Publications

@inproceedings{kranig_toward_2025,
  title     = {Toward a Representative {DNS} Data Corpus: A Longitudinal Comparison of Collection Methods},
  author    = {Kranig, Calvin and Pauley, Eric and Wung, Wei-Shiang and Barford, Paul and Crovella, Mark and Sommers, Joel},
  booktitle = {Network Traffic Measurement and Analysis Conference (TMA)},
  pages     = {1--11},
  year      = {2025},
  doi       = {10.23919/TMA66427.2025.11096967}
}

@inproceedings{wung_squatspotting_2025,
  title     = {Squatspotting: Towards the Systematic Measurement of Typosquatting Techniques},
  author    = {Wung, Wei-Shiang and Kranig, Calvin and Pauley, Eric and Barford, Paul and Crovella, Mark and Sommers, Joel},
  booktitle = {IFIP Networking 2025},
  year      = {2025}
}

@inproceedings{bsb25,
  title     = {{Efficient Storage Integrity in Adversarial Settings}},
  booktitle = {2025 IEEE Symposium on Security and Privacy (SP)},
  author    = {Burke, Quinn and Sheatsley, Ryan and Beugin, Yohan and Pauley, Eric and Hines, Owen and Swift, Michael, and McDaniel, Patrick},
  month     = {may},
  year      = {2025}
}

@inproceedings{pauley_secure_2025,
  address   = {San Diego, CA},
  title     = {Secure {IP} {Address} {Allocation} at {Cloud} {Scale}},
  booktitle = {2025 {Network} and {Distributed} {Systems} {Security} {Symposium} ({NDSS})},
  publisher = {Internet Society},
  author    = {Pauley, Eric and Domico, Kyle and Hoak, Blaine and Sheatsley, Ryan and Burke, Quinn and Beugin, Yohan and Kirda, Engin and McDaniel, Patrick},
  month     = feb,
  year      = {2025}
}

@inproceedings{10356225,
  author    = {Guide, Ryan and Pauley, Eric and Beugin, Yohan and Sheatsley, Ryan and McDaniel, Patrick},
  booktitle = {MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM)},
  title     = {Characterizing the Modification Space of Signature IDS Rules},
  year      = {2023},
  volume    = {},
  number    = {},
  pages     = {536-541},
  keywords  = {Military communication;Network intrusion detection;Telecommunication traffic;Telescopes;Market research;Space exploration;Security},
  doi       = {10.1109/MILCOM58377.2023.10356225}
}

@inproceedings{10.1145/3618257.3624810,
  author    = {Pauley, Eric and Barford, Paul and McDaniel, Patrick},
  title     = {The CVE Wayback Machine: Measuring Coordinated Disclosure from Exploits against Two Years of Zero-Days},
  year      = {2023},
  isbn      = {9798400703829},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/3618257.3624810},
  doi       = {10.1145/3618257.3624810},
  booktitle = {Proceedings of the 2023 ACM on Internet Measurement Conference},
  pages     = {236–252},
  numpages  = {17},
  keywords  = {known exploited vulnerabilities, coordinated vulnerability disclosure, intrusion detection systems, honeypots, internet telescopes},
  location  = {Montreal QC, Canada},
  series    = {IMC '23}
}

@inproceedings{shp+23,
  title     = {The Space of Adversarial Strategies},
  author    = {Ryan Sheatsley and Blaine Hoak and Eric Pauley and Patrick McDaniel},
  booktitle = {32nd USENIX Security Symposium (USENIX Security 23)},
  year      = {2023},
  month     = aug,
  publisher = {USENIX Association}
}

@inproceedings{pauley_dscope_2023,
  address   = {Anaheim, CA},
  title     = {{DScope}: {A} {Cloud}-{Native} {Internet} {Telescope}},
  booktitle = {Proceedings of the 32nd {USENIX} {Security} {Symposium} ({USENIX} {Security} 2023, to appear)},
  publisher = {USENIX Association},
  author    = {Pauley, Eric and Barford, Paul and McDaniel, Patrick},
  month     = aug,
  year      = {2023}
}

