Intrusion Detection Systems for Modern Cybersecurity: Techniques, Datasets, and Open Research Challenges

Security has emerged as a huge issue in the present world due to the advancement in Information Technology and the use of cyberspace. Intrusion Detection Systems (IDS) are very important in the process of ensuring that the network traffic is monitored and any malicious activity is detected in order to protect the network. This paper is an attempt to present a detailed study of IDS, with its classification, detection techniques, methods, datasets, issues, and developments. IDS can be generally classified based on the type of data they use and the type of detection techniques they employ which include signature based IDS, anomaly based IDS, and hybrid IDS. These new technologies such as ML, DL, and AI are improving the performance and flexibility of IDS in detecting new and unknown attacks including zero-day threats. The paper also provides an overview of some of the most common datasets like KDD Cup 99, NSL-KDD, and CICIDS2017 and stress the lack of modern real-world datasets. Some of the major issues include high false positives, scalability, privacy, and evasion techniques are discussed. Besides, the research considers the future developments such as AI-based IDS, cloud and edge computing, explainable AI, and real-time detection. There is the need to carry out further studies and evaluation for the development of novel and efficient IDS models that will address the changing threats and at the same time ensure privacy.