Intelligent Security Algorithm to Avoid Any Intrusions

Modern cybersecurity ecosystems have been impacted significantly by sophisticated cyberattack strategies; including polymorphic malware, zero-day exploits and insider attacks, all of which are complicated even more so by the widespread proliferation of  Internet of Things (IoT) technology. Many traditional IDS platforms have difficulty keeping pace with the dynamic evolution of threats, primarily due to two major shortcomings: their inability to recognize new attack patterns and the unreasonably high false positive rate at which they alert security teams. To address those shortcomings, this study introduces a Robust Intelligent Security Algorithm (RISA). By integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks this hybrid deep learning architecture extracts spatial features and captures temporal patterns from data traffic to detect multi-step attacks. Results from experimental testing using the UNSW-NB15 dataset demonstrate that the proposed RISA has an accuracy of 98.5%; a precision of 98.1% and a recall of 98.6%, resulting in an F1 Score of 98.35%. Furthermore, the proposed model exhibits a very low false positive rate (1.7%) and an average inference time of 0.04 milliseconds. Therefore, the results clearly illustrate the ability of the proposed algorithm to accurately detect threats in real-time, while being operationally efficient; making it suitable for deployment in modern fast-paced network environments.