AI-assisted Computer Network Operations testbed for Nature-Inspired Cyber Security based adaptive defense simulation and analysis

In the current ever-changing cybersecurity scenario, active cyber defense strategies are imperative. In this work, we present a standard testbed to measure the efficacy and efficiency of customized networks while analyzing various parameters during the active attack. The presented testbed can be used for analyzing the network behavior in presence of various types of attacks and can help in fine-tuning the proposed algorithm under observation. The proposed testbed will allow users to design, implement, and evaluate the active cyber defense mechanisms with good library support of nature-inspired and AI-based techniques. Network loads, number of clusters, types of home networks, and number of nodes in each cluster and network can be customized. While using the presented testbed and incorporating active-defense strategies on existing network architectures, users can also design and propose new network architectures for effective and safe operation. In this paper, we propose a unified and standard testbed for cyber defense strategy simulation and bench-marking, which would allow the users to investigate current approaches and compare them with others, while ultimately aiding in the selection of the best approach for a given network security situation. We have compared the network performance in difference scenarios namely, normal, under attack and under attack in presence of NICS-based adaptive defense mechanism and achieved stable experimental results. The experimental results clearly show that the proposed testbed is able to simulate the network conditions effectively with minimum efforts in network configuration. The simulation results of defense mechanisms verified on the proposed testbed got the improvement on almost 80 percent while increasing the turnaround time to 1-2 percent. The applicability of proposed testbed in modern technologies like Fog Computing and Edge Computing is also discussed in this paper. (C) 2021 Elsevier B.V. All rights reserved.