PREDATOR: Proactive Recognition and Elimination of Domain Abuse at Time-Of-Registration
Shuang Hao, Alex Kantchelian, Brad Miller, Vern Paxson, Nick Feamster
摘要
Miscreants register thousands of new domains every day to launch Internet-scale attacks, such as spam, phishing, and drive-by downloads. Quickly and accurately determining a domain's reputation (association with malicious activity) provides a powerful tool for mitigating threats and protecting users. Yet, existing domain reputation systems work by observing domain use (e.g., lookup patterns, content hosted)-often too late to prevent miscreants from reaping benefits of the attacks that they launch. As a complement to these systems, we explore the extent to which features evident at domain registration indicate a domain's subsequent use for malicious activity. We develop PREDATOR, an approach that uses only time-of-registration features to establish domain reputation. We base its design on the intuition that miscreants need to obtain many domains to ensure profitability and attack agility, leading to abnormal registration behaviors (e.g., burst registrations, textually similar names). We evaluate PREDATOR using registration logs of second-level .com and .net domains over five months. PREDATOR achieves a 70% detection rate with a false positive rate of 0.35%, thus making it an effective-and early-first line of defense against the misuse of DNS domains. It predicts malicious domains when they are registered, which is typically days or weeks earlier than existing DNS blacklists. CCS Concepts •Security and privacy → Intrusion/anomaly detection and malware mitigation; •Networks → Network domains;
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper22
- Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression LearningMatthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu 等S&P 2018 · 被引用 867 次
- Hiding in Plain Sight: A Longitudinal Study of Combosquatting AbusePanagiotis Kintis, Najmeh Miramirkhani, Charles Lever, Yizheng Chen 等CCS 2017 · 被引用 166 次
- Detecting Fake Accounts in Online Social Networks at the Time of RegistrationsDong Yuan, Yuanli Miao, Neil Zhenqiang Gong, Zheng Yang 等CCS 2019 · 被引用 86 次
- A Lustrum of Malware Network Communication: Evolution and InsightsChaz Lever, Platon Kotzias, Davide Balzarotti, Juan Caballero 等S&P 2017 · 被引用 86 次
- Predicting Impending Exposure to Malicious Content from User BehaviorMahmood Sharif, Jumpei Urakawa, Nicolas Christin, Ayumu Kubota 等CCS 2018 · 被引用 71 次
相关 Paper
- MANTIS: Detection of Zero-Day Malicious Domains Leveraging Low Reputed Hosting InfrastructureFatih Deniz, Mohamed Nabeel, Ting Yu, Issa KhalilS&P 2025
- Exposing the Roots of DNS Abuse: A Data-Driven Analysis of Key Factors Behind Phishing Domain RegistrationsYevheniya Nosyk, Maciej Korczynski, Carlos Gañán, Sourena Maroofi 等CCS 2025 · 被引用 1 次
- Don't Let One Rotten Apple Spoil the Whole Barrel: Towards Automated Detection of Shadowed DomainsDaiping Liu, Zhou Li, Kun Du, Haining Wang 等CCS 2017 · 被引用 60 次
- Practical Attacks Against DNS Reputation SystemsTillson Galloway, Kleanthis Karakolios, Zane Ma, Roberto Perdisci 等S&P 2024 · 被引用 13 次
- Domain-Z: 28 Registrations Later Measuring the Exploitation of Residual Trust in DomainsChaz Lever, Robert J. Walls, Yacin Nadji, David Dagon 等S&P 2016 · 被引用 76 次
