Swarm-Based Intelligent Models for Developing Cybersecurity Frameworks with IDS

The need for real time and robust monitoring system has become most important with the exponential growth of networked physical and cyber threats. This paper focuses on the design and implementation of proposed system for a novel Intrusion Detection System with the help of swarm based intelligent model, which is capable of detecting the threats in real time to prompt timely responses by leveraging temporal data analytics. The main objective of this paper is to minimize the potential damages with timely threat identification by developing scalable models so that these models can process and analyse the real time data. To achieve this objective, we are proposing a multi layered framework by identifying temporal patterns to improve detection accuracy with low latency. The proposed approach focusing on the extraction of meaningful features from temporal time series data, so that it will help us in enabling dynamic threat identification in multiple domains. From this work, a novel algorithm for anomaly detection in view of high-speed data, an adaptive threshold mechanism will be considered to reduce false positives and a lightweight strategy to ensure capability for low latency applications. This model had been evaluated through ‘Swam-based LSTM” and compared with the existing model like vanilla LSTM and GRU models for temporal data analysis. All these models evaluated based on the data set kddcup99.