Parallel ABILSTM and CBIGRU Ensemble Network Intrusion Detection System

Abstract

Perpetual improvements are happening in communication networks, both in hardware and software. This constant improvement provides better communication speeds and improves the user experience. The concept of smart cities is a boon that evolves towards the complete automation of the city and the responsible conservation of natural resources. Modern communication technologies support the gradual growth of smart cities simultaneously; the changes in the network architectures open a gateway to attackers, making the cyber network vulnerable. Manual construction and enhancement of network security schemes and protocols are tedious, time-consuming processes that may not be applicable in real time. This dynamic non-deterministic problem can be solved by combining the combination of Artificial Intelligence techniques that can do marvels in anomaly detection in a typical cyber network behaviour. This work, named parallel ABILSTM and CBIGRU ensemble network intrusion detection system (PACENIDS), is intended to use an ensemble of Altered Bi-directional Long Short-Term Memory (ABILSTM) and Customized Bi-directional Gated Recurrent Unit (CBIGRU) to improve the detection of real-time network intrusion attempts with more accuracy and to prevent the network from intimidating attacks on time. The parallel operational nature of the proposed algorithm ensures a swifter performance towards attack detection. This paper uses an impact based fuzzy feature selection algorithm to improve the performance of the proposed approach. The NSL-KDD dataset is used to evaluate the suggested approach. The proposed PACENIDS achieves 96.59% for binary classification, 94.47% and 97.67% for multiclass without and with feature selection, respectively. The experimental result shows that the suggested ensemble approach increases accuracy and precision and reduces the false alarm rate in the target intruder detection system