An advanced security model for fog computing to tackle various attacks on the data link layer using the QQL-FHES scheme

Fog computing provides maximum flexibility and low latency by divesting the usage of cloud resources and amenities to the end users. In rendering the services to the cloud environment, Fog computing encounters critical challenges over Privacy and security while utilizing the network edges. Mainly, attacks like impersonation attacks, eavesdropping attacks, and jamming attacks are common threats in the fog environment. To overcome these challenges, an intelligent defense scheme is essential for improved datalink layer security (DLS). With the proposed intelligent defense scheme, the fog layer could take up the challenge of finding all the possible attacks. This work has proposed a Modified Homomorphic Encryption Scheme (MHES). With the hybrid Fully Homomorphic Encryption Scheme (FHES) and a Q-Learning algorithm, the proposed encryption scheme is the most effective method to resist malicious attacks in Fog computing. Here a quadruple Q-learning method (QQL) is proposed for confining the motive of attacks and generating an optimal security mechanism against clever attackers. They were illegitimate to the datalink layer (DL). The combination of the FHES and QQL in Fog computing provides better security to the DL against various intelligent malicious attacks.