Authors - Akshay Kumar, Sudhir Agarmore, Edidiong Akpabio, Kumar Gaurav, Nitesh Kumar, Aditya Mandal Abstract - Intrusion Detection Systems (IDS) has been seen to be an integral aspect of network security, where an extra layer of protection mechanisms may contribute to protection against different kinds of cyberattacks. With fast-evolving cyber threats, from simple malware to sophisticated zero-day attacks, continuous developments are required in IDS technologies. Traditional IDS models, such as signature-based detection, work better when applied to known threats but are pretty weak against emerging and unseen types of attacks. On the other hand, anomaly-based IDS models give pretty good results in finding unknown attacks, but most of them report a high false-positive rate. Finally, this work provides an overview of current state-of-the-art IDS methods and focuses on machine learning, deep learning, and hybrid models for intrusion detection. We further discuss the benefits and limitations of both the supervised and unsupervised learning algorithms and their applications in anomaly detection and pattern identification within network traffic. It investigates the applications of deep learning methodologies, such as CNNs and RNNs, within the IDS framework. These models give a high chance to work with complex data and detect various kinds of sophisticated attacks efficiently. Hybrid systems that combine traditional detection methods with machine learning demonstrate better accuracy and fewer false positives. Among the future directions of research that will be discussed are development challenges for the scalability of IDSs, real-time detection, and handling zero-day vulnerabilities for improving the efficiency of IDS in securing modern network infrastructures.
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