A novel anomaly detection system on the internet of railways using extended neural networks

• Main Authors: Islam, Umar, Malik, Rami Qays, Al-Johani, Amnah S., Khan, Muhammad Riaz, Daradkeh, Yousef Ibrahim, Ahmad, Ijaz, Alissa, Khalid A., Abdul-Samad, Zulkiflee, Tag-Eldin, Elsayed M.
• Format: Article
• Published: MDPI 2022
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.um.edu.my/41136/

The Internet of Railways (IoR) network is made up of a variety of sensors, actuators, network layers, and communication systems that work together to build a railway system. The IoR's success depends on effective communication. A network of railways uses a variety of protocols to share and transmit information amongst each other. Because of the widespread usage of wireless technology on trains, the entire system is susceptible to hacks. These hacks could lead to harmful behavior on the Internet of Railways if they spread sensitive data to an infected network or a fake user. For the previous few years, spotting IoR attacks has been incredibly challenging. To detect malicious intrusions, models based on machine learning and deep learning must still contend with the problem of selecting features. k-means clustering has been used for feature scoring and ranking because of this. To categorize attacks in two datasets, the Internet of Railways and the University of New South Wales, we employed a new neural network model, the extended neural network (ENN). Accuracy and precision were among the model's strengths. According to our proposed ENN model, the feature-scoring technique performed well. The most accurate models in dataset 1 (UNSW-NB15) were based on deep neural networks (DNNs) (92.2%), long short-term memory LSTM (90.9%), and ENN (99.7%). To categorize attacks, the second dataset (IOR dataset) yielded the highest accuracy (99.3%) for ENN, followed by CNN (87%), LSTM (89%), and DNN (82.3%).