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Malware and Ransomware Classification, Detection, and Prevention using Artificial Intelligence (Al) Techniques
Book chapter

Malware and Ransomware Classification, Detection, and Prevention using Artificial Intelligence (Al) Techniques

Md Jobair Hossain Faruk, Hossain Shahriar, Mohammad Masum and Khairul Alom
Big Data Analytics and Intelligent Systems for Cyber Threat Intelligence, pp.211-233
River Publishers, 1
03/2023
DOI: https://doi.org/10.1201/9781003373384-11

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Abstract

BHO Ai FPR Network Intrusion Detection System Roc Curve Feature Selection Semi-supervised Learning Cyber Threat Intelligence Malware Detection Malware and ransomware classification Artificial Intelligence or Cybernetics Cybersecurity Machine Learning
Rapid technological advancement has made cybersecurity more difficult due to damaging malware and ransomware assaults that pose a critical security danger. When it comes to countering freshly developed, sophisticated, dangerous programs, traditional antimalware and ransomware solutions are highly constrained. In contrast, antimalware and ransomware technology has advanced significantly. There is still a lot of work to bring cutting-edge ideas to fruition. Detecting and blocking malware and ransomware activities is the focus of this study, as is the neural network may be utilized to build new malware solutions. We examine the present malware detection methods, their faults, and how to improve them. A decision tree (DT), random forest (RF), Naive Bayes (NB), logistic regression (LR), and neural network based classifiers were employed to classify ransomware. Using ransomware data, we evaluated our proposed framework for each technique. The experimental results demonstrate that RF classifiers outperform other methods in terms of accuracy (0.99 ± 0.01), F-beta (0.97 ± 0.03), precision scores (0.99 ± 0.00), and NB perform best in recall (0.99 ± 0.00).

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