A Lightweight and Multi-Stage Approach for Android Malware Detection Using Non-Invasive Machine Learning Techniques

Costa, Leonardo da; Moia, Vitor Hugo Galhardo

doi:10.1109/access.2023.3296606

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…The authors in [24] proposed a method for detecting multistage attacks using machine learning to process malware events. Inspiration can also be found in slightly different solutions, for example, for Android systems [25], where the method is based on obtaining features from the Android package kit file format. Such an approach may require efficient indexing and searching over large-scale feature vectors.…”

Section: Related Workmentioning

confidence: 99%

Multistage Malware Detection Method for Backup Systems

Novak,

Oujezsky,

Kaura

et al. 2024

Technologies

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This paper proposes an innovative solution to address the challenge of detecting latent malware in backup systems. The proposed detection system utilizes a multifaceted approach that combines similarity analysis with machine learning algorithms to improve malware detection. The results demonstrate the potential of advanced similarity search techniques, powered by the Faiss model, in strengthening malware discovery within system backups and network traffic. Implementing these techniques will lead to more resilient cybersecurity practices, protecting essential systems from hidden malware threats. This paper’s findings underscore the potential of advanced similarity search techniques to enhance malware discovery in system backups and network traffic, and the implications of implementing these techniques include more resilient cybersecurity practices and protecting essential systems from malicious threats hidden within backup archives and network data. The integration of AI methods improves the system’s efficiency and speed, making the proposed system more practical for real-world cybersecurity. This paper’s contribution is a novel and comprehensive solution designed to detect latent malware in backups, preventing the backup of compromised systems. The system comprises multiple analytical components, including a system file change detector, an agent to monitor network traffic, and a firewall, all integrated into a central decision-making unit. The current progress of the research and future steps are discussed, highlighting the contributions of this project and potential enhancements to improve cybersecurity practices.

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Section: Related Workmentioning

confidence: 99%