Enhancing Intrusion Detection Systems with Ai: Examine the Integration of Ai into Traditional Ids to Improve Detection Rates and Reduce False Positives
Keywords:
Intrusion Detection System, Artificial Intelligence, Machine Learning, Cybersecurity, False Positives, Deep Learning, Network SecurityAbstract
These days, when cyber risks are more frequent and powerful, traditional intrusion detection systems (IDS) are lacking in stopping new and sophisticated attacks. Although signature-based and anomaly-based IDS have strong basics, they usually generate many misleading alarms and do not react quickly to new threats. Integrating AI, specifically Machine Learning (ML) and Deep Learning (DL), in IDS is valuable in improving threat detection and the system’s response. This paper aims to explore how AI is helping IDS systems achieve better results and fewer fake signals. A comparison of Support Vector Machines (SVM), Random Forest (RF), and Convolutional Neural Networks (CNN) reveals that using AI can make IDS more effective than non-AI-based IDS when tested using benchmark datasets NSL-KDD and CICIDS2017 (Shone et al., 2018; Ring et al., 2019). By blending anomaly identification with intelligent classification and adaptive learning, the new architecture can identify zero-day attacks more accurately. Models are evaluated using precision, recall, F1-score, and detection latency. The latest results show that our approach could reduce false positives by 35% and lead to more true positives. It additionally demonstrates events with bar graphs, pie charts, and system diagrams that evidence the changes in performance and architecture. It adds helpful knowledge to intelligent cybersecurity and offers valuable advice for using AI-driven IDS within enterprises. Future research will investigate federated learning and reinforcement learning to help improve the scalability and privacy of IDS algorithms.
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