Authors : M V V Gopala Krishna Murthy; D Lahari; Ch Lakshmi Pujitha; P Lakshmi Pranamya; T Harsha Tri Lakshmi; S Sai Durga Lavanya

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/3w5amvnd

Scribd : https://tinyurl.com/yuy27zpr

DOI : https://doi.org/10.38124/ijisrt/25apr945

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Abstract : The increasing number of cyber threats and security breaches has necessitated the development of intelligent, automated, and proactive cybersecurity mechanisms. This project focuses on designing and implementing an Intrusion Detection and Prevention System (IDPS) that leverages Machine Learning (ML) techniques to detect and prevent network intrusions in real-time. The system continuously monitors incoming network traffic, extracts meaningful features, and classifies it as normal or malicious using a trained Random Forest algorithm, ensuring high detection accuracy and minimal false positives. If an attack is detected, the system automatically blocks the attacker's IP address, preventing further malicious activity and enhancing network security. The backend is developed using Flask, while MySQL is utilized for storing attack logs, detected intrusions, and blocked IPs, ensuring an efficient and well-structured database management system. The user-friendly dashboard, designed with an intuitive UI, enables real-time monitoring and management of intrusion events, providing detailed logs and analytics to help security administrators analyze attack patterns and refine network defenses. The system is trained using 17 critical network features, allowing it to differentiate between normal and anomalous traffic with high precision. It is designed to function efficiently in large-scale network environments, making it suitable for organizations, enterprises, and cloud-based infrastructures that require robust cybersecurity measures. Additionally, the integration of automated response mechanisms ensures that threats are mitigated instantly without manual intervention, significantly reducing the risk of security breaches. The implementation of machine learning algorithms such as Support Vector Machine (SVM), Random Forest enhances the system’s ability to adapt to evolving cyber threats. This IDPS system not only provides real-time threat prevention but also contributes to cybersecurity intelligence by offering insights into intrusion trends, attacker behavior, and potential vulnerabilities in the network. With cyberattacks becoming increasingly sophisticated, the need for such an advanced intrusion prevention mechanism is more crucial than ever. This project represents a highly scalable, efficient, and reliable approach to proactive network defense, providing organizations with a powerful security solution to safeguard their digital assets from emerging cyber threat.

Keywords : Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Network Security, Machine Learning, Cyber Threat Detection, IP Blocking, Flask Web Application, Real-Time Monitoring, Automated Response, Anomaly Detection, Email Notification, MySQL Database.

References :

1. M. Belouch , S. El Hadaj , M. Idhammad,"A Two-Stage Classifier Approach Using REPTree Algorithm for Network Intrusion Detection",2017.
2. A. Iftikhar, M. Basheri, M. Javed Iqbal, A. Raheem, ''Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection'',2018.
3. Jitti Annie Abraham,V. R. Bindu,''Intrusion Detection and Prevention in Networks Using Machine Learning and Deep Learning Approaches: A Review'',2021.
4. Sibi Amaran, Ramalingam Madhan Mohan, Rethnaraj Jebakumar,” Optimal Machine Learning Based Intrusion Detection System in Wireless Sensor Networks for Surveillance Applications”,2022.
5. Ajmeera Kiran; S. Wilson Prakash; B Anand Kumar; Likhitha; Tammana Sameeratmaja; Ungarala Satya Surya Ram Charan ,“ Intrusion Detection System Using Machine Learning”,2023.
6. V. Ebenezer; Rosebel Devassy; G. Jaspher W. Kathrine,” Intrusion Detection and Prevention System to Analyse and Prevent Malware using Machine Learning”,2023.
7. Mona Esmaeili, Morteza Rahimi, Hadise Pishdast, Dorsa Farahmandazad, Matin Khajavi, Hadi Jabbari Saray ,“ Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security" ,2024.