ELEVATING SECURITY MEASURES IN AD HOC NETWORKS: AN INTRUSION DETECTION APPROACH WITH DEEP LEARNING
1Meenakshi Nawal, 2Medha Khenwar
1Associate Professor, Swami Keshwanand Institute of Technology, Management & Gramothan, Jaipur, India
2Assistant Professor, Suresh Gyan Vihar University, Jaipur, India
ABSTRACT: Ad hoc networks, characterized by their ability to spontaneously establish connections without infrastructure, are increasingly utilized in disaster relief and military operations due to the ubiquity of wireless technology. However, their decentralized nature renders them vulnerable to attacks, such as the black hole assault, where rogue nodes disrupt routing. This study employs computer modeling to simulate such attacks and proposes a novel intrusion detection system based on machine learning algorithms, particularly utilizing the VGG architecture. The system aims to categorize network packets as safe or dangerous, enabling the identification of intrusions. Through experimentation, it is demonstrated that this method shows promise across various classifiers and can adapt to evolving attack strategies. The need for robust detection mechanisms persists amidst continuous changes in attack methodologies.
Keywords: Intrusions, Adhoc, networks, Data Sets, Deep Learning.