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@article{186697,
        author = {Miss.Khare Vishakha G and Mr.Abhale B.A. and Mr. Pathare.G. N and Miss.Jadhav Samiksha V and Miss.Kaklij Sakshi B and Miss.Khare Vishakha G and Miss.Gangurde Shreya S},
        title = {Cybersecurity Intrusion Detection System Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {2467-2474},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186697},
        abstract = {This project develops an advanced Intrusion Detection System (IDS) by combining real time attack simulations run on Kali Linux with the CIC-IDS2017 dataset. Machine learning models for precise intrusion detection are developed and evaluated using the CIC-IDS2017 dataset, which includes labeled network traffic data covering a range of attack types and typical activities. To supplement this, Kali Linux is used to create more realistic attacks like scanning ports, brute force, and denial-of-service (DoS) in a controlled seFng. To improve the machine learning models, network traffic is recorded and analyzed during these attacks. Combining real- world, live attack data with thorough dataset training improves the system's capacity to identify a variety of malicious activity with low false positives and high accuracy. By successfully detecting changing cyberthreats in dynamic environments, this hybrid approach provides a scalable and adaptable intrusion detection system (IDS) that improves network security.},
        keywords = {Intrusion Detection System, machine learning, CIC-IDS2017 dataset, Kali Linux, real-time attack simulation, network security, port scanning, denial of service, brute force attack, traffic analysis, cybersecurity, anomaly detection.},
        month = {November},
        }