Intrusion Detection System using Long Short-Term Memory and Fully Connected Neural Network on Kddcup99 and NSL-KDD Dataset
Keywords:
Intrusion Detection, LSTM, CNN, KDDCup99Abstract
Implementing intrusion detection models, which include locating and categorizing unauthorized access to a computer network or information system, often uses machine learning techniques. However, numerous difficulties occur as a result of the fact that cybercriminals constantly alter their attack techniques in response to the discovery of new system vulnerabilities. The number of efforts to harm is quickly increasing, and as a consequence, traditional methods cannot analyses massive amounts of data. Therefore, a comprehensive detection strategy that incorporates scalable solutions is necessary to solve the issue. A deep learning model is offered to solve the intrusion classification challenge properly. , Deep Learning (DL) algorithms have produced very accurate outcomes for handling various problems in practically various area. Deep learning methods such as LSTM (Long Short-Term Memory) and FCNN (Fully Connected Neural Network) categorize benign and malicious connections on intrusion datasets. A more precise categorization of multi-class assault patterns is the goal of this endeavor. When applied to five-class issues, the deep learning model that was suggested produces more accurate classifications. When run on the KDDCup99 dataset, it reaches an accuracy of 99.99%, and when run on the NSL-KDD dataset, it reaches 99.95%.In both data set our model secure maximum output.
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