Biblio

Modbus TCP/IP protocol is a commonly used protocol in industrial automation control systems, systems responsible for sensitive operations such as gas turbine operation and refinery control. The protocol was designed decades ago with no security features in mind. Denial of service attack and malicious parameter command injection are examples of attacks that can exploit vulnerabilities in industrial control systems that use Modbus/TCP protocol. This paper discusses and explores the use of intrusion detection and prevention systems (IDPS) with deep packet inspection (DPI) capabilities and DPI industrial firewalls that have capability to detect and stop highly specialized attacks hidden deep in the communication flow. The paper has the following objectives: (i) to develop signatures for IDPS for common attacks on Modbus/TCP based network architectures; (ii) to evaluate performance of three IDPS - Snort, Suricata and Bro - in detecting and preventing common attacks on Modbus/TCP based control systems; and (iii) to illustrate and emphasize that the IDPS and industrial firewalls with DPI capabilities are not preventing but only mitigating likelihood of exploitation of Modbus/TCP vulnerabilities in the industrial and automation control systems. The results presented in the paper illustrate that it might be challenging task to achieve requirements on real-time communication in some industrial and automation control systems in case the DPI is implemented because of the latency and jitter introduced by these IDPS and DPI industrial firewall.

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.