A Cross-Platform Study on Emerging Malicious Programs Targeting IoT Devices

Ban, Tao; Isawa, Ryoichi; Huang, Shin‐Ying; Yoshioka, Katsunari; Inoue, Daisuke

doi:10.1587/transinf.2018ofl0007

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Combining the Internet of Things (IoT) concept with industrial process management have reshaped the modern industry with great automation and tremendous revenue creation. Yet, it has become a potential target of varied cyber hackers and attacks due to the prevalent and open Internet of Medical Things (IoMT) [2,3]. Cyber-attacks on modern smart industries are of great concern as the losses caused by sophisticated cyber-attacks are extremely huge.…”

Section: Introductionmentioning

confidence: 99%

A hybrid DL-driven intelligent SDN-enabled malware detection framework for Internet of Medical Things (IoMT)

Khan

Akhunzada

2021

Computer Communications

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The rise of Internet of Things (IoT) has envisioned smart connectivity of billions of varied devices with high computational and processing capabilities resulting in the evolution of Internet of Medical Things (IoMT). This radical increase in the digital landscape of IoMT that processes considerably large amount of valuable data is primarily a potential attack target for varied cyber adversaries. One of the most prevalent, sophisticated and new evolving cyber threats for IoT ecosystems are multifaceted malicious malwares. The authors propose a highly scalable hybrid (deep learning) DL-driven SDN-enabled framework for efficient and timely detection of sophisticated IoMT malwares. Further, the proposed framework leverages the underlying IoMT resource constrained devices without exhaustion. We employed state-of-the-art publicly available dataset for a comprehensive evaluation of the proposed mechanism. Further, standard metrics have been employed to rigorously evaluate the performance of the proposed technique. For verification purpose, we compare our proposed mechanism with our constructed hybrid DL-driven architectures comprised of state-of-the-art DLalgorithms and current benchmarks. The proposed scheme outperforms in terms of high detection accuracy and speed efficiency. We also employed 10-fold cross validation to explicitly show unbiased results.

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Section: Introductionmentioning

confidence: 99%

A hybrid DL-driven intelligent SDN-enabled malware detection framework for Internet of Medical Things (IoMT)

Khan

Akhunzada

2021

Computer Communications

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“…Azmoodeh et al 15 used a machine learning approach to categorize ransomware from nonmalicious applications through the energy consumption patterns of different processes. Ban et al 16 proposed a machine learning method to characterize malware targeting multiple IoT platforms through OpCode sequences and application programming interface (API) sequences. Meidan et al 17 proposed a deep autoencoders algorithm according to the network‐based anomaly detection method.…”

Section: Introductionmentioning

confidence: 99%

An internet of things malware classification method based on mixture of experts neural network

Wang

Yang

et al. 2020

Trans Emerging Tel Tech

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With the rapid development of the internet of things (IoT) technology, IoT devices have brought great convenience to agriculture, industry, and our daily lives. However, there exist numerous vulnerabilities and lack of efficient protective measures in IoT, thus its devices can be easily infected by malware. It is of great importance to improve the accuracy of malware classification for detecting and preventing the IoT malware. In this article, we adopt the mixture of experts (MoE) neural network to analyze and classify the family of the IoT malware. A classification framework is proposed based on the MoE neural network which utilizes the multitask learning approach and is designed to train multiple neural networks, each of which is responsible for a set of data and tasks. The proposed framework contains three neural networks which are designed to analyze and classify the malware and benignware samples. Especially, an improved hierarchical softmax algorithm based on the MoE neural network is used to distinguish the malware from benignware and get its exact classification. Experiment results show that the presented MoE neural network model can effectively distinguish the malware and benignware. Besides, the MoE neural network is also effective for malware family classification. The comparison of experiment results with other literatures shows that the proposed classification method has better performance.

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IoT malware classification based on reinterpreted function-call graphs

Ban

Cheng

et al. 2023

Computers & Security

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A Cross-Platform Study on Emerging Malicious Programs Targeting IoT Devices

Cited by 8 publications

References 5 publications

A hybrid DL-driven intelligent SDN-enabled malware detection framework for Internet of Medical Things (IoMT)

A hybrid DL-driven intelligent SDN-enabled malware detection framework for Internet of Medical Things (IoMT)

An internet of things malware classification method based on mixture of experts neural network

IoT malware classification based on reinterpreted function-call graphs

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