Industrial Internet-of-Things (IIoT) is a powerful IoT application which remodels the growth of industries by ensuring transparent communication among various entities such as hubs, manufacturing places and packaging units. Introducing data science techniques within the IIoT improves the ability to analyze the collected data in a more efficient manner, which current IIoT architectures lack due to their distributed nature. From a security perspective, network anomalies/attackers pose high security risk in IIoT. In this paper, we have addressed this problem, where a coordinator IoT device is elected to compute the trust of IoT devices to prevent the malicious devices to be part of network. Further, the transparency of the data is ensured by integrating a blockchain-based data model. The performance of the proposed framework is validated extensively and rigorously via MATLAB against various security metrics such as attack strength, message alteration, and probability of false authentication. The simulation results suggest that the proposed solution increases IIoT network security by efficiently detecting malicious attacks in the network.
翻译:工业互联网是一种强大的IoT应用,它通过确保中心、制造场所和包装单位等不同实体之间的透明通信,对工业的增长进行改造,在IIoT内采用数据科学技术,提高了以更有效的方式分析所收集数据的能力,而目前IIoT结构由于分布性质而缺乏这种能力。从安全角度来看,网络反常/攻击者对IIoT构成高度安全风险。在本文件中,我们处理了这一问题,选择了一个协调员IoT装置来计算IoT装置的信任,以防止恶意装置成为网络的一部分。此外,通过整合一个基于链式的数据模型,确保数据的透明度。通过MATLAB,针对攻击强度、信息改变和虚假认证的可能性等各种安全指标,对拟议框架的绩效进行广泛和严格的验证。模拟结果表明,拟议的解决方案通过在网络中有效检测恶意袭击,提高了IIoT网络的安全性。