Multimodal Deep Learning for Integrated Cybersecurity Analytics

EasyChair Preprint no. 14011

Abstract

In the rapidly evolving landscape of cybersecurity, the detection and mitigation of sophisticated cyber threats have become increasingly challenging. Traditional approaches to cybersecurity analytics often struggle to keep pace with the ever-growing volume and complexity of data generated from various sources. This paper proposes a novel approach to address these challenges by leveraging multimodal deep learning techniques for integrated cybersecurity analytics.

The proposed approach combines multiple data modalities, including network traffic data, log files, and system behavior data, to provide a comprehensive view and understanding of cyber threats. By employing deep learning algorithms, the model can effectively capture intricate patterns, correlations, and anomalies that may be indicative of malicious activities.

Furthermore, the integration of multimodal data enables the model to exploit the complementary nature of different data sources, thereby enhancing the accuracy and robustness of the cybersecurity analytics system. The use of deep learning also enables the model to adapt and learn from new and evolving threats, providing a more proactive and resilient defense mechanism.

Keyphrases: Cybersecurity, network, neural

@Booklet{EasyChair:14011,
  author = {Kaledio Potter and Dylan Stilinki and Selorm Adablanu},
  title = {Multimodal Deep Learning for Integrated Cybersecurity Analytics},
  howpublished = {EasyChair Preprint no. 14011},

  year = {EasyChair, 2024}}