Deep learning based cross architecture internet of things malware detection and classification

Chaganti, Rajasekhar; Ravi, Vinayakumar; Pham, Tuan D.

doi:10.1016/j.cose.2022.102779

Cited by 56 publications

(12 citation statements)

References 23 publications

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“…The static analysis methods that use strings [ 7 , 13 , 14 , 15 , 16 , 17 ] and byte sequences [ 18 , 19 , 20 ] as features extract a large number of unique features, imposing a computationally intensive and time-consuming detection process. However, reducing the number of features through preprocessing discards valuable data.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

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

Zhao

Kuerban

2023

Sensors

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“…Deep Learning [22] and [26] Handle complex data-sets, and learn complex relationships between features.…”

Section: Techniquesmentioning

confidence: 99%

“…Researchers have proposed various methods to detect malware to secure IoT devices. Machine learning techniques have gained popularity for malware detection, as they can analyze large datasets of known malware and benign samples to identify patterns for detecting previously unseen malware [21,22]. Numerous studies have proposed and tested machine learning-based approaches, with some focusing on real-time detection and others targeting specific types of IoT malware [23].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Malware Detection for IoT Devices: Understanding the Evolving Threat Landscape and Strategies for Protection

Ali

MAQSOOD

Hou

et al. 2023

Preprint

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The Internet of Things (IoT) has experienced significant growth in recent years, with IoT connections surpassing those of traditional connected devices in the last few years. However, with this growth comes an increased risk of IoT security breaches as cybercriminals take advantage of lax security measures at the endpoint level. One of the main challenges in IoT security is the lack of proper protocols, policies, and procedures to protect these devices from malware and malicious software. This review article focuses on the recent advancements in machine learning-based malware detection techniques for IoT devices. We discuss the challenges of detecting malware in IoT devices, including the limited resources and processing power, as well as the diversity of device types and operating systems. We also review recent machine learning-based malware detection techniques for IoT devices, including deep learning, ensemble learning, and transfer learning, and evaluate their efficiency. This review aims to provide a comprehensive understanding of the current state-of-the-art machine learning-based malware detection techniques for IoT devices, highlighting the potential and limitations of these techniques and the role of analytics in future research directions.

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“… Obaidat et al (2022) proposed a CNN model using Java bytecode. Chaganti, Ravi & Pham (2022) developed a Bi-directional DL approach for classifying the IoT malware images. Bensaoud & Kalita (2022) employed Malimg dataset for evaluating the CNN model.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial intelligence-driven malware detection framework for internet of things environment

Alsubai

Dutta²,

Alnajim

et al. 2023

PeerJ Computer Science

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The Internet of Things (IoT) environment demands a malware detection (MD) framework for protecting sensitive data from unauthorized access. The study intends to develop an image-based MD framework. The authors apply image conversion and enhancement techniques to convert malware binaries into RGB images. You only look once (Yolo V7) is employed for extracting the key features from the malware images. Harris Hawks optimization is used to optimize the DenseNet161 model to classify images into malware and benign. IoT malware and Virusshare datasets are utilized to evaluate the proposed framework’s performance. The outcome reveals that the proposed framework outperforms the current MD framework. The framework generates the outcome at an accuracy and F1-score of 98.65 and 98.5 and 97.3 and 96.63 for IoT malware and Virusshare datasets, respectively. In addition, it achieves an area under the receiver operating characteristics and the precision-recall curve of 0.98 and 0.85 and 0.97 and 0.84 for IoT malware and Virusshare datasets, accordingly. The study’s outcome reveals that the proposed framework can be deployed in the IoT environment to protect the resources.

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Deep learning based cross architecture internet of things malware detection and classification

Cited by 56 publications

References 23 publications

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

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

Machine Learning-Based Malware Detection for IoT Devices: Understanding the Evolving Threat Landscape and Strategies for Protection

Artificial intelligence-driven malware detection framework for internet of things environment

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