Researchers at Clemson University (led by Dr. Mashrur “Ronnie” Chowdhury) have developed in-vehicle security (IVS) software for detecting cyber-attacks in the in-vehicle network. IVS is developed using software-defined networking and artificial intelligence concepts to detect cyber attacks. The software consists of a long-short term memory (LSTM) neural network model used for detecting cyber-attacks. The software can be deployed inside an in-vehicle electronic control unit (ECU). It continuously monitors the in-vehicle network data stream and detects cyber-attacks that alter the contents of the network data packets through remote access to ECUs. While monitoring, the software trains the model with the recent data and updates the model parameters. The unique aspect of the software is that it considers the in-vehicle network data as correlated time series data and the LSTM model can detect anomalies effectively in a time series data containing longer term patterns. The software was tested on two real-world CAN bus datasets and two types of attacks: replay attack and amplitude-shift attack and showed superior performance compared to baseline detection models. The analysis and findings related to the IVS software has been published in the IEEE Sensors Letters journal. You can find the article in the following website https://ieeexplore.ieee.org/document/9091063.