-attacks and threats such as malware attacks that are currently regarded as serious dangers to the security of IoT devices and systems. Such threats are sufficient to infiltrate individual private information that inflicts harm to both the financial standing and reputation in an organization. In literature, researchers have used multiple machine learning and deep learning models to tackle this security threat, however, still accurate classification and detection of metamorphic malware in IoT devices remains a challenge. In this article, we used a deep learning model to accurately detect metamorphic malware in IoT devices. We have employed six models including (VGG16, InceptionV3, CNN, ResNet50, MobileNet, and Efficient NetB0 on Malimg publicly available malware image dataset. The Internet of Things (IoT) would benefit from having a method that could identify metamorphic malware. It isn't possible to rely on detection techniques that are fixed or signature-based. Throughout this research, a straightforward technique for carrying out dynamic analysis to comprehend the behavior of code is suggested. To determine if executables are malicious, it is necessary to first measure the behavior of executables and then utilize this information to make that determination. Additionally, the purpose of this study is to create a classifier that makes utilization of deep learning techniques to analyze complicated behavior reports. The obtained results depict that the proposed model achieves a promising accuracy of 99% and F1-score of 97% employed on the standard Malimg dataset as compared to other existing machine and deep learning models.

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Section: Background Studymentioning

confidence: 99%

“…In the fourth quarter of the year 2016, the Mirai [21] (IoT Botnet) malware attack has infected around 80 thousands to 2.5 million Internet-connected devices (computers) via a DDoS attack. Due to the widespread use of search engines, DDoS hackers may easily locate the new IoT devices more quickly over the Internet [10].…”

Section: Background Studymentioning

confidence: 99%

Deep Neural Networks for Enhanced Security: Detecting Metamorphic Malware in IoT Devices

Habib,

Shirazi,

Aurangzeb

et al. 2024

IEEE Access

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“…Then they applied three classification algorithms to the traffic sessions rather than individual packets because per-packet classification is computationally much more costly and does not yield any significant benefits. Palla et al [ 46 ] detected Mirai viruses in IoT devices using neural networks to highlight packets in network traffic. Their scheme used Artificial Neural Networks (ANN) to compute the accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, we compare the performance of MDABP against the existing malware classification methods. Since existing dynamic cross-architecture analysis methods that use a single set of system calls as features are not available, we compare MDABP with dynamic analysis methods based on the characteristics of network traffic [ 45 , 46 , 47 ]. Specifically, Kumar et al [ 45 ] proposed a distributed modularization scheme based on a network traffic database that involves three machine learning models as classification models.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…After experimental validation, the authors demonstrated that the KNN model has the highest accuracy of 94.44% and a 100% recall rate. Palla et al [ 46 ] detected Mirai viruses in IoT devices using an artificial neural network (ANN) to highlight packets in network traffic. Their method was experimentally verified to attain an accuracy rate of 92.9% and a recall rate of 99%.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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MDABP: A Novel Approach to Detect Cross-Architecture IoT Malware Based on PaaS

Zhao

Kuerban

2023

Sensors

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With the development of internet technology, the Internet of Things (IoT) has been widely used in several aspects of human life. However, IoT devices are becoming more vulnerable to malware attacks due to their limited computational resources and the manufacturers’ inability to update the firmware on time. As IoT devices are increasing rapidly, their security must classify malicious software accurately; however, current IoT malware classification methods cannot detect cross-architecture IoT malware using system calls in a particular operating system as the only class of dynamic features. To address these issues, this paper proposes an IoT malware detection approach based on PaaS (Platform as a Service), which detects cross-architecture IoT malware by intercepting system calls generated by virtual machines in the host operating system acting as dynamic features and using the K Nearest Neighbors (KNN) classification model. A comprehensive evaluation using a 1719 sample dataset containing ARM and X86-32 architectures demonstrated that MDABP achieves 97.18% average accuracy and a 99.01% recall rate in detecting samples in an Executable and Linkable Format (ELF). Compared with the best cross-architecture detection method that uses network traffic as a unique type of dynamic feature with an accuracy of 94.5%, practical results reveal that our method uses fewer features and has higher accuracy.

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