Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model

Cybersecurity threats are increasing rapidly as hackers use advanced techniques. As a result, cybersecurity has now a significant factor in protecting organizational limits. Intrusion detection systems (IDSs) are used in networks to f lag serious issues during network management, including identifying malicious traffic, which is a challenge. It remains an open contest over how to learn features in IDS since current approaches use deep learning methods. Hybrid learning, which combines swarm intelligence and evolution, is gaining attention for further improvement against cyber threats. In this study, we employed a PSO-GA (fusion of particle swarm optimization (PSO) and genetic algorithm (GA)) for feature selection on the CICIDS-2017 dataset. To achieve better accuracy, we proposed a hybrid model called LSTM-GRU of deep learning that fused the GRU (gated recurrent unit) and LSTM (long short-term memory). The results show considerable improvement, detecting several network attacks with 98.86% accuracy. A comparative study with other current methods confirms the efficacy of our proposed IDS scheme.