TY - JOUR
T1 - Energy-efficient hybrid detection framework for suspicious software components in mobile applications using echo state networks
AU - Rawat, Hitesh
AU - Raj, A. Samson Arun
AU - González-Torres, Antonio
AU - Bhardwaj, Purvee
AU - Sankaran, K. Sakthidasan
AU - Thiyagu, T. M.
AU - Rawat, Anjali
N1 - Publisher Copyright:
© Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.
PY - 2025
Y1 - 2025
N2 - This research introduces a novel detection framework for identifying Suspicious Software Components (SSC) in mobile applications, enhancing accuracy and efficiency in cybersecurity. The primary objectives are to develop an advanced threat detection system with improved accuracy, reduce false positive rates, and ensure energy-efficient deployment in resource-constrained environments. The proposed method integrates static feature extraction with deep learning and machine learning (ML) models, leveraging Echo State Networks (ESN) for superior threat classification. Additionally, energy consumption analysis ensures feasibility for deployment in mobile environments. The methodology is validated using the CIC-MalMem-2022 dataset, demonstrating improved detection capabilities while maintaining low false positive rates. Experimental results show that the proposed ESN-based framework achieves an accuracy of 99.85% while maintaining superior energy efficiency. This work advances mobile security by offering a robust, scalable, and energy-efficient solution for malware detection.
AB - This research introduces a novel detection framework for identifying Suspicious Software Components (SSC) in mobile applications, enhancing accuracy and efficiency in cybersecurity. The primary objectives are to develop an advanced threat detection system with improved accuracy, reduce false positive rates, and ensure energy-efficient deployment in resource-constrained environments. The proposed method integrates static feature extraction with deep learning and machine learning (ML) models, leveraging Echo State Networks (ESN) for superior threat classification. Additionally, energy consumption analysis ensures feasibility for deployment in mobile environments. The methodology is validated using the CIC-MalMem-2022 dataset, demonstrating improved detection capabilities while maintaining low false positive rates. Experimental results show that the proposed ESN-based framework achieves an accuracy of 99.85% while maintaining superior energy efficiency. This work advances mobile security by offering a robust, scalable, and energy-efficient solution for malware detection.
KW - Echo state network
KW - Energy consumption
KW - Smart device
KW - Static analysis
KW - Suspicious script codes
UR - http://www.scopus.com/inward/record.url?scp=105009091131&partnerID=8YFLogxK
U2 - 10.1007/s41870-025-02615-9
DO - 10.1007/s41870-025-02615-9
M3 - Artículo
AN - SCOPUS:105009091131
SN - 2511-2104
JO - International Journal of Information Technology (Singapore)
JF - International Journal of Information Technology (Singapore)
ER -