4 accepted papers
Network traffic analysis is crucial for maintaining the security of networks. Yet deploying accurate models on edge nodes remains challenging due to protocol diversity, complex traffic behaviors, and stringent resource constraints. Although recent deep learning models achieve strong performance, the
The combination of self-supervised learning and adversarial training (AT) can significantly improve the adversarial robustness of self-supervised models. However, the robustness of self-supervised adversarial training (self-AT) still lags behind that of state-of-the-art (SOTA) supervised AT (sup-AT)…
The emergence of Satellite-Terrestrial Integrated Networks (STIN) has significantly expanded terrestrial network coverage but introduced new security threats. Current Intrusion Detection Systems (IDSs) for STIN mostly consider the distributed nature of satellites, overlooking the computational limit…
Self-supervised learning (SSL) is a technique used to learn feature representations from unlabeled data. However, existing SSL frameworks either rely too heavily on domain knowledge due to their design based on feature invariance, leading to a lack of domain transferability, or they are based on aut…