Centrala begrepp
Twin Auto-Encoder (TAE) model enhances cyberattack detection by transforming latent representations into separable ones, outperforming existing methods.
Sammanfattning
The article introduces the Twin Auto-Encoder (TAE) model for cyberattack detection. It addresses challenges in distinguishing between normal and malicious samples in latent representations. TAE transforms latent representations into separable ones, improving downstream attack detection models' performance. Extensive evaluations show TAE's superiority over state-of-the-art models on various datasets, especially on sophisticated attacks.
Index:
Introduction to Cyberattack Detection Systems (CDSs)
Representation Learning (RL) Importance in CDSs
Challenges with Latent Representations of Auto-Encoders (AEs)
Proposed Solution: Twin Auto-Encoder (TAE)
Architecture of TAE: Encoder, Hermaphrodite, Decoder
Transformation Operator in TAE for Separable Representations
Loss Function and Training Process of TAE
Experimental Settings: Datasets Used and Hyperparameters Configurations
Performance Analysis: Comparison with Existing Models and Machine Learning Algorithms
Statistik
"Experiment results show the superior accuracy of TAE over state-of-the-art RL models."
"Moreover, TAE also outperforms state-of-the-art models on some sophisticated and challenging attacks."