An Enhanced Framework for Intrusion Detection System using LSTM

The article presents a comprehensive model for the detection and categorization of network attacks via deep learning-based recurrent architectures. Security has been one of the most challenging issues for computer systems. Conversely, recent advancements in machine learning are influencing almost all facets of computer science, particularly cybersecurity. To evaluate this impact, we have created three distinct cybersecurity problems to illustrate the advantages of deep learning techniques. We analyzed their prospective uses for SPAM filtration, malware detection, and adult content identification using A proposed deep learning. This study presents the implementation of deep learning techniques for attack detection using Long Short-Term Memory (LSTM) networks. Our technique was assessed using benchmark datasets, KDD99 and NSL KDD, and our experiments demonstrate that the models attained maximal accuracy for training as well as test classifications. Experimental analysis and results of the proposed strategy in comparison to existing techniques and other prevalent AI along with deep learning methodologies.