Related papers: Unilateral Antidotes to DNS Cache Poisoning

Backdoor attacks have been one of the emerging security threats to deep neural networks (DNNs), leading to serious consequences. One of the mainstream backdoor defenses is model reconstruction-based. Such defenses adopt model unlearning or…

Cryptographic algorithm agility is an important property for DNSSEC: it allows easy deployment of new algorithms if the existing ones are no longer secure. In this work we show that the cryptographic agility in DNSSEC, although critical for…

DNS Security Extensions (DNSSEC) provide the most effective way to fight DNS cache poisoning attacks. Yet, very few DNS resolvers perform DNSSEC validation. Identifying such systems is non-trivial and the existing methods are not suitable…

Federated Learning systems are increasingly subjected to a multitude of model poisoning attacks from clients. Among these, edge-case attacks that target a small fraction of the input space are nearly impossible to detect using existing…

Gradient-based adversarial attacks on deep neural networks pose a serious threat, since they can be deployed by adding imperceptible perturbations to the test data of any network, and the risk they introduce cannot be assessed through the…

A powerful category of (invisible) data poisoning attacks modify a subset of training examples by small adversarial perturbations to change the prediction of certain test-time data. Existing defense mechanisms are not desirable to deploy in…

Emerging protocols such as DNS-over-HTTPS (DoH) and DNS-over-TLS (DoT) improve the privacy of DNS queries and responses. While this trend towards encryption is positive, deployment of these protocols has in some cases resulted in further…

Local Differential Privacy (LDP) has been widely adopted to protect user privacy in decentralized data collection. However, recent studies have revealed that LDP protocols are vulnerable to data poisoning attacks, where malicious users…

We study the robustness of data-centric methods to find neural network architectures, known as neural architecture search (NAS), against data poisoning. To audit this robustness, we design a poisoning framework that enables the systematic…

Deep Neural Networks (DNNs) are known to be vulnerable to both backdoor and adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct robustness problems and solved separately, since they belong to…

Data poisoning is a type of adversarial attack on training data where an attacker manipulates a fraction of data to degrade the performance of machine learning model. Therefore, applications that rely on external data-sources for training…

Distributed Denial of Service (DDoS) attacks exhaust victim's bandwidth or services. Traditional architecture of Internet is vulnerable to DDoS attacks and an ongoing cycle of attack & defense is observed. In this paper, different types and…

Security issues are crucial in a number of machine learning applications, especially in scenarios dealing with human activity rather than natural phenomena (e.g., information ranking, spam detection, malware detection, etc.). It is to be…

Poisoning attacks are a category of adversarial machine learning threats in which an adversary attempts to subvert the outcome of the machine learning systems by injecting crafted data into training data set, thus increasing the machine…

The Domain Name System (DNS) comprises name servers translating domain names into, commonly, IP addresses. Authoritative name servers hosts the resource records (RR) for certain zones, and resolver name servers are responsible for querying…

Today, internet and web services have become an inseparable part of our lives. Hence, ensuring continuous availability of service has become imperative to the success of any organization. But these services are often hampered by constant…

The current Domain Name System (DNS), as a core infrastructure of the internet, exhibits several shortcomings: its centralized architecture leads to censorship risks and single points of failure, making domain name resolution vulnerable to…

There is a continuous increase in the sophistication that modern malware exercise in order to bypass the deployed security mechanisms. A typical approach to evade the identification and potential takedown of a botnet command and control…

This paper presents a mitigation scheme to cope with the random query string Denial of Service (DoS) attack, which is based on a vulnerability of current Content Delivery Networks (CDNs). The attack exploits the fact that edge servers…

Deep Neural Network (DNN) models have vulnerabilities related to security concerns, with attackers usually employing complex hacking techniques to expose their structures. Data poisoning-enabled perturbation attacks are complex adversarial…