Cognitively reconfigurable mimic-based heterogeneous password recovery system. Issue 116 (May 2022)

• Record Type: Journal Article
• Authors: Li, Bin; Zhou, Qinglei; Cao, Yan; Si, Xueming
• Is Part Of: Computers & security. Issue 116(2022)
• Publication Date: 2022-05
• Subjects: Mimic computing -- Password recovery -- Hybrid heterogeneous -- High efficiency -- Cognitive decision -- Load balancing; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2022.102667
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: The recovery of encrypted information systems based on password verification mechanisms is an important task in electronic forensics, data restoration, illegal information filtering, and network security maintenance. The traditional password recovery system is mainly based on CPUs and GPUs, which have low cracking efficiency and cannot meet the computing needs of users. Therefore, this paper proposes a cognitively reconfigurable mimic-based heterogeneous password recovery system. Applying the concept of mimic computing, a multidimensional and reconfigurable hybrid heterogeneous system is established through the use of CPUs, GPUs, and FPGAs, which works by coordinating software and hardware to improve the system's computing ability and range of solution types to meet diverse cracking needs. Second, high-performance password recovery and password generation algorithms that run on FPGAs are designed to expedite the calculation and verification of passwords. Third, a hierarchical password database is established to realize dynamic feedback and updating of passwords, aiming for various application scenarios and improving password recovery efficiency. Finally, through task perception and decision-making reasoning and with dynamic structural transformation and load balancing, the computing potential of the system is fully mobilized so that the entire system can efficiently complete the encryption recovery task. Experimental analysis and results show that compared with traditional CPU and GPU systems, our system exhibits significantly improved recovery efficiency and can better support heterogeneous systems, with high scalability and energy efficiency.