@inproceedings{pauley_performant_2022,
  title     = {Performant {Binary} {Fuzzing} without {Source} {Code} using {Static} {Instrumentation}},
  abstract  = {Advancements in fuzz testing have achieved the ability to quickly and comprehensively find security-critical faults in software systems. Yet, some of these techniques rely on access to source code, which is often unavailable in practice. In this paper, we explore techniques to replicate the depth and efficiency of source-code available fuzzers via static binary instrumentation. Developing such instrumentation is difficult because compilation is a lossy process, and much of the source-level semantics leveraged by these techniques are not available in binaries. We recover much of this information via heuristic control flow reconstruction, a shadow stack for function identification, and a novel technique for instrumenting comparison instructions. We evaluate RWFUZZ on the LAVA-M dataset, achieving the same effectiveness as a best-in-class source-available fuzzer with a 3.4× execution time overhead (lower than existing dynamic fuzzing approaches). In this way, we show that techniques for binary fuzzing may approach the functional ability of source-available fuzzing.},
  booktitle = {2022 {IEEE} {Conference} on {Communications} and {Network} {Security} ({CNS})},
  publisher = {IEEE},
  author    = {Pauley, Eric and Tan, Gang and Zhang, Danfeng and McDaniel, Patrick},
  month     = oct,
  year      = {2022}
}

@inproceedings{pauley_measuring_2022,
  title     = {Measuring and {Mitigating} the {Risk} of {IP} {Reuse} on {Public} {Clouds}},
  isbn      = {978-1-66541-316-9},
  url       = {https://www.computer.org/csdl/proceedings-article/sp/2022/131600b523/1CIO7rpcgSs},
  doi       = {10.1109/SP46214.2022.00094},
  abstract  = {Public clouds provide scalable and cost-efficient computing through resource sharing. However, moving from traditional on-premises service management to clouds introduces new challenges; failure to correctly provision, maintain, or decommission elastic services can lead to functional failure and vulnerability to attack. In this paper, we explore a broad class of attacks on clouds which we refer to as cloud squatting. In a cloud squatting attack, an adversary allocates resources in the cloud (e.g., IP addresses) and thereafter leverages latent configu- ration to exploit prior tenants. To measure and categorize cloud squatting we deployed a custom Internet telescope within the Amazon Web Services us-east-1 region. Using this apparatus, we deployed over 3 million servers receiving 1.5 million unique IP addresses (≈ 56\% of the available pool) over 101 days beginning in March of 2021. We identified 4 classes of cloud services, 7 classes of third-party services, and DNS as sources of exploitable latent configurations. We discovered that exploitable configurations were both common and in many cases extremely dangerous; we received over 5 million cloud messages, many containing sensitive data such as financial transactions, GPS location, and PII. Within the 7 classes of third-party services, we identified dozens of exploitable software systems spanning hundreds of servers (e.g., databases, caches, mobile applications, and web services). Lastly, we identified 5446 exploitable domains spanning 231 eTLDs—including 105 in the top 10 000 and 23 in the top 1000 popular domains. Through tenant disclosures we have identified several root causes, including (a) a lack of organizational controls, (b) poor service hygiene, and (c) failure to follow best practices. We conclude with a discussion of the space of possible mitigations and describe the mitigations to be deployed by Amazon in response to this study.},
  language  = {English},
  booktitle = {2022 {IEEE} {Symposium} on {Security} and {Privacy} ({SP})},
  publisher = {IEEE Computer Society},
  author    = {Pauley, Eric and Sheatsley, Ryan and Hoak, Blaine and Burke, Quinn and Beugin, Yohan and McDaniel, Patrick},
  month     = apr,
  year      = {2022},
  note      = {ISSN: 2375-1207},
  pages     = {1523--1523}
}

@article{BeuginBurkeHoakSheatsleyPauleyTanHussainMcDaniel+2022+25+46,
  author  = {Yohan Beugin and Quinn Burke and Blaine Hoak and Ryan Sheatsley and Eric Pauley and Gang Tan and Syed Rafiul Hussain and Patrick McDaniel},
  doi     = {doi:10.2478/popets-2022-0034},
  url     = {https://doi.org/10.2478/popets-2022-0034},
  title   = {Building a Privacy-Preserving Smart Camera System},
  journal = {Proceedings on Privacy Enhancing Technologies},
  number  = {2},
  volume  = {2022},
  year    = {2022},
  pages   = {25--46}
}

