Comparative evaluation of machine learning models for intrusion detection in communication networks

Conventional power networks now have far more possibilities thanks to the development of smart grid technologies. They are, however, more vulnerable to wide range of advanced cyberattacks as a result of this development. The confidentiality and integrity of systems are under risk from such breaches, which might result in disastrous network breakdowns. Installing an intrusion detection system (IDS) is a crucial step in addressing these issues and guaranteeing safe and dependable operations in smart grid settings. This paper suggests an improved Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model in combination with Likely Point Particle Swarm Optimisation (PSO) to create a hybrid intrusion detection system. The suggested system's accuracy, intrusion detection rates, and false alarm minimisation are a carefully assessed. Modern communication networks have become more complicated due to incorporation of cutting-edge technology and diverse services, which has made it easier for cybercriminals to plan clever and flexible assaults. Because they lack dynamic and sophisticated defence mechanisms, traditional security systems frequently fall short in effectively thwarting these attacks. The creation of proactive, self-adaptive, and real-time security mechanisms is essential for protecting future communication infrastructures as well as smart grid frameworks. Using large datasets from real-world networks, the suggested hybrid approach combines the strengths of ensemble learning and artificial intelligence to more precisely detect and stop harmful activity. This method establishes a standard for next-generation intrusion detection techniques and guarantees strong and resilient network security by optimising feature selection using Likely Point PSO and utilising the predictive powers of sophisticated LSTM-RNNs.