Decoy VNF for Enhanced Security in Fog Computing

Abstract

Fog computing extends cloud resources to the edge of the network, thus enabling network providers to support real-time applications at low latencies. These applications further demand high security against malicious attacks that target distributed fog servers. One effective defense mechanism here against cyber attacks is the use of honeypots. The latter acts as a potential target for attackers by diverting malicious traffic away from the servers that are dedicated to legitimate users. However, one main limitation of honeypots is the lack of real traffic and network activities. Therefore, it is important to implement a solution that simulates the behavior of the real system to lure attackers without the risk of being exposed. Hence this paper proposes a practical approach to generate network traffic by introducing decoy virtual network functions (VNF) embedded on fog servers, which make the network traffic on honeypots resemble a legitimate, vulnerable fog system to attract cyber attackers. The use of virtualization allows for robust scalability and modification of network functions based on incoming attacks, without the need for dedicated hardware. Moreover, deep learning is leveraged here to build fingerprints for each real VNF, which is subsequently used to support its decoy counterpart against active probes. The proposed framework is evaluated based on CPU utilization, memory usage, disk input/output access, and network latency.