Related papers: Is Protective DNS Blocking the Wild West?

We turn our attention to the elephant in the room of data protection, which is none other than the simple and obvious question: "Who's tracking sensitive domains?". Despite a fast-growing amount of work on more complex facets of the…

It is notoriously difficult to securely configure HTTPS, and poor server configurations have contributed to several attacks including the FREAK, Logjam, and POODLE attacks. In this work, we empirically evaluate the TLS security posture of…

Online tracking is a whack-a-mole game between trackers who build and monetize behavioral user profiles through intrusive data collection, and anti-tracking mechanisms, deployed as a browser extension, built-in to the browser, or as a DNS…

Deep learning is becoming increasingly popular in real-life applications, especially in natural language processing (NLP). Users often choose training outsourcing or adopt third-party data and models due to data and computation resources…

Recent events have placed a renewed focus on the issue of racial justice in the United States and other countries. One dimension of this issue that has received considerable attention is the security and privacy threats and vulnerabilities…

The Internet has many backbone components on top of which the whole world is connected. It is important to make these components, like Border Gateway Protocol (BGP), Domain Name System (DNS), and Public Key Infrastructure (PKI), secure and…

This work proposes a novel approach to infer and characterize Internet-scale DNS amplification DDoS attacks by leveraging the darknet space. Complementary to the pioneer work on inferring Distributed Denial of Service (DDoS) activities…

Most TLS clients such as modern web browsers enforce coarse-grained TLS security configurations. They support legacy versions of the protocol that have known design weaknesses, and weak ciphersuites that provide fewer security guarantees…

Over the past decade, Internet centralization and its implications for both people and the resilience of the Internet has become a topic of active debate. While the networking community informally agrees on the definition of centralization,…

Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages. Nevertheless, the exchange of model updates and gradients in this architecture provides new…

Content Delivery Networks (CDNs) offer a protection layer for enhancing the security of websites. However, a significant security flaw named Absence of Domain Verification (DVA) has become emerging recently. Although this threat is…

Domain Name Service (DNS) resolution is a mechanism that resolves the symbolic names of networked devices to their corresponding Internet Protocol (IP) address. With the emergence of the document that describes an extension to a DNS service…

Deep neural networks are known to have security issues. One particular threat is the Trojan attack. It occurs when the attackers stealthily manipulate the model's behavior through Trojaned training samples, which can later be exploited.…

The growing market for smart home IoT devices promises new conveniences for consumers while presenting novel challenges for preserving privacy within the home. Specifically, Internet service providers or neighborhood WiFi eavesdroppers can…

Domain Name System (DNS) domains became Internet-level identifiers for entities (like companies, organizations, or individuals) hosting services and sharing resources over the Internet. Domains can specify a set of security policies (such…

Dynamic Spectrum Sharing (DSS) is increasingly promoted as a key element of modern spectrum policy, driven by the rising demand from commercial wireless systems and advances in spectrum access technologies. Passive radio sciences, including…

Data poisoning attacks -- where an adversary can modify a small fraction of training data, with the goal of forcing the trained classifier to high loss -- are an important threat for machine learning in many applications. While a body of…

The proliferation of smart home Internet of Things (IoT) devices presents unprecedented challenges for preserving privacy within the home. In this paper, we demonstrate that a passive network observer (e.g., an Internet service provider)…

Deep Learning is currently used to perform multiple tasks, such as object recognition, face recognition, and natural language processing. However, Deep Neural Networks (DNNs) are vulnerable to perturbations that alter the network prediction…

Deep Neural Networks (DNNs) have found extensive applications in safety-critical artificial intelligence systems, such as autonomous driving and facial recognition systems. However, recent research has revealed their susceptibility to…