Levy Flight – Pelican Optimization Algorithm Based Refined Long Short-Term Memory for Network Intrusion Detection System
Keywords:
Intrusion Detection System, Levy Flight, Pelican Optimization Algorithm, Refined Long Short-Term Memory and SecurityAbstract
Intrusion Detection System (IDS) is a method of observing and tracking events on computer systems, which is utilized for identifying signs of security problems and activities monitored by event-based methods and security data. Network IDS (NIDS) is performed intrusion detection by partial packet data of fixed size, but the existing methods suffer to maximize the detection rate and reduce the false alarm rate. In this research, a Levy Flight – Pelican Optimization Algorithm (LF-POA) based Refined Long Short-Term Memory (RLSTM) is proposed for network intrusion detection. The datasets used for evaluating the proposed method are CIC-IDS 2017, UNSW-NB15, NSL-KDD and Bot-IoT. One-Hot encoding and min-max normalization methods are used as pre-processing techniques, while the feature selection process is performed by POA which is enhanced by Levy flight. The RLSTM method is used for classifying the intrusion as normal or attack. The performance of the proposed technique is analyzed on the basis of accuracy, precision, recall, f1-score, detection rate and false alarm rate. The proposed method attains a detection rate of 99.75%, 95.31%, 98.25% and 93.94% on CIC-IDS 2017, UNSW-NB15, NSL-KDD and Bot-IoT datasets, respectively. The proposed technique performs better than other existing techniques like Convolutional Neural Network – Long Short-Term Memory (CNN-LSTM) and AdaBoost based method.
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CIC-IDS 2017 dataset: https://www.kaggle.com/datasets/cicdataset/cicids2017/data
UNSW-NB15 dataset: https://research.unsw.edu.au/projects/unsw-nb15-dataset
NSL-KDD dataset: https://www.kaggle.com/datasets/hassan06/nslkdd
Bot-IoT dataset: https://research.unsw.edu.au/projects/bot-iot-dataset
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