| Title | Securing SCADA Systems against Cyber-Attacks using Artificial Intelligence |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Aldossary, Lina Abdulaziz, Ali, Mazen, Alasaadi, Abdulla |
| Conference Name | 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) |
| Date Published | sep |
| Keywords | AI, Artificial neural networks, Bi-LSTM, compositionality, DBN, FNN, GRU, Human Behavior, IDS, Intrusion detection, machine learning algorithms, ML, pubcrawl, resilience, Resiliency, RNN, SCADA, SCADA systems, SCADA Systems Security, Software algorithms, Technological innovation, Time series analysis |
| Abstract | Monitoring and managing electric power generation, distribution and transmission requires supervisory control and data acquisition (SCADA) systems. As technology has developed, these systems have become huge, complicated, and distributed, which makes them susceptible to new risks. In particular, the lack of security in SCADA systems make them a target for network attacks such as denial of service (DoS) and developing solutions for this issue is the main objective of this thesis. By reviewing various existing system solutions for securing SCADA systems, a new security approach is recommended that employs Artificial Intelligence(AI). AI is an innovative approach that imparts learning ability to software. Here deep learning algorithms and machine learning algorithms are used to develop an intrusion detection system (IDS) to combat cyber-attacks. Various methods and algorithms are evaluated to obtain the best results in intrusion detection. The results reveal the Bi-LSTM IDS technique provides the highest intrusion detection (ID) performance compared with previous techniques to secure SCADA systems |
| DOI | 10.1109/3ICT53449.2021.9581394 |
| Citation Key | aldossary_securing_2021 |
Securing SCADA Systems against Cyber-Attacks using Artificial Intelligence