Authors : P. M. N. V. V. Sarveswara Gupta; B. Venkateswarlu; S. Karthikeya; Dr. Mohan Kumar Chandol; V. G. Sai Sumanth

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://shorturl.at/2vd5z

Scribd : https://shorturl.at/ALbDR

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN2043

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : It is crucial to secure digital assets and networks against harmful activity in the linked world of today. Through the detection and mitigation of unauthorized access, malicious activity, and possible security threats, Intrusion Detection and Prevention Systems (IDPS) are essential to the protection of systems and networks. The development, approaches, technologies, difficulties, and future directions of intrusion detection and prevention systems are all covered in detail in this research paper. The study examines the advantages and disadvantages of several IDPS methodologies, such as hybrid, anomaly-based, and signature-based techniques. It also addresses how to improve the efficacy and efficiency of IDPS using cutting- edge methods like big data analytics, artificial intelligence, and machine learning. In addition, the study discusses and suggests possible solutions for the problems that IDPS faces, including false positives, evasion strategies, and scalability concerns. In order to assist academics, researchers, and practitioners with insights, it concludes by outlining future directions for study and development in the field of intrusion detection and prevention systems.

Keywords : Intrusion Detection and Prevention Systems, IDPS, Signature-based, Anomaly-based, Machine Learning, Artificial Intelligence, Big Data Analytics.

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