Adaptive Intrusion Detection System Using Deep Learning for Network Security
Ali Fenjan, Mohammed Thakir Mahmood Almashhadany, Saadaldeen Rashid Ahmed, Hala Adnan. Fadel, Ravi Sekhar, Pritesh Shah, B. S. Veena
Abstract
Cybersecurity threats are significant risks to the networks of today; hence, information security solutions like intrusion detection systems (IDS) are urgent to guard against undesired activities. The methodologies utilized for the purpose of IDS in the past failed to be ahead of the game in relation to adjusting to the ever-evolving threats. The need for sophisticated techniques is evident in this piece of information. In this study, the lack of study representation is addressed. Here, we offer the deep learning based Adaptive Intrusion Detection System (IDS) framework, which utilizes several algorithms, including CNNs, ANNs, and MLPs. Thus, our strategy tries to improve the accuracy level of detectors and the scalability of aircraft autonomy mechanisms while simultaneously tackling future dangers. The model is assessed on a data set that has varied network traffic scenarios. We receive promising findings as the system can classify scenarios with 96% accuracy. This validation of the fact supports our proposal of featuring a way of defense against advanced cyber activities.