SECHO: A deep reinforcement learning-based scheme for secure handover in mobile edge computing

The experience of users in Mobile Edge Computing (MEC) highly depends on data offloading whose major bottle neck is the design of handover scheme. Existing research advocates that offloading security and quality of service (QoS) should be both considered for an optimal handover. However, recent studies concentrate on improving QoS while neglect security. Due to the complexity of MEC scenarios, generating an optimal handover scheme improving both QoS and security remains a challenge. To solve this problem, we propose SECHO, a secure handover scheme for blockchain-based single-user mobile edge computing system. Then, we design a security policy selection algorithm of offloading tasks to guarantees security performance and controls overhead. Finally, we customize the neural network for a deep reinforcement learning algorithm to realize optimal handover, where a security policy selection method is engaged to collaboratively enhance offloading QoS while decreasing underlying risks. Simulation results show that SECHO leads a 29.6%-302% advantage in overall performance than existing approaches. It proves that our proposal is effective to achieve secure handover while not degrading offloading QoS.