In a membership inference attack, an attacker aims to infer whether a data sample is in a
target classifier's training dataset or not. Specifically, given a black-box access to the target …

Machine learning (ML) based malicious traffic detection is an emerging security paradigm,
particularly for zero-day attack detection, which is complementary to existing rule based …

Anonymity networks, eg, Tor, are vulnerable to various website fingerprinting (WF) attacks,
which allows attackers to perceive user privacy on these networks. However, the defenses …

Deep neural networks (DNNs) are commonly used for various traffic analysis problems, such
as website fingerprinting and flow correlation, as they outperform traditional (eg, statistical) …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

Time series has numerous application scenarios. However, since many time series data are
personal data, releasing them directly could cause privacy infringement. All existing …

This paper investigates the privacy leakage of smart speakers under an encrypted traffic
analysis attack, referred to as voice command fingerprinting. In this attack, an adversary can …

A video identification attack is a tangible privacy threat that can reveal videos that victims are
watching. In this paper, we present the first study of a video identification attack in Long Term …

It is well established that neural networks are vulnerable to adversarial examples, which are
almost imperceptible on human vision and can cause the deep models misbehave. Such …

In modern cloud platforms, it is becoming more important to preserve the privacy of guest
virtual machines (VMs) from the untrusted host. To this end, Secure Encrypted Virtualization …