Intrusion detection systems (IDS) are tools that monitor network traffic and detect suspicious or malicious activity based on predefined rules or signatures. They are effective against known vulnerabilities, as they can alert the system administrators or security personnel when they encounter a match with a known attack pattern or behavior. However, IDS have some limitations and challenges, especially when applied to industrial automation and control systems (IACS). Some of these are:

Modern IDS do not recognize IACS devices by default, as they are designed for general-purpose IT networks and protocols. Therefore, they may generate false positives or negatives when dealing with IACS-specific devices, protocols, or traffic patterns. To overcome this, IDS need to be customized or adapted to the IACS environment and context, which may require additional expertise and resources.

They are not very inexpensive to design and deploy, as they require careful planning, configuration, testing, and maintenance. They also need to be integrated with other security tools and processes, such as firewalls, antivirus, patch management, incident response, etc. Moreover, they may introduce additional costs and risks, such as network performance degradation, data privacy issues, or legal liabilities.

They are not effective against unknown or zero-day vulnerabilities, as they rely on predefined rules or signatures that may not cover all possible attack scenarios or techniques. Therefore, they may fail to detect novel or sophisticated attacks that exploit new or undiscovered vulnerabilities. To mitigate this, IDS need to be complemented with other security measures, such as anomaly detection, threat intelligence, or machine learning.

They require a significant amount of care and feeding, as they need to be constantly updated, tuned, and monitored. They also generate a large amount of data and alerts, which may overwhelm the system administrators or security personnel. Therefore, they need to be supported by adequate tools and processes, such as data analysis, alert filtering, prioritization, correlation, or visualization.