@inproceedings{10.1145/3460120.3484570,
  author    = {Sheatsley, Ryan and Hoak, Blaine and Pauley, Eric and Beugin, Yohan and Weisman, Michael J. and McDaniel, Patrick},
  title     = {On the Robustness of Domain Constraints},
  year      = {2021},
  isbn      = {9781450384544},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/3460120.3484570},
  doi       = {10.1145/3460120.3484570},
  abstract  = {Machine learning is vulnerable to adversarial examples--inputs designed to cause models to perform poorly. However, it is unclear if adversarial examples represent realistic inputs in the modeled domains. Diverse domains such as networks and phishing have domain constraints--complex relationships between features that an adversary must satisfy for an attack to be realized (in addition to any adversary-specific goals). In this paper, we explore how domain constraints limit adversarial capabilities and how adversaries can adapt their strategies to create realistic (constraint-compliant) examples. In this, we develop techniques to learn domain constraints from data, and show how the learned constraints can be integrated into the adversarial crafting process. We evaluate the efficacy of our approach in network intrusion and phishing datasets and find: (1) up to 82% of adversarial examples produced by state-of-the-art crafting algorithms violate domain constraints, (2) domain constraints are robust to adversarial examples; enforcing constraints yields an increase in model accuracy by up to 34%. We observe not only that adversaries must alter inputs to satisfy domain constraints, but that these constraints make the generation of valid adversarial examples far more challenging.},
  booktitle = {Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security},
  pages     = {495–515},
  numpages  = {21},
  keywords  = {constraint satisfaction, constraint learning, adversarial machine learning, formal logic},
  location  = {Virtual Event, Republic of Korea},
  series    = {CCS '21}
}

@inproceedings{king_parteetor_2024,
  address   = {Salt Lake City, UT, USA},
  title     = {ParTEETor: A System for Partial Deployments of TEEs within Tor},
  booktitle = {Proceedings of the 23rd Workshop on Privacy in the Electronic Society (WPES)},
  publisher = {ACM},
  author    = {King, Rachel and Burke, Quinn and Beugin, Yohan and Hoak, Blaine and Li, Kunyang and Pauley, Eric and Sheatsley, Ryan and McDaniel, Patrick},
  month     = oct,
  year      = {2024}
}

@inproceedings{pauley_understanding_2023,
  address   = {San Diego, CA},
  title     = {Understanding the {Ethical} {Frameworks} of {Internet} {Measurement} {Studies}},
  booktitle = {The 2nd {International} {Workshop} on {Ethics} in {Computer} {Security} ({EthiCS} 2023)},
  author    = {Pauley, Eric and McDaniel, Patrick},
  month     = feb,
  year      = {2023},
  doi       = {10.14722/ethics.2023.239547}
}

@article{burke_securing_2025,
  title   = {Securing Cloud File Systems With Trusted Execution},
  author  = {Burke, Quinn and Beugin, Yohan and Hoak, Blaine and King, Rachel and Pauley, Eric and Sheatsley, Ryan and Yu, Mingli and He, Ting and {La Porta}, Thomas F. and McDaniel, Patrick},
  journal = {IEEE Transactions on Dependable and Secure Computing},
  volume  = {22},
  number  = {3},
  pages   = {1976--1992},
  year    = {2025},
  doi     = {10.1109/TDSC.2024.3474423}
}

@article{10.1145/3333501,
  author     = {Celik, Z. Berkay and Fernandes, Earlence and Pauley, Eric and Tan, Gang and McDaniel, Patrick},
  title      = {Program Analysis of Commodity IoT Applications for Security and Privacy: Challenges and Opportunities},
  year       = {2019},
  issue_date = {July 2020},
  publisher  = {Association for Computing Machinery},
  address    = {New York, NY, USA},
  volume     = {52},
  number     = {4},
  issn       = {0360-0300},
  url        = {https://doi.org/10.1145/3333501},
  doi        = {10.1145/3333501},
  abstract   = {Recent advances in Internet of Things (IoT) have enabled myriad domains such as smart homes, personal monitoring devices, and enhanced manufacturing. IoT is now pervasive—new applications are being used in nearly every conceivable environment, which leads to the adoption of device-based interaction and automation. However, IoT has also raised issues about the security and privacy of these digitally augmented spaces. Program analysis is crucial in identifying those issues, yet the application and scope of program analysis in IoT remains largely unexplored by the technical community. In this article, we study privacy and security issues in IoT that require program-analysis techniques with an emphasis on identified attacks against these systems and defenses implemented so far. Based on a study of five IoT programming platforms, we identify the key insights that result from research efforts in both the program analysis and security communities and relate the efficacy of program-analysis techniques to security and privacy issues. We conclude by studying recent IoT analysis systems and exploring their implementations. Through these explorations, we highlight key challenges and opportunities in calibrating for the environments in which IoT systems will be used.},
  journal    = {ACM Comput. Surv.},
  month      = {aug},
  articleno  = {74},
  numpages   = {30},
  keywords   = {IoT security and privacy, IoT programming platforms, program analysis}
}

