IoTForge Pro

The necessity for strong security measures to fend off cyberattacks has increased due to the growing use of Industrial Internet of Things (IIoT) technologies. This research introduces IoTForge Pro, a comprehensive security testbed designed to generate a diverse and extensive intrusion dataset for IIoT environments. The testbed simulates various IIoT scenarios, incorporating network topologies and communication protocols to create realistic attack vectors and normal traffic patterns. The generated dataset, named ForgeIIOT, includes various attack types, such as denial-of-service, man-in-the-middle, ransomware, wildcard abuse and malware-based intrusions, providing a valuable resource for developing and evaluating intrusion detection systems (IDS). Additionally, we apply advanced machine learning techniques to analyze the ForgeIIOT dataset, demonstrating the effectiveness of different models in identifying and classifying various types of cyberattacks. Our experimental results highlight the potential of machine learning algorithms in enhancing the security of IIoT systems by accurately detecting anomalies and malicious activities. This research contributes to the field by offering a rich dataset and a robust framework for testing and improving IDS for IIoT, ultimately aiming to strengthen the cybersecurity posture of industrial networks.

鉴于工业物联网（IIoT）技术的广泛应用，为抵御网络攻击而采取的严格安全措施的需求日益增长。本研究推出了IoTForge Pro，这是一个全面的网络安全测试平台，旨在为IIoT环境生成多样化且广泛的安全入侵数据集。该测试平台模拟了多种IIoT场景，融合了网络拓扑和通信协议，以构建真实的攻击向量和正常流量模式。生成的数据集命名为ForgeIIOT，包含了各种攻击类型，如拒绝服务攻击、中间人攻击、勒索软件、通配符滥用和基于恶意软件的入侵等，为开发与评估入侵检测系统（IDS）提供了宝贵资源。此外，我们运用先进的机器学习技术对ForgeIIOT数据集进行分析，展示了不同模型在识别和分类各类网络攻击方面的有效性。我们的实验结果突显了机器学习算法在精确检测异常和恶意活动，从而增强IIoT系统安全性能方面的潜力。本研究通过提供丰富的数据集和强大的测试与改进IDS的框架，为工业网络的安全态势强化做出了贡献。