Obfuscation-Resilient Android Malware Analysis Based on Complementary Features

Gao, Cuiying; Cai, Minghui; Yin, Shuijun; Huang, Gaozhun; Li, Heng; Yuan, Wei; Luo, Xiapu

doi:10.1109/tifs.2023.3302509

Cited by 16 publications

(1 citation statement)

References 51 publications

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“…Deep learning models may struggle to adapt to rapidly changing environments, leading to potential obsolescence [129][130][131][132][133][134][135][136]. Developing dynamic models capable of continuous learning from new data and adapting to evolving threat landscapes is a solution to this problem.…”

Section: Open Challengesmentioning

confidence: 99%

Deep learning-powered malware detection in cyberspace: a contemporary review

Redhu,

Choudhary,

Srinivasan

et al. 2024

Front. Phys.

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This article explores deep learning models in the field of malware detection in cyberspace, aiming to provide insights into their relevance and contributions. The primary objective of the study is to investigate the practical applications and effectiveness of deep learning models in detecting malware. By carefully analyzing the characteristics of malware samples, these models gain the ability to accurately categorize them into distinct families or types, enabling security researchers to swiftly identify and counter emerging threats. The PRISMA 2020 guidelines were used for paper selection and the time range of review study is January 2015 to Dec 2023. In the review, various deep learning models such as Recurrent Neural Networks, Deep Autoencoders, LSTM, Deep Neural Networks, Deep Belief Networks, Deep Convolutional Neural Networks, Deep Generative Models, Deep Boltzmann Machines, Deep Reinforcement Learning, Extreme Learning Machine, and others are thoroughly evaluated. It highlights their individual strengths and real-world applications in the domain of malware detection in cyberspace. The review also emphasizes that deep learning algorithms consistently demonstrate exceptional performance, exhibiting high accuracy and low false positive rates in real-world scenarios. Thus, this article aims to contribute to a better understanding of the capabilities and potential of deep learning models in enhancing cybersecurity efforts.

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