Protecting and Mitigating Against Cyber Threats

8Intrusion Detection in Wireless Networks Using Novel Classification Models

Archith Gandla, Dinesh K., Vasu Gambhirrao^*, R. M. Krsihna Sureddi, Ramakrishna Kolikipogu and Ramu Kuchipudi

Chaitanya Bharathi Institute of Technology (A), Hyderabad, Telangana, India

Abstract

In the ever-expanding realm of computer networks, ensuring the integrity and security of digital communication has become paramount. The rapid evolution of cyber threats demands innovative solutions to safeguard critical information and systems. This project delves into the domain IDS, emphasizing on implementation of an effective mechanism to detect network-based intrusions. The project addresses the pressing need for robust intrusion detection by leveraging the capabilities of DL and ML technologies. The existing system consists of rule based procedures which cannot effectively detect innovative attacks from intruders. Additionally there are procedures based on ML or deep learning some of which have very good accuracy but computationally very expensive to implement, there are some procedures which are not resource intensive but they fail to generalize well on some classes of attacks. The proposed system aims to overcome the shortcomings of existing systems. It uses hybrid deep learning models like CNN, LSTM, transfer learning techniques etc. to detect and classify the attack with less resource intensive methodology in computer networks. The proposed system also aims to deploy the model.

Keywords: Network ...