Learning-Based Secure Mobile Offloading
Learning-based secure offloading strategy applies the reinforcement learning algorithms to derive the optimal data offloading and defense policy for the mobile devices to counter the potential smart attackers in a mobile computing system, without any preliminary knowledge of the environmental parameters and the attack model.
With the proliferation of cloud-based mobile services, mobile devices such as smartphones and tablets can offload their applications and data to the cloud to improve user experience in terms of longer battery lifetime, larger data storage, faster processing speed, and more powerful security services (Xiao et al., 2017). However, data offloading to the cloud via access points (APs) or base stations (BSs) is vulnerable to various types of attacks, such as spoofing, eavesdropping, and jamming (Kumar and Lu, 2010). A smart attacker can use smart and programmable radio devices such as Universal Software Radio Peripherals (USPRs) or the...
This work is supported by the National Natural Science Foundation of China under Grant 61671396.
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