Investigate Discriminative AutoEncoder in Few-shot Learning-based Anomaly Detection
Abstract
Discriminative AutoEncoder (DisAE) plays a crucial role in enhancing the adaptability and gener- alization of few-shot learning methods (DisAEFL) for detecting rare anomalies. DisAE captures meta- knowledge from multiple known tasks, facilitating rapid adaptation in DisAEFL. Key factors like the discriminative parameter (a) and normal proportion (pn) significantly impact DisAEFL performance. However, their influence on the DisAE manifold and DisAEFL’s efficacy in rare cyberattack detection remain understudied in cybersecurity. This study presents an investigative approach to probe DisAE’s influence on DisAEFL’s performance in addressing rare, unseen cyberattacks, aiming to gain insight into the DisAE manifold and outline future research directions. Through intensive analysis, we focus on parameters a and pn, detailing how to examine them to observe DisAE’s effects on DisAEFL. Two main experiments are conducted to investigate their influences. Experimental results on the NSL-KDD dataset reveal a strong correlation between these parameters and both the DisAE manifold and DisAEFL performance. These findings suggest strategies for more efficiently constructing the DisAE manifold to enhance DisAEFL’s adaptability and generalization. Overall, this study contributes to advancing anomaly detection methodologies in cybersecurity by shedding light on the interplay between DisAE, DisAEFL, and crucial parameters.
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DOI: http://dx.doi.org/10.21553/rev-jec.375
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