Data Protection through Data Security-as-a-Service using Blockchain Enabled Platform

Data access is inevitable in today’s world and it is prone to threat attacks and hence data security is utmost important for any enterprise to handle industrial solutions. The economics of data being used across the industries rapidly growing in current digital world so the potential data related threats is also rapidly growing. Data security is an integrated solution component for any Enterprise solution but with the growing demand on data security and potential threat handling, Data Security as a Service (DSaaS)f is a new model widely accepted in modern age architecture in Blockchain and Big Data world combining the power of cloud based security services, decentralized network in Blockchain and tamper-proof ledger management. Any Enterprise Security architecture comprises of how data is handled in a secured way and how integration between services (consumers/producers or API interaction or any middleware services) handles data between them. Hence it is inevitable to that future technology adoption should include Data Security-as-a-service for zero-trust solution design complying with compliance and security standards for industry.

Study of Machine Learning for Cloud Assisted IoT Security as a Service

Machine learning (ML) has been emerging as a viable solution for intrusion detection systems (IDS) to secure IoT devices against different types of attacks. ML based IDS (ML-IDS) normally detect network traffic anomalies caused by known attacks as well as newly introduced attacks. Recent research focuses on the functionality metrics of ML techniques, depicting their prediction effectiveness, but overlooked their operational requirements. ML techniques are resource-demanding that require careful adaptation to fit the limited computing resources of a large sector of their operational platform, namely, embedded systems. In this paper, we propose cloud-based service architecture for managing ML models that best fit different IoT device operational configurations for security. An IoT device may benefit from such a service by offloading to the cloud heavy-weight activities such as feature selection, model building, training, and validation, thus reducing its IDS maintenance workload at the IoT device and get the security model back from the cloud as a service.