What is it about?
Cyber attacks continue to increase in frequency and sophistication, easily defeating widely used defenses. Malware authors use numerous techniques to evade analysis tools, which delays analysis of malware thereby enabling it to earn more revenue or inflict more damage. Artificial Intelligence is becoming essential in detecting new malware, however it is only as good as the data it is trained with.
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Why is it important?
We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI powered malware detection model: malware sophistication, analysis techniques, malware repositories, feature selection and machine learning vs deep learning. The effectiveness of an AI model is dependent on the quality of the features it is trained with. In turn, the quality and authenticity of these features is dependent on the quality of the dataset and the suitability of the analysis tool.
Perspectives
Sophisticated and evasive malware is challenging to extract authentic discriminatory features from and combined with poor quality datasets this can lead to a situation where a model achieves high accuracy with only one specific dataset.
Matthew Gaber
Edith Cowan University
Read the Original
This page is a summary of: Malware Detection with Artificial Intelligence: A Systematic Literature Review, ACM Computing Surveys, December 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3638552.
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