@article{pauley_clouds_2025,
  title   = {Clouds Are Where the (Security) Action Is: Have Honeypots Been Left in the Dust?},
  author  = {Pauley, Eric and Barford, Paul and McDaniel, Patrick},
  journal = {IEEE Security \& Privacy},
  volume  = {23},
  number  = {6},
  pages   = {51--57},
  year    = {2025},
  doi     = {10.1109/MSEC.2025.3603410}
}

@article{li_robustness_2025,
  title   = {On the Robustness Tradeoff in Fine-Tuning},
  author  = {Li, Kunyang and Noirot Ferrand, Jean-Charles and Sheatsley, Ryan and Hoak, Blaine and Beugin, Yohan and Pauley, Eric and McDaniel, Patrick},
  journal = {arXiv preprint arXiv:2503.14836},
  year    = {2025},
  doi     = {10.48550/arXiv.2503.14836}
}

@article{domico_adversarial_2025,
  title   = {Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning},
  author  = {Domico, Kyle and Noirot Ferrand, Jean-Charles and Sheatsley, Ryan and Pauley, Eric and Hanna, Josiah and McDaniel, Patrick},
  journal = {arXiv preprint arXiv:2503.01734},
  year    = {2025},
  doi     = {10.48550/arXiv.2503.01734}
}

@article{ferrand_targeting_2025,
  title   = {Targeting Alignment: Extracting Safety Classifiers of Aligned {LLMs}},
  author  = {Noirot Ferrand, Jean-Charles and Beugin, Yohan and Pauley, Eric and Sheatsley, Ryan and McDaniel, Patrick},
  journal = {arXiv preprint arXiv:2501.16534},
  year    = {2025},
  doi     = {10.48550/arXiv.2501.16534}
}

@misc{héon2023systematic,
  title         = {Systematic Evaluation of Geolocation Privacy Mechanisms},
  author        = {Alban Héon and Ryan Sheatsley and Quinn Burke and Blaine Hoak and Eric Pauley and Yohan Beugin and Patrick McDaniel},
  year          = {2023},
  eprint        = {2309.06263},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CR}
}

@misc{burke2023securing,
  title         = {Securing Cloud File Systems using Shielded Execution},
  author        = {Quinn Burke and Yohan Beugin and Blaine Hoak and Rachel King and Eric Pauley and Ryan Sheatsley and Mingli Yu and Ting He and Thomas La Porta and Patrick McDaniel},
  year          = {2023},
  eprint        = {2305.18639},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CR}
}

misc{https://doi.org/10.48550/arxiv.2208.09776,
  doi = {10.48550/ARXIV.2208.09776},
  url = {https://arxiv.org/abs/2208.09776},
  author = {Beugin, Yohan and Burke, Quinn and Hoak, Blaine and Sheatsley, Ryan and Pauley, Eric and Tan, Gang and Hussain, Syed Rafiul and McDaniel, Patrick},
  keywords = {Cryptography and Security (cs.CR), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Privacy-Preserving Protocols for Smart Cameras and Other IoT Devices},
  publisher = {arXiv},
  year = {2022},
}

@phdthesis{pauley_static_2020,
  title  = {Static {Instrumentation} for {Performant} {Binary} {Fuzzing}},
  url    = {https://etda.libraries.psu.edu/catalog/17753eap5377},
  school = {Pennsylvania State University},
  author = {Pauley, Eric},
  month  = mar,
  year   = {2020}
}