Adversarial Attacks on Cognitive Self-Organizing Networks: The Challenge and the Way Forward

Authors: Muhammad Usama, Junaid Qadir, Ala Al-Fuqaha
Published: 2018-09-26

Source: https://arxiv.org/abs/1810.07242

PDF: https://arxiv.org/pdf/1810.07242

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敵対的学習 敵対的攻撃手法 モデルの頑健性保証

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Abstract

Future communications and data networks are expected to be largely cognitive self-organizing networks (CSON). Such networks will have the essential property of cognitive self-organization, which can be achieved using machine learning techniques (e.g., deep learning). Despite the potential of these techniques, these techniques in their current form are vulnerable to adversarial attacks that can cause cascaded damages with detrimental consequences for the whole network. In this paper, we explore the effect of adversarial attacks on CSON. Our experiments highlight the level of threat that CSON have to deal with in order to meet the challenges of next-generation networks and point out promising directions for future work.