Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification

Intrusion detection has been a vast-surveyed topic for many decades as network attacks are tremendously growing. This has heightened the need for security in networks as web-based communication systems are advanced nowadays. The proposed work introduces an intelligent semi-supervised intrusion detection system based on different algorithms to classify the network attacks accurately. Initially, the pre-processing is accomplished using null value dropping and standard scaler normalization. After pre-processing, an enhanced Deep Reinforcement Learning (EDRL) model is employed to extract high-level representations and learn complex patterns from data by means of interaction with the environment. The enhancement of deep reinforcement learning is made by associating a deep autoencoder (AE) and an improved flamingo search algorithm (IFSA) to approximate the Q-function and optimal policy selection. After feature representations, a support vector machine (SVM) classifier, which discriminates the input into normal and attack instances, is employed for classification. The presented model is simulated in the Python platform and evaluated using the UNSW-NB15, CICIDS2017, and NSL-KDD datasets. The overall classification accuracy is 99.6%, 99.93%, and 99.42% using UNSW-NB15, CICIDS2017, and NSL-KDD datasets, which is higher than the existing detection frameworks.