Enhancing Industrial Security with IoT-based Passive Intrusion Detection and Segmentation

Abstract

Introduction: passive intrusion detection in industrial environments can be challenging, especially when the area being monitored is vast. However, with the advent of IoT technology, it is possible to deploy sensors and devices that can help with mass segmentation of passive intrusion. Hence, this approach deploys ML (Machine Learning) algorithm as improvised (Convolutional Neural Network) CNN support for identifying and avoid illegal access to critical areas in real time, ultimately improving security and safety in industrial environments. Methods: in turn the proposed algorithm can detect patterns and anomalies that could indicate a passive intrusion. In order to discover the patterns and connections between the various sensor data points, DL (Deep Learning) techniques like CNNs, Recurrent Neural Networks (RNNs), and Autoencoders (AE) may be trained on massive datasets of sensor data. Results: then, the robust technique DL (Deep Learning) can be utilized for ID (Intrusion Detection) the industrialized settings, when specifically combined with other IoT devices like sensors and alert systems. Thus, the model is trained and tested. Finally, it achieved 98,51 % and 94,85 % accuracy accordingly. Conclusion: these frameworks after the completing training phase can be employed for the novel sensor data’s actual analysis and also for the anomalies detection as it reveals a potential ID.