Identification of a Malicious Optical Edge Device in the SDN-Based Optical Fog/Cloud Computing Network

Abstract

Abstract Software-defined networking (SDN) and optical transmission are the most cost-effective technologies for implementing high-bandwidth-based communication in the fog/cloud computing environment. The passive optical network uses optical line terminals and optical network units as optical edge devices (OEDs) to deliver fog/cloud-based services effectively. The security of such OEDs is one of the key issues for successful implementation of fog/cloud computing over the SDN-based optical network. The main security challenge is to detect and prevent the malicious OED that transmitting abusing data-frames in the SDN-based optical fog/cloud computing network. An OED can be easily hacked by the attacker to launch intrusive attacks those affect the quality of service of the optical channel. In this paper, a secure framework is proposed for identifying malicious OED in the fog/cloud computing over the SDN-based optical network. It identifies the malicious OED and shifts it to the honeypot to mitigate and analyze the attack. It uses two-stage hidden Markov model (HMM), intrusion detection system (IDS)-based fog manager and an optical virtual honeypot device (OVHD). A two-stage HMM is effectively used to reduce the false alarms of IDS in the identification of malicious OED and shifting it onto the OVHD. The OVHD is created in the SDN-based optical network by using the concept of free-available-resource and optical network virtualization. The proposed OVHD logs all malicious activities as well as attacker’s path for preventing future attacks. In order to validate the proposed framework, the simulation of two-stage HMM is implemented in MATLAB and mitigation impacts of the internal attacks are studied by using iFogSim toolkit. The results show the effectiveness of the proposed framework.