Roy Sangita, Singh Jawar, Mathew Jimson
Department of Electrical Engineering, Indian Institute of Technology Patna, Patna, 801106, India.
Department of Computer Science and Engineering, Thapar Institute of Engineering & Technology, Patiala, 147004, India.
Sci Rep. 2025 Mar 20;15(1):9558. doi: 10.1038/s41598-025-93138-7.
The escalating distributed denial of service (DDoS) attacks severely threatens the security of the industrial internet of things (IIoT). This paper introduces moving target defense (MTD) as an adaptive solution to fortify IIoT security against DDoS attacks. Dynamically reconfiguring network elements and service placements makes it challenging for attackers to target specific vulnerabilities. We propose an MTD traffic manager (MTDTM) architecture to enable early detection and mitigation of DDoS attacks within resource-constrained edge clouds. A traffic classifier is integrated into our model to intelligently filter incoming traffic, ensuring real-time responsiveness to the demands of IIoT applications. Moreover, dynamic admission rules and relocation of service replicas efficiently distribute the traffic, ensuring the availability of services for legitimate users. Unlike traditional static defense methods, our adaptive approach caters to the evolving DDoS threat landscape of IIoT, safeguarding critical industrial processes. Simulation results validate the efficiency of our algorithm while maintaining an acceptable quality of service. Our research demonstrated a 15% to 20% improvement in service response times compared to existing algorithms. Notably, we achieved significant enhancements in average resource availability during DDoS attacks across various parameter variations.
不断升级的分布式拒绝服务（DDoS）攻击严重威胁着工业物联网（IIoT）的安全。本文介绍了移动目标防御（MTD），作为一种强化IIoT安全以抵御DDoS攻击的自适应解决方案。动态重新配置网络元素和服务布局使攻击者难以针对特定漏洞。我们提出了一种MTD流量管理器（MTDTM）架构，以在资源受限的边缘云中实现对DDoS攻击的早期检测和缓解。流量分类器集成到我们的模型中，以智能过滤传入流量，确保对IIoT应用需求的实时响应。此外，动态准入规则和服务副本的重新定位有效地分配流量，确保为合法用户提供服务可用性。与传统的静态防御方法不同，我们的自适应方法适应IIoT不断演变的DDoS威胁格局，保障关键工业流程。仿真结果验证了我们算法的效率，同时保持了可接受的服务质量。我们的研究表明，与现有算法相比，服务响应时间提高了15%至20%。值得注意的是，在各种参数变化的DDoS攻击期间，我们在平均资源可用性方面取得了显著提高。
