سیستم تشخیص نفوذ هوشمند دارای یک حصار مجازی، تشخیص نفوذ فعال، طبقه بندی، ردیابی و تشخیص عمل

An intrusion detection system (IDS) is primarily used to protect nuclear power plants from external threats, such as sabotage and malicious attacks. However, earlier versions of IDSs are configured to detect an intrusion from visual inspection by an operator. This has the disadvantages of requiring standby human resources and relying on operator capabilities. In this paper, therefore, we propose an image-based intelligent intrusion detection system (IIDS) with a virtual fence, active intruder detection, classification, and tracking, and motion recognition to solve these limitations. An integrated acquisition device was manufactured combining optical and thermal cameras to compensate for the disadvantages of optical cameras, which have difficulty detecting an intrusion at night, under adverse weather conditions, and when the intruder is camouflaged. The virtual fence has a function to set the boundary between surveillance and external areas in a graphical user interface, and to define an early pre-alarm area if necessary. The background model is designed to detect moving objects, and detected objects are segmented into bounding boxes. We implemented a network model based on a convolutional neural network (CNN) to classify moving objects as either intruders or wild animals. If an intruder is detected in real time and is crossing the virtual fence, the alarm tile blinks with the associated color. Five types of intruder behavior patterns are recognized by optimizing a long-term recurrent convolutional network (LRCN) model. The proposed IIDS meets the physical protection requirements recommended in the nuclear regulatory guidelines, and can be used as an unmanned surveillance system. It is expected to perform more active and reliable intrusion detection in combination with existing sensors, such as microwaves, electric fields, and fence disturbance sensors in a nuclear power plant.