LitCVit: A Lightweight Self-Supervised Contrastive Vision Transformer for Encrypted Malicious Traffic Detection

LitCVit is a lightweight AI model designed to detect malicious encrypted network traffic (data sent over secure connections) without needing to decrypt it or manually extract features. The model uses self-supervised learning (training where the AI learns patterns from unlabeled data) and vision transformers (a type of neural network architecture) to analyze patterns across multiple data packets and flows (sequences of related network communications) while running much faster than existing approaches, achieving 98% accuracy on test datasets.