TY  - EJOU
AU  - Al-kahtani, Mohammad S. 
AU  - Mehmood, Zahid 
AU  - Sadad, Tariq 
AU  - Zada, Islam 
AU  - Ali, Gauhar 
AU  - ElAffendi, Mohammed 

TI  - Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model
T2  - Intelligent Automation \& Soft Computing

PY  - 2023
VL  - 37
IS  - 2
SN  - 2326-005X

AB  - 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 flag 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.
KW  - Cyber security; deep learning; intrusion detection; PSO-GA; CICIDS-2017; intelligent system; security and privacy; IoT

DO  - 10.32604/iasc.2023.037673