PSI-rooted subgraph: A novel feature for IoT botnet detection using classifier algorithms

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

doi:10.1016/j.icte.2019.12.001

Cited by 37 publications

(9 citation statements)

References 20 publications

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“…The following section will analyze frameworks that try to explain why a particular classification is made. Other interesting works are [190], [191].…”

Section: ) Artificial Intelligence In Bot(net) Detectionmentioning

confidence: 99%

Explainable Artificial Intelligence in CyberSecurity: A Survey

et al. 2022

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Nowadays, Artificial Intelligence (AI) is widely applied in every area of human being's daily life. Despite the AI benefits, its application suffer from the opacity of complex internal mechanisms and doesn't satisfy by design the principles of Explainable Artificial Intelligence (XAI). The lack of transparency further exacerbates the problem in the field of Cybersecurity because entrusting crucial decisions to a system that cannot explain itself presents obvious dangers. There are several methods in the literature capable of providing explainability of AI results. Anyway, the application of XAI in Cybersecurity can be a doubleedged sword. It substantially improves the Cybersecurity practices but simultaneously leaves the system vulnerable to adversary attacks. Therefore, there is a need to analyze the state-of-the-art of XAI methods in Cybersecurity to provide a clear vision for future research. This study presents an in-depth examination of the application of XAI in Cybersecurity. It considers more than 300 papers to comprehensively analyze the main Cybersecurity application fields, like Intrusion Detection Systems,Malware detection, Phishing and Spam detection, BotNets detection, Fraud detection, Zero-Day vulnerabilities, Digital Forensics and Crypto-Jacking . Specifically, this study focuses on the explainability methods adopted or proposed in these fields, pointing out promising works and new challenges.

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“…The following section will analyze frameworks that try to explain why a particular classification is made. Other interesting works are [190], [191].…”

Section: ) Artificial Intelligence In Bot(net) Detectionmentioning

confidence: 99%

Explainable Artificial Intelligence in CyberSecurity: A Survey

et al. 2022

View full text Add to dashboard Cite

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“…H.-T. Nguyen et al [14] proposed a static analysis framework based on PSI-rooted subgraph features. Graph-based features from MIPS and ARM ELF samples were converted into numerical type using vectorisation.…”

Section: Studies That Use Static Analysis Approachmentioning

confidence: 99%

Machine Learning-based Detection of IoT Malware using System Call Data

Tahir

Qadir

2022

Preprint

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The popularity of IoT has raised many security concerns. Multiple devices with new and improved features are introduced into the market every year, expanding the IoT attack surface. The number of emerging malware variants are also increasing along with the use of antidetection mechanisms such as packing and obfuscation. The cyber security community has proposed multiple solutions, but the platform heterogeneity of IoT devices poses a unique challenge. Most studies use a static analysis approach for malware detection. However, this approach is not effective for the detection of packed and obfuscated malware. A few studies use a dynamic analysis approach, but they do not address the multiarchitecture nature of IoT devices. The main contribution of this research is to present a machine learning model that can detect rapidly evolving, packed or obfuscated malware that targets MIPS, ARM, and x86 IoT devices. We compile our own dataset by dynamically analysing 524 malware samples and 524 benign samples for various IoT architectures. The extracted system call data is preprocessed and used to train five machine learning models. The best results are achieved using Random Forest (RF) with a detection accuracy of 99.04% and FPR of 0.01.

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“…e literature [83] proposed a new high-order subgraph feature based on PSI (printable characters) extracted from malicious code to detect large-scale botnets. ese features had precise behavior descriptions and less space requirements.…”

Section: Complex Networkmentioning

confidence: 99%

Survey on Botnet Detection Techniques: Classification, Methods, and Evaluation

Xing

Hui

Zhao

et al. 2021

Mathematical Problems in Engineering

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With the continuous evolution of the Internet, as well as the development of the Internet of Things, smart terminals, cloud platforms, and social platforms, botnets showing the characteristics of platform diversification, communication concealment, and control intelligence. This survey analyzes and compares the most important efforts in the botnet detection area in recent years. It studies the mechanism characteristics of botnet architecture, life cycle, and command and control channel and provides a classification of botnet detection techniques. It focuses on the application of advanced technologies such as deep learning, complex network, swarm intelligence, moving target defense (MTD), and software-defined network (SDN) for botnet detection. From the four dimensions of service, intelligence, collaboration, and assistant, a common bot detection evaluation system (CBDES) is proposed, which defines a new global capability measurement standard. Combing with expert scores and objective weights, this survey proposes quantitative evaluation and gives a visual representation for typical detection methods. Finally, the challenges and future trends in the field of botnet detection are summarized.

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PSI-rooted subgraph: A novel feature for IoT botnet detection using classifier algorithms

Cited by 37 publications

References 20 publications

Explainable Artificial Intelligence in CyberSecurity: A Survey

Explainable Artificial Intelligence in CyberSecurity: A Survey

Machine Learning-based Detection of IoT Malware using System Call Data

Survey on Botnet Detection Techniques: Classification, Methods, and Evaluation

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