Title | Federated Machine Learning Architecture for Searching Malware |
Publication Type | Conference Paper |
Year of Publication | 2021 |
Authors | Hahanov, V.I., Saprykin, A.S. |
Conference Name | 2021 IEEE East-West Design Test Symposium (EWDTS) |
Keywords | Analytical models, cloud sandbox, cloud–edge computing, Collaboration, composability, Computational modeling, Computer architecture, cyber-physical system, federated machine learning, logic-vector analysis, machine learning, Malware, malware detection, malware sandbox, mathematical models, ML–computing, Policy Based Governance, pubcrawl, Sandboxing, signature, Training |
Abstract | Modern technologies for searching viruses, cloud-edge computing, and also federated algorithms and machine learning architectures are shown. The architectures for searching malware based on the xor metric applied in the design and test of computing systems are proposed. A Federated ML method is proposed for searching for malware, which significantly speeds up learning without the private big data of users. A federated infrastructure of cloud-edge computing is described. The use of signature analysis and the assertion engine for searching malware is shown. The paradigm of LTF-computing for searching destructive components in software applications is proposed. |
DOI | 10.1109/EWDTS52692.2021.9581000 |
Citation Key | hahanov_federated_2021 |