Related papers: The Evolution of DNS Security and Privacy

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Distributed Denial of Service (DDoS) attacks persist as significant threats to online services and infrastructure. This paper offers a comprehensive survey of emerging DDoS attacks and detection strategies over the past decade.
arXiv Detail & Related papers (2025-02-27T11:22:25Z)
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We investigate different DNS security approaches using an edge DNS resolver implemented as a Virtual Network Function (VNF) We present our results for cache-based and non-cached responses and evaluate the corresponding security benefits.
arXiv Detail & Related papers (2025-02-13T19:16:39Z)
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DNS (Domain Name System) is one of the most critical components of the Internet. Researchers have been constantly developing methods to detect and defend against the attacks against DNS. Most solutions discard packets for defensive approaches, which can cause legitimate packets to be dropped. We propose MTDNS, a resilient MTD-based approach that employs Moving Target Defense techniques.
arXiv Detail & Related papers (2024-10-03T06:47:16Z)
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We present DNSSEC+, which addresses security and deployability downsides of DNSSEC. We show how DNSSEC+ fulfills nine security, privacy, and deployability properties for name resolution.
arXiv Detail & Related papers (2024-08-02T01:25:14Z)
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We develop a new class of DNSSEC-based algorithmic complexity attacks on DNS, we dub KeyTrap attacks. With just a single DNS packet, the KeyTrap attacks lead to a 2.0x spike in CPU count in vulnerable DNS resolvers, stalling some for as long as 16 hours. Exploiting KeyTrap, an attacker could effectively disable Internet access in any system utilizing a DNSSEC-validating resolver.
arXiv Detail & Related papers (2024-06-05T10:33:04Z)
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arXiv Detail & Related papers (2024-05-30T09:23:53Z)
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arXiv Detail & Related papers (2023-12-07T08:03:10Z)
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arXiv Detail & Related papers (2023-06-09T14:33:26Z)
FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases [50.065022493142116]
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arXiv Detail & Related papers (2023-02-28T11:31:29Z)
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arXiv Detail & Related papers (2021-03-10T19:03:38Z)
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Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
arXiv Detail & Related papers (2020-07-05T18:22:40Z)
On Certifying Robustness against Backdoor Attacks via Randomized Smoothing [74.79764677396773]
We study the feasibility and effectiveness of certifying robustness against backdoor attacks using a recent technique called randomized smoothing. Our results show the theoretical feasibility of using randomized smoothing to certify robustness against backdoor attacks. Existing randomized smoothing methods have limited effectiveness at defending against backdoor attacks.
arXiv Detail & Related papers (2020-02-26T19:15:46Z)

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