What are the main challenges and limitations of intrusion detection and prevention research?

1 Data Quality and Availability

One of the key challenges for IDP research is the lack of high-quality and realistic data sets for testing and evaluating IDP systems. Many existing data sets are outdated, incomplete, or unrealistic, as they do not reflect the current and diverse threats, network configurations, and user behaviors. Moreover, data sets are often difficult to obtain or share, due to privacy, security, or ethical concerns. Therefore, IDP researchers need to develop new methods and techniques for generating, collecting, and sharing data sets that are relevant, representative, and realistic.

2 Evaluation Metrics and Benchmarks

Another challenge for IDP research is the lack of standard and consistent metrics and benchmarks for measuring and comparing the performance and effectiveness of IDP systems. Different IDP systems may use different criteria, methods, and assumptions for detecting and preventing intrusions, which makes it hard to compare them objectively and fairly. Moreover, there is no widely accepted and comprehensive framework for evaluating the impact and trade-offs of IDP systems on network performance, security, and usability. Therefore, IDP researchers need to establish and agree on common and meaningful metrics and benchmarks for IDP systems, and apply them consistently and transparently.

3 Adaptability and Scalability

A third challenge for IDP research is the need to design and develop IDP systems that are adaptable and scalable to cope with the dynamic and complex nature of network environments and threats. IDP systems need to be able to detect and prevent new and unknown attacks, as well as adapt to changing network conditions, user behaviors, and policies. Moreover, IDP systems need to be able to handle large and heterogeneous data sources, as well as multiple and distributed network segments and devices. Therefore, IDP researchers need to explore and employ new techniques and technologies, such as machine learning, artificial intelligence, cloud computing, and edge computing, to enhance the adaptability and scalability of IDP systems.

4 Ethical and Legal Issues

A fourth challenge for IDP research is the ethical and legal implications of IDP systems, especially in relation to privacy, accountability, and liability. IDP systems may collect, process, and store sensitive and personal data from network users and devices, which may raise privacy concerns and risks. Moreover, IDP systems may take autonomous or semi-autonomous actions to prevent intrusions, which may affect the network functionality, availability, and integrity, as well as the rights and responsibilities of network owners, operators, and users. Therefore, IDP researchers need to consider and address the ethical and legal issues of IDP systems, and ensure that they comply with the relevant laws, regulations, and standards.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?