ProgramsView all programs
From the news
- Chancellor Amma Addresses the Parliament of World’s Religions
- Amrita Students Qualify for the European Mars Rover Challenge
Publication Type : Conference Paper
Publisher : Inventive Communication and Computational Technologies,
Source : Inventive Communication and Computational Technologies, Springer Singapore, Singapore (2020)
ISBN : 9789811501463
Campus : Amritapuri
School : School of Engineering
Department : Electronics and Communication
Year : 2020
Abstract : At present, with the expansion of size of the internet, security plays a crucial role in computer networks. Also with the advancement of Internet of things, earlier technology like firewall, authentication and encryption are not effective in ensuring the complete security. This has lead to the development of Intrusion Detection Systems (IDS) which monitors the events in computer networks to recognize the threats that violates computer security. With the help of various machine learning algorithms we have carried out the implementation of IDS. Machine learning technique increases the accuracy of anomaly detection in real-time scenario. This work focuses on K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM), which classify the program behavior as intrusive or not. To prevent DoS (Denial-of-Service) attacks, a new method is implemented in this paper. The KNN classified data which provides malicious IP address are blocked in routers through Standard Access-list.
Cite this Research Publication : B. Bhanu Prakash, Kaki Yeswanth, M. Sai Srinivas, Balaji S., Y. Chandra Sekhar, and Aswathy K. Nair, “An Integrated Approach to Network Intrusion Detection and Prevention”, in Inventive Communication and Computational Technologies, Singapore, 2020.