A Scalable and Efficient Multi-Agent Architecture for Malware Protection in Data Sharing Over Mobile Cloud

Abstract

Sensitive Data requires encryption before uploading to a public cloud. Access control based on Attribute-Based Encryption (ABE) is an effective technique to ensure data shared security and privacy in the public cloud. Cipher-Text Policy of ABE may suffer from scalability and performance issues as they do not permit for addition or removal of computing nodes at run time. Furthermore, another problem is existing approaches suffer from single-point-of-failure (SPoF). Therefore, we introduce a scalable multi-agent system architecture based on CP-ABE to ensure data sharing on public cloud storage and reliability in our proposed work. We proposed a cloud host as an inter-mediator between the user and the authorized agents without violating the system’s privacy and security. We have also proposed a novel methodology to protect the cloud from malware by exploiting the state’s efficient power of the art Gemini approach. Gemini is an efficient methodology for binary code-based graph embedding similarity detection. Our proposed study overcomes the deficiencies of scalability and efficiency along with providing the mechanism for malware detection in the cloud. It covers three aspects: scalability, the efficiency with multi-agents, and malware detection capability. Our contributed work is scalable, efficient for cloud data sharing, and protects from malware. Results reveal that our work provides better performance with preserving the security, privacy, and fine granularity features of CP-ABE and malware screening using regress analysis by the graph embedding technique.

Keywords

• Scalable cloud architecture
• multi-agent architecture
• malware detection for mobile cloud
• graph similarity based malware detection
• graph embedding based analysis