相关论文: Maybenot: A Framework for Traffic Analysis Defense…

The Tor network is a widely used system for anonymous communication. However, Tor is known to be vulnerable to attackers who can observe traffic at both ends of the communication path. In this paper, we show that prior attacks are just the…

Deep learning methods have shown state of the art performance in a range of tasks from computer vision to natural language processing. However, it is well known that such systems are vulnerable to attackers who craft inputs in order to…

End-to-end encryption (E2EE) has emerged as a fundamental element of modern digital communication, protecting data from unauthorized access during transmission. By design, E2EE ensures that only the intended recipient can decrypt the…

Growing concern for individual privacy, driven by an increased public awareness of the degree to which many of our electronic activities are tracked by interested third parties (e.g. Google knows what I am thinking before I finish entering…

QUIC has rapidly evolved into a cornerstone transport protocol for secure, low-latency communications, yet its deployment continues to expose critical security and privacy vulnerabilities, particularly during connection establishment phases…

Encrypted traffic classification is the task of identifying the application or service associated with encrypted network traffic. One effective approach for this task is to use deep learning methods to encode the raw traffic bytes directly…

This paper introduces a Testbed designed for generating network traffic, leveraging the capabilities of containers, Kubernetes, and eBPF/XDP technologies. Our Testbed serves as an advanced platform for producing network traffic for machine…

As advances in Deep Neural Networks (DNNs) demonstrate unprecedented levels of performance in many critical applications, their vulnerability to attacks is still an open question. We consider evasion attacks at testing time against Deep…

Criminals are increasingly using mobile based communication applications, like WhatsApp, that have end-to-end encryption to connect to each other. This makes traditional analysis of call graphs, or traffic analysis, virtually impossible and…

Website Fingerprinting (WF) aims to deanonymize users on the Tor network by analyzing encrypted network traffic. Recent deep-learning-based attacks show high accuracy on undefended traces. However, they struggle against modern defenses that…

Encrypted tunneling protocols are widely used. Beyond business and personal uses, malicious actors also deploy tunneling to hinder the detection of Command and Control and data exfiltration. A common approach to maintain visibility on…

Mixnets provide strong meta-data privacy and recent academic research and industrial projects have made strides in making them more secure, performance, and scalable. In this paper, we focus our work on stratified Mixnets -- a popular…

Nowadays, autonomous driving has attracted much attention from both industry and academia. Convolutional neural network (CNN) is a key component in autonomous driving, which is also increasingly adopted in pervasive computing such as…

Cyber Threat hunting is a proactive search for known attack behaviors in the organizational information system. It is an important component to mitigate advanced persistent threats (APTs). However, the attack behaviors recorded in…

Graph Neural Networks (GNNs) have achieved remarkable performance on tasks involving relational data. However, small perturbations to the graph structure can significantly alter GNN outputs, raising concerns about their robustness in…

Anonymity systems such as Tor aim to enable users to communicate in a manner that is untraceable by adversaries that control a small number of machines. To provide efficient service to users, these anonymity systems make full use of…

Tor is vulnerable to network-level adversaries who can observe both ends of the communication to deanonymize users. Recent work has shown that Tor is susceptible to the previously unknown active BGP routing attacks, called RAPTOR attacks,…

Under the emerging network coding paradigm, intermediate nodes in the network are allowed not only to store and forward packets but also to process and mix different data flows. We propose a low-complexity cryptographic scheme that exploits…

Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train or infer with private data while exploiting accelerators, such as GPUs, that are hosted in the cloud.…

DoS and DDoS attacks have been growing in size and number over the last decade and existing solutions to mitigate these attacks are in general inefficient. Compared to other types of malicious cyber attacks, DoS and DDoS attacks are…