Network Intrusion Detection System using ML

Pyla Jyothi*, Chinthalapudi Suneela**, Dindi Sowmya***, Gollangi Mohan****, Badireddi Haranadha Sai*****
*-***** Department of Computer Science and Engineering, Maharaj Vijayaram Gajapathi Raj College of Engineering, Andhra Pradesh, India.
Periodicity:January - June'2024
DOI : https://doi.org/10.26634/jwcn.12.2.20600

Abstract

In today's fast-paced technological landscape, advancements are happening at the speed of light. Every aspect of the surroundings is intricately linked to development and technology. AI, cloud computing, and the IoT reign supreme in the realm of technology. In the realm of cyberspace, everything is interconnected through computers and networking devices. With the increasing reliance on computers and the internet, ensuring safety becomes ever more critical. Safeguarding network architecture and the proper use of networking devices and tools play a vital role in cybersecurity. This study has developed a system called Network Intrusion Detection System using ML, which is suitable for home network environments. This paper has created an application that makes a significant impact on real-time network environments by providing security for a particular home network. Leveraging ML in networks has improved the results by providing accuracy and efficiency. The algorithm of Logistic Regression is used to demonstrate network behavior and classify network traffic as either in an "Attack" or "Benign" state. This helps in detecting suspicious activities across the network and can prevent them at a later stage.

Keywords

Machine Learning, Network Intrusion Detection System (NIDS), Logistic Regression, T Shark, Flow Meter.

How to Cite this Article?

Jyothi, P., Suneela, C., Sowmya, D., Mohan, G., and Sai, B. H. (2024). Network Intrusion Detection System using ML. i-manager’s Journal on Wireless Communication Networks, 12(2), 29-37. https://doi.org/10.26634/jwcn.12.2.20600

References

[2]. Al Lail, M., Garcia, A., & Olivo, S. (2023). Machine learning for network intrusion detection—A comparative study. Future Internet, 15(7), 243.
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