An Advanced Multimodal Deep Learning Framework with Self-Attentive Multi-Layer Perceptron’s for Mitigating Multi-vector Cyber Attacks

The multi-vector cyberattacks are dynamic and are becoming more sophisticated, and the traditional security systems cannot easily detect and prevent them in real-time. The given paper is a proposal of a further Multimodal Deep Learning Architecture (MDLA) using self-attentive Multimodal Multi-Layer Perceptrons (MLPs) to detect and avert a broad spectrum of cyber threats. The application is also meant to handle and analyze various forms of data like network traffic, system event logs and user activity patterns. It allows users to have an in-depth insight into the nature of cyberattacks on different channels. The main novelty of the proposed model is that it entails the introduction of a self-attention mechanism into a multi-layer perceptron (MLP) framework. This is a characteristic that allows the model to attentively concentrate on the most important features and characteristics of complex and higher dimensional data whichopy such that it is more able to detect minor attack fingerprints which may have not been noticed before. Also, the multi-layered filtering technique of noise is applied in a gradual stage-by-stage fashion to extricate valuable representations at every level of processing. It makes the process more effective and precise. The findings indicate that the framework performs better than the conventional deep-learning models, thereby recalling, F1 score, and improving accuracy and lowering the false-positive rates when evaluated on standard benchmark datasets. The proposed solution is more scalable and flexible, which makes it a good proposal to apply in the complex and dynamic cybersecurity settings. Overall, this paper has shown that by combining multimodal data processing, deep learning, and attention processes, it is possible to implement intelligent, automated, and reliable cyber protection systems that might be able to respond to the threats.