IoT Botnet Detection Approach Based on PSI graph and DGCNN classifier

Nguyen, Huy-Trung; Ngo, Quoc-Dung; Le, Van-Hoang

doi:10.1109/icicsp.2018.8549713

Cited by 79 publications

(42 citation statements)

References 17 publications

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“…The approach helps in preventing the compromise IoT devices to be compromised without technical, administrative knowledge. Nguyen et al proposed a new approach for Linux IoT botnet detection [33]. The approach combines CNN graph and PSI classifier.…”

Section: A Rq1:what Are the Contributions Of The Primary Studies?mentioning

confidence: 99%

Systematic Literature Review on IoT-Based Botnet Attack

et al. 2020

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The adoption of the Internet of Things (IoT) technology is expanding exponentially because of its capability to provide a better service. This technology has been successfully implemented on various devices. The growth of IoT devices is massive at present. However, security is becoming a major challenge with this growth. Attacks, such as IoT-based botnet attacks, are becoming frequent and have become popular amongst attackers.IoT has a resource constraint and heterogeneous environments, such as low computational power and memory. Hence, these constraints create problems in implementing a security solution in IoT devices. Therefore, various kind of attacks are possible due to this vulnerability, with IoT-based botnet attack being one of the most popular.In this study, we conducted a comprehensive systematic literature review on IoT-based botnet attacks. Existing state of the art in the area of study was presented and discussed in detail. A systematic methodology was adopted to ensure the coverage of all important studies. This methodology was detailed and repeatable. The review outlined the existing proposed contributions, datasets utilised, network forensic methods utilised and research focus of the primary selected studies. The demographic characteristics of primary studies were also outlined.The result of this review revealed that research in this domain is gaining momentum, particularly in the last 3 years (2018-2020). Nine key contributions were also identified, with Evaluation, System, and Model being the most conducted.

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Section: A Rq1:what Are the Contributions Of The Primary Studies?mentioning

confidence: 99%

Systematic Literature Review on IoT-Based Botnet Attack

et al. 2020

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“…Convolutional Neural Network (CNN) was then employed for the analysis and yielded 99.66% accuracy for detection and 99.32% accuracy for malware family classification. Nguyen et al [23] focused on the detection of IoT botnets by using Printable String Information (PSI) -graph as the main feature for the learning. They applied a Deep Graph Convolutional Neural Network (DGCNN) classifier and get an accuracy of 92%.…”

Section: B Malware Detection and Malware Family Classificationmentioning

confidence: 99%

Efficient Detection and Classification of Internet-of-Things Malware Based on Byte Sequences from Executable Files

Wan

Ban

Cheng

et al. 2020

IEEE Open J. Comput. Soc.

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The simple implementation and monotonous operation features make Internet of Things (IoT) vulnerable to malware attacks. In order to understand the behavior of IoT malware for further mitigation and prevention, static analysis on executable files of IoT malware is a feasible approach. However, current analysis approaches based on opcode or call-graph usually do not work well with the diversity in CPU architectures and are often resource demanding. In this paper, we propose an efficient scheme to detect and classify IoT malware programs leveraging machine learning methods. We show that reliable and efficient detection and classification can be implemented by exploring the essential discriminant information stored in the byte sequences at the entry points of executable programs. We demonstrate the performance of the proposed scheme using a large scale dataset composed of 111K benignware and 111K malware programs from 7 CPU architectures. The proposed scheme achieves near optimal generalization performance for malware detection (99.9% in accuracy) and for malware family classification (98.4% in accuracy). Moreover, when the CPU architecture information is considered in the learning, the proposed scheme combined with SVM classifiers can yield even higher generalization performance using fewer bytes from the executable files. The findings in this paper is promising for implementing a lightweight malware protection solution on IoT devices with limited resources.

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“…Nguyen et al [102] proposed a ''graph-based convolution neural network (CNN)'' mechanism for the detection of IoT botnets, which can launch malware attacks. During their experimentation, it was observed that their proposed method reliably classified the benign and IoT malware with an improved accuracy.…”

Section: A Existing Malware Detection Schemes In Iot/iomt Communicatmentioning

confidence: 99%

IoMT Malware Detection Approaches: Analysis and Research Challenges

et al. 2019

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The advancement in Information and Communications Technology (ICT) has changed the entire paradigm of computing. Because of such advancement, we have new types of computing and communication environments, for example, Internet of Things (IoT) that is a collection of smart IoT devices. The Internet of Medical Things (IoMT) is a specific type of IoT communication environment which deals with communication through the smart healthcare (medical) devices. Though IoT communication environment facilitates and supports our day-today activities, but at the same time it has also certain drawbacks as it suffers from several security and privacy issues, such as replay, man-in-the-middle, impersonation, privileged-insider, remote hijacking, password guessing and denial of service (DoS) attacks, and malware attacks. Among these attacks, the attacks which are performed through the malware botnet (i.e., Mirai) are the malignant attacks. The existence of malware botnets leads to attacks on confidentiality, integrity, authenticity and availability of the data and other resources of the system. In presence of such attacks, the sensitive data of IoT communication may be disclosed, altered or even may not be available to the authorized users. Therefore, it becomes essential to protect the IoT/IoMT environment from malware attacks. In this review paper, we first perform the study of various types of malware attacks, and their symptoms. We also discuss some architectures of IoT environment along with their applications. Next, a taxonomy of security protocols in IoT environment is provided. Moreover, we conduct a comparative study on various existing schemes for malware detection and prevention in IoT environment. Finally, some future research challenges and directions of malware detection in IoT/IoMT environment are highlighted.

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IoT Botnet Detection Approach Based on PSI graph and DGCNN classifier

Cited by 79 publications

References 17 publications

Systematic Literature Review on IoT-Based Botnet Attack

Systematic Literature Review on IoT-Based Botnet Attack

Efficient Detection and Classification of Internet-of-Things Malware Based on Byte Sequences from Executable Files

IoMT Malware Detection Approaches: Analysis and Research Challenges

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