Hashgraph consensus algoritması kullanarak ıot cihazının sınıflandırılması için dağıtık karalisteleme protokolü

Industrial applications require highly reliable, secure, low-power and low-delay communications. However, wireless communication links in the industrial environment suffer from various channel impairments which can compromise these requirements. This thesis presents a new reliable blacklisting protocol for ensuring the Internet of Things (IoT) network security and mitigating the effects of interference caused by multipath Rayleigh fading using a distributed approach. The proposed blacklisting protocol is simulated over a distributed IoT network setup where flat Rayleigh fading disrupts Message Queuing Telemetry Transport (MQTT) communications. Distributed servers jointly decide in real-time whether to blacklist a device after individually performing anomaly detection and submitting their results to the hashgraph network. The IoT devices are classified by a device fingerprinting method using various machine learning (ML) algorithms that are trained with real-time packet capture data. The proposed blacklisting protocol is shown to increase the accuracy of blacklisting malignant devices from 42% to 82% as the number of servers increases from one to five for mixed attacks. It also achieves higher accuracies ranging between 47.2%-97.6% versus 47.4%-90.7% compared to the related work for Denial of Service (DoS) attacks. For this simulation with multiple servers, a novel solution for reducing the PER in a Rayleigh fading environment by searching optimal distributed server locations with a hybrid approach is also provided. The proposed protocol and the server location optimization algorithm are particularly suitable for the Industrial IoT (IIoT) in mitigating the effects of harsh communication environments in manufacturing facilities.