An Advanced Network Intrusion Detection System based on Deep Learning Models

Due to the rapid growth of the technologies in networks and bulk amount of network traffic, the risk of cyberattacks has increased. Traditional signature-based security systems are inadequate to countermeasure these attacks. In order to improve the identification of hostile activity in network traffic, this study offers an intelligent Network Intrusion Identification System that combines machine learning & deep learning approaches. Using packet analysis tools, the suggested system records real-time network packets and extracts features and preprocesses structured data for efficient model training. To categorize network behaviour as normal or intrusive, there are many machine learning & deep learning models are deployed utilizing Python-based frameworks. The experimental evaluation shows that deep learningbased models obtain better detection accuracy and lower false-positive values than conventional machine learning techniques Scalability, versatility, and suitability for real-time deployment are guaranteed by the system's modular architecture. The findings show that the suggested strategy offers a reliable and effective way to protect contemporary network infrastructures from changing cyber threats.