Multi-source Data Fusion for Real-time Cybersecurity Situational Awareness and Visualization

Building situational awareness systems that can collect and evaluate data from several sources in real time is crucial as cyber threats become more complex. Sensor fusion, probabilistic reasoning, graph theory, and information theory are all brought together in the innovative mathematical model known as Multi-Source Fusion-Based Cybersecurity Situational Awareness (MF-CSSA). This model is designed to give a complete framework for the visualization and awareness of real-time cybersecurity scenarios. Scalability, accuracy, reaction time, false positive rate, and situational awareness score are the five key performance indicators (KPIs) that we used in order to simulate and assess the proposed model’s architecture. Scalability is the level of scalability that the architecture has. A hundred iterations of the simulation were carried out, and the results indicate that MF-CSSA regularly outperforms the baseline techniques. By combining data and using probabilistic reasoning, the model is able to predict hostile activities in a consistent manner, as shown by the fact that it has an average accuracy of 88% when detecting threats. Having the ability to function in real time is very important, as shown by the fact that its average reaction time is 2.1 seconds. By doing so, it stops attacks from happening before they ever happen. By achieving an average score of 0.82 on the situational awareness scale, the model demonstrates that it is capable of recognizing and contextualizing cybersecurity scenarios. Another way of putting it is that it is able to swiftly adjust to new dangers. The model has a low false positive rate of just 8%, which is in addition to considerably decreasing the amount of work that analysts have to put in and alert fatigue. Last but not least, MF-CSSA is able to develop and handle a significant amount of traffic since it can process around 110 events per second. Because of this, it is a good option for huge networks that experience significant levels of information flow. It is clear from these data that the MF-CSSA architecture is a way of real-time cyber threat defense that is not only intelligent but also practical and provides accurate results.