Soft Computing Methods for Resolving Network Security Optimization Issues

Abstract

Various algorithms that mimic human knowledge and cognitive capacities are called soft computing. The essential elements of the soft computing approach fuzzy logic, genetic algorithms, artificial neural networks, and probabilistic logic systems are thoroughly reviewed in this study. The intrusion detection system community is adopting these strategies more frequently because of their capacity to streamline intricate procedures. One of its main advantages is soft computing's ability to detect and respond to cybercrimes without established signatures and identify known and unknown intrusions. The increasing incidence of cybercrime attacks highlights the criticality of building effective intrusion detection systems to protect vital information systems.

In particular, this work explores soft computing techniques to identify unexpected incursions. It presents actual data demonstrating these techniques' superior performance over conventional intrusion detection techniques. Soft computing techniques can help organizations lower the risk of security breaches and improve their ability to respond to changing threats. The paper also addresses how soft computing techniques can be flexible enough to change with evolving cyber threats. Our findings show that incorporating soft computing into intrusion detection systems enhances their ability to defend networks against highly skilled intrusions. Through our inquiry, we want to demonstrate the significance of these methods in strengthening information security protocols.