An optimized and efficient android malware detection framework for future sustainable computing

Article Computer Science, Information Systems

DeepCatra: Learning flow- and graph-based behaviours for Android malware detection

Yafei Wu et al.

Summary: This study proposes DeepCatra, a multi-view learning approach for Android malware detection, which achieves considerable improvement by considering static call traces and extracting features in malware detection.

IET INFORMATION SECURITY (2023)

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Article Computer Science, Information Systems

An Enhanced Deep Learning Neural Network for the Detection and Identification of Android Malware

Pakarat Musikawan et al.

Summary: Android-based mobile devices have become popular due to their ease of use and wide range of capabilities. However, this popularity has also attracted attackers who use sophisticated malware obfuscation and detection avoidance tactics. In this article, an improved deep neural network called AMDI-Droid is presented to safeguard Android devices from malicious apps. The model combines multiple hidden layers to learn effective feature representations and uses a blending approach to produce final predictions.

IEEE INTERNET OF THINGS JOURNAL (2023)

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Article Computer Science, Artificial Intelligence

Cost-effective ensemble models selection using deep reinforcement learning

Yoni Birman et al.

Summary: Ensemble learning is a common technique that applies multiple learning models to the same task to improve classification accuracy. SPIREL is a novel cost-effective classification method that dynamically assigns different learning models and classification thresholds, showing high cost-effectiveness in large malware datasets.

INFORMATION FUSION (2022)

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Article Computer Science, Information Systems

An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks

Prabhat Kumar et al.

Summary: Internet of Medical Things (IoMT), a branch of Internet of Things (IoT), provides unparalleled benefits in improving patient care quality and treatment efficiency. However, the increasing severity and frequency of cyber-attacks in IoMT environments pose significant challenges.

COMPUTER COMMUNICATIONS (2021)

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Article Computer Science, Theory & Methods

DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network

Syed Ibrahim Imtiaz et al.

Summary: As the use of Android smartphones becomes more widespread, there is an increasing need for more efficient methods to detect and prevent malicious applications from attacking and compromising user devices.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2021)

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Article Computer Science, Artificial Intelligence