Integrating Bi-Directional LSTM and Swarm Intelligence for Dynamic Cyber Threat Prediction

Abstract

Cyber threat prediction is a critical area in cybersecurity where timely and accurate identification of threats can prevent significant damages. This paper proposes a novel approach integrating Bi-Directional Long Short-Term Memory (Bi-LSTM) networks with Swarm Intelligence (SI) techniques for dynamic cyber threat prediction. Bi-LSTM networks are chosen for their ability to capture long-term dependencies in sequential data, which is crucial in cyber threat analysis. Swarm Intelligence methods, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), are utilized to optimize the parameters of the Bi-LSTM model, enhancing its predictive capabilities in dynamic environments.