| Title | Artificial Intelligence Empowered Cyber Threat Detection and Protection for Power Utilities |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Hasan, Kamrul, Shetty, Sachin, Ullah, Sharif |
| Conference Name | 2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC) |
| Date Published | dec |
| Keywords | advanced persistent threat, Advanced Persistent Threat (APT), AI, APT, APT attack, artificial intelligence, Big Data, computer security, cyber threat detection, cyber threats, Human Behavior, IDPS systems, Intrusion Detection and Prevention System, intrusion detection and prevention systems, learning (artificial intelligence), machine learning, Malware, Metrics, ML techniques, NIST, power engineering computing, Power system protection, power utilities, pubcrawl, resilience, Resiliency, Scalability, security of data, Smart grid, Smart grids, smart power grids, Tools |
| Abstract | Cyber threats have increased extensively during the last decade, especially in smart grids. Cybercriminals have become more sophisticated. Current security controls are not enough to defend networks from the number of highly skilled cybercriminals. Cybercriminals have learned how to evade the most sophisticated tools, such as Intrusion Detection and Prevention Systems (IDPS), and Advanced Persistent Threat (APT) is almost invisible to current tools. Fortunately, the application of Artificial Intelligence (AI) may increase the detection rate of IDPS systems, and Machine Learning (ML) techniques can mine data to detect different attack stages of APT. However, the implementation of AI may bring other risks, and cybersecurity experts need to find a balance between risk and benefits. |
| DOI | 10.1109/CIC48465.2019.00049 |
| Citation Key | hasan_artificial_2019 |
Artificial Intelligence Empowered Cyber Threat Detection and Protection for Power Utilities