Sumit Kumar^1,*, S. Indu², Gurjit Singh Walia¹

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1035-1051, 2023, DOI:10.32604/iasc.2023.024469

Abstract Android Smartphones are proliferating extensively in the digital world due to their widespread applications in a myriad of fields. The increased popularity of the android platform entices malware developers to design malicious apps to achieve their malevolent intents. Also, static analysis approaches fail to detect run-time behaviors of malicious apps. To address these issues, an optimal unification of static and dynamic features for smartphone security analysis is proposed. The proposed solution exploits both static and dynamic features for generating a highly distinct unified feature vector using graph based cross-diffusion strategy. Further, a unified feature is subjected to the fuzzy-based classification… More >

Adnan Bin Amanat Ali¹, Vasaki Ponnusamy¹, Anbuselvan Sangodiah¹, Roobaea Alroobaea², N. Z. Jhanjhi^3,*, Uttam Ghosh⁴, Mehedi Masud²

Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 571-585, 2021, DOI:10.32604/iasc.2021.015913

Abstract Most smartphone users prefer easy and convenient authentication without remembering complicated passwords or drawing intricate patterns. Preferably, after one-time authentication, there is no verification of the user’s authenticity. Therefore, security and privacy against unauthorized users is a crucial research area. Behavioral authentication is an emerging security technique that is gaining attention for its uniqueness and transparency. In this paper, a behavior-based authentication system is built using swipe movements to continuously authenticate the user after one-time traditional authentication. The key feature is the selection of an optimal feature set for the swipe movement. Five machine learning classifiers are used, of which… More >