Intelligent Intrusion Detection System for Intranet Security Based on Machine Learning and Behaviroal Analystics

Registration ID: IJNRD_324341

Published ID: IJNRD2605087

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Eppakayala Manoj Kumar, Gande Sadvik, Eluri Chandra Sheker, Mrs. R. Prathiba, Ganagoni Pranay

Machine Learning, Intrusion Detection, Behavior-Based Attacks, Cyber Security, Network Security, Intranet Attack, Zeek IDS, Feature Engineering (FE)

In today’s digital age, security challenges threaten user privacy as networks face increased vulnerability to malicious attacks due to large data volumes. Intrusion Detection Systems (IDS) play a crucial role in identifying cyber-attacks and protecting system resources and users. This study utilizes machine learning classifiers (MLC) to analyze the NSL-KDD dataset, optimizing by preprocessing to remove irrelevant features. System performance is assessed using four attribute subsets, comparing model accuracy across DoS, Probe, U2L, and R2L attack classes to determine the best algorithm for each class. Using Forest with 20 features successfully achieved an accuracy of up to 99% in intrusion detection. In the cybersecurity landscape, detecting intranet attacks remains particularly challenging as hostile tactics constantly adapt and evolve. This research introduces an innovative machine learning approach that identifies potential threats by analyzing behavioral patterns rather than relying on fixed signatures. The methodology harnesses advanced algorithms to recognize and counter intranet-based attacks by identifying anomalous behaviors that deviate from established usage patterns. By examining network traffic and system logs, our model differentiates between normal and suspicious activities, enabling it to detect and respond to threats proactively. This approach shows significant promise for strengthening intranet security through its real-time monitoring capabilities and adaptive defense systems. The solution enhances security posture by continuously analyzing behavior patterns and identifying potential threats before they can cause damage. Our empirical evaluations and comparative analyses confirm the model's effectiveness. Test results demonstrate how it successfully identifies anomalies that traditional security measures might miss, while maintaining a low rate of false positives. This technology complements existing cybersecurity frameworks rather than replacing them, providing an additional layer of protection

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Eppakayala Manoj Kumar, Gande Sadvik, Eluri Chandra Sheker, Mrs. R. Prathiba, & Ganagoni Pranay (May-2026). Intelligent Intrusion Detection System for Intranet Security Based on Machine Learning and Behaviroal Analystics . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(5), a801-a805. https://ijnrd.org/papers/IJNRD2605087.pdf

Volume 11 Issue 5, May-2026

Pages : a801-a805

Paper Reg. ID: IJNRD_324341

Published Paper Id: IJNRD2605087

Research Area: Other area not in list

Author Type: Indian Author

Country: Chennai, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2605087.pdf

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