论文标题
通过对抗性学习和专家反馈来检测不规则的网络活动
Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback
论文作者
论文摘要
在许多学科中，异常检测是一项无处不在且具有挑战性的任务。随着至关重要的沟通网络在我们的日常生活中发挥作用，这些网络的安全对于社会平稳运作至关重要。为此，我们提出了一个新颖的自我监督的深度学习框架，用于无线通信系统中的异常检测。具体而言，CAAD在对抗性设置中采用对比度学习，以学习无线网络中正常和异常行为的有效表示。我们对CAAD进行了严格的性能比较与几种最先进的异常检测技术，并验证CAAD的平均性能提高了92.84％。此外，我们还增加了CAAD，使其能够通过新颖的对比学习反馈循环系统地结合专家反馈，以改善学习的表示形式，从而减少预测不确定性（CAAD-EF）。我们将CAAD-EF视为一种新颖，整体且广泛适用于异常检测的解决方案。
Anomaly detection is a ubiquitous and challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. To this end, we propose a novel self-supervised deep learning framework CAAD for anomaly detection in wireless communication systems. Specifically, CAAD employs contrastive learning in an adversarial setup to learn effective representations of normal and anomalous behavior in wireless networks. We conduct rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques and verify that CAAD yields a mean performance improvement of 92.84%. Additionally, we also augment CAAD enabling it to systematically incorporate expert feedback through a novel contrastive learning feedback loop to improve the learned representations and thereby reduce prediction uncertainty (CAAD-EF). We view CAAD-EF as a novel, holistic and widely applicable solution to anomaly detection.
