HamDroid: permission-based harmful android anti-malware detection using neural networks

Article Computer Science, Artificial Intelligence

MLDroid-framework for Android malware detection using machine learning techniques

Arvind Mahindru et al.

Summary: The research introduces MLDroid, a web-based framework for detecting malware in Android devices, achieving a high detection rate of 98.8% through dynamic analysis and machine learning algorithms.

NEURAL COMPUTING & APPLICATIONS (2021)

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Review Computer Science, Information Systems

A survey of android application and malware hardening

Vikas Sihag et al.

Summary: With the increasing quantity and complexity of malware, Android users are facing severe security threats. Malware authors employ various techniques to evade detection, making it more challenging to detect. Strengthening security mechanisms has become increasingly important in application development.

COMPUTER SCIENCE REVIEW (2021)

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Review Computer Science, Information Systems

A survey of malware detection in Android apps: Recommendations and perspectives for future research

Asma Razgallah et al.

Summary: This paper investigates the main mechanisms and approaches for malware detection in Android applications, identifying the advantages and limitations of each, and suggesting avenues for future research in this area.

COMPUTER SCIENCE REVIEW (2021)

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Article Computer Science, Information Systems

NATICUSdroid: A malware detection framework for Android using native and custom permissions

Akshay Mathur et al.

Summary: The rapid growth and popularity of Android apps have made it a vulnerable target for malware, prompting researchers to propose a new malware detection framework called NATICUSdroid. By using statistically selected Android permissions for classification, the experimental results show that the Random Forest classifier-based model performed best with an accuracy of 97%.

JOURNAL OF INFORMATION SECURITY AND APPLICATIONS (2021)

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Article Computer Science, Information Systems

GDroid: Android malware detection and classification with graph convolutional network

Han Gao et al.

Summary: The paper introduces a novel approach for Android malware detection and familial classification based on Graph Convolutional Network (GCN). Through experiments, GDroid system shows promising results in detecting Android malware and classifying malware families, outperforming existing methods.

COMPUTERS & SECURITY (2021)

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Article Computer Science, Information Systems

BLADE: Robust malware detection against obfuscation in android

Vikas Sihag et al.

Summary: The BLADE system is a novel obfuscation-resilient system based on Opcode Segments, which uses innovative methods for feature characterization and simplification of dalvik opcodes to enhance resilience. It has been found effective, accurate, and resilient against various obfuscation techniques.

FORENSIC SCIENCE INTERNATIONAL-DIGITAL INVESTIGATION (2021)

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Article Computer Science, Information Systems