ShielDroid framework achieves 97.5% accuracy in Android malware detection

Researchers have developed ShielDroid, a novel framework for detecting Android malware through hybrid dynamic analysis. This approach analyzes application behavior in real-time to identify malicious applications that evade traditional static analysis. The system combines Random Forest and Multilayer Perceptron algorithms, achieving a 97.5% accuracy rate with an execution time of 22.945 seconds. ShielDroid aims to bolster mobile device security by enabling timely detection of sophisticated malware. AI

IMPACT Enhances mobile security by providing a more accurate and timely method for detecting sophisticated Android malware.

RANK_REASON The cluster contains an academic paper detailing a new methodology and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Muhammad Iqbal Hossain
• multilayer perceptron
• random forest
• ShielDroid

• paper
• other

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