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.

在当前不断变化的网络安全场景中，积极的网络防御策略势在必行。在这项工作中，我们提出了一个标准测试平台来衡量定制网络的功效和效率，同时分析主动攻击期间的各种参数。所提出的测试平台可用于分析存在各种类型攻击的网络行为，并有助于在观察下微调所提出的算法。拟议的测试平台将允许用户设计、实施和评估主动网络防御机制，以及对自然启发和基于人工智能的技术的良好库支持。可以自定义网络负载、集群数量、家庭网络类型以及每个集群和网络中的节点数量。在使用所提供的测试平台并在现有网络架构上结合主动防御策略的同时，用户还可以设计和提出新的网络架构，以实现有效和安全的运行。在本文中，我们为网络防御策略模拟和基准测试提出了一个统一的标准测试平台，这将允许用户调查当前方法并将它们与其他方法进行比较，同时最终帮助为给定网络选择最佳方法安全情况。我们比较了不同场景下的网络性能，即正常、受到攻击和受到基于 NICS 的自适应防御机制的攻击，并取得了稳定的实验结果。实验结果清楚地表明，所提出的测试平台能够以最少的网络配置工作有效地模拟网络条件。在提议的测试平台上验证的防御机制的模拟结果提高了近 80%，同时将周转时间增加到 1-2%。本文还讨论了提议的测试平台在雾计算和边缘计算等现代技术中的适用性。