Implementing AI-Driven Intrusion Detection System with Python and Light Connect Object

Author(s)	Raoui Mouad, Naja Najib, Abdellah
Country	Morocco
Abstract	The escalating number of cybersecurity threats poses significant challenges for ensuring the security of networked systems. Intrusion Detection Systems (IDS) play a vital role in detecting and preventing malicious activities. This paper focuses on the implementation of an AI-driven IDS using Python and Light Connect Object (LCO) to enhance the detection capabilities and improve the overall security of networked systems. By integrating AI techniques into the IDS framework, we aim to effectively identify both known and unknown attacks. The proposed system is evaluated using real- world network traffic data, and its performance is measured using metrics such as detection accuracy and false positive rate. The results demonstrate the effectiveness and practicality of the AI-driven IDS in enhancing network security
Keywords	LCO: light connect object, IDS: Intrusion Detection Systems, AI: Artificial Intelligence
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 6, Issue 1, January-February 2024
Published On	2024-02-08
Cite This	Implementing AI-Driven Intrusion Detection System with Python and Light Connect Object - Raoui Mouad, Naja Najib, Abdellah - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.13167
DOI	https://doi.org/10.36948/ijfmr.2024.v06i01.13167
Short DOI	https://doi.org/gtg6n7

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