| Title | Feature Selection for Attacker Attribution in Industrial Automation amp; Control Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Jha, Ashish, Novikova, Evgeniya S., Tokarev, Dmitry, Fedorchenko, Elena V. |
| Conference Name | 2021 IV International Conference on Control in Technical Systems (CTS) |
| Keywords | attacker attribution, attacker profile, Attribute selection, attribution, Automation, composability, control systems, feature extraction, Human Behavior, Linux OS, Metrics, pubcrawl, Safety, security, security events, statistical analysis, system events, Transportation |
| Abstract | Modern Industrial Automation & Control Systems (IACS) are essential part of the critical infrastructures and services. They are used in health, power, water, and transportation systems, and the impact of cyberattacks on IACS could be severe, resulting, for example, in damage to the environment, public or employee safety or health. Thus, building IACS safe and secure against cyberattacks is extremely important. The attacker model is one of the key elements in risk assessment and other security related information system management tasks. The aim of the study is to specify the attacker's profile based on the analysis of network and system events. The paper presents an approach to the selection of attacker's profile attributes from raw network and system events of the Linux OS. To evaluate the approach the experiments were performed on data collected within the Global CPTC 2019 competition. |
| DOI | 10.1109/CTS53513.2021.9562879 |
| Citation Key | jha_feature_2021 |
Feature Selection for Attacker Attribution in Industrial Automation amp; Control Systems