BEGIN:VCALENDAR VERSION:2.0 PRODID:Data::ICal 0.24 BEGIN:VEVENT DESCRIPTION: 'Title: Your Packets Are Showing: Hybrid Quantum ML for Pass ive OS\n Fingerprinting\n Tags: DEF CON Official Talk | Tool ๐Ÿ› \n When: Friday\, Aug 7\, 12:00 - 12:30 PDT\n Where: LVCCW Level 1 Hall 3 1 006 (Main Track 1) and DCTV-1 - [1]Map\n\n Description:\n\n Quantum cy bersecurity isn't just Q-Day. We took passive OS\n fingerprinting\, the technique behind p0f and every modern ML-based\n fingerprinting tool\, a nd mapped it onto a 20-qubit quantum circuit\,\n replacing XGBoost as th e classifier inside an "OsirisML"-style\n pipeline. Head-to-head on real packet captures from CIC-IDS 2017\, the\n quantum version landed within 0.013 F1 of XGBoost on identical\n features\, using roughly two orders of magnitude fewer trainable\n parameters. This is the first time a real DEF CON-relevant security\n workload has been mapped onto a quantum cla ssifier with results that\n hold up against the classical tool the commu nity already uses. The\n conversation about quantum and security has bee n stuck on\n cryptography. This talk is about everything else it can do. \n\n M. Zalewski\, "p0f v3\," [Online]. Available:\n https://lcamtuf.c oredump.cx/p0f3/. [Accessed: Apr. 25\, 2026].\n\n J. Holland\, P. Schmit t\, N. Feamster\, and P. Mittal\, "New Directions in\n Automated Traffic Analysis\," in Proc. 2021 ACM SIGSAC Conf. on\n Computer and Communicat ions Security (CCS)\, 2021\, pp. 3366โ€“3383\,\n doi: 10.1145/3460120.34 84758.\n\n S. Ekeroth\, J. Neale\, and J. S. Kim\, "Machine Learning Opt imization\n for Enhanced OS Fingerprinting\," Virginia Tech\, 2024.\n\n I. Sharafaldin\, A. H. Lashkari\, and A. A. Ghorbani\, "Toward Generatin g\n a New Intrusion Detection Dataset and Intrusion Traffic\n Characte rization\," in Proc. 4th Int. Conf. on Information Systems\n Security an d Privacy (ICISSP)\, 2018.\n\n T. Chen and C. Guestrin\, "XGBoost: A Sca lable Tree Boosting System\,"\n in Proc. 22nd ACM SIGKDD Int. Conf. on K nowledge Discovery and Data\n Mining\, 2016\, pp. 785โ€“794\, doi: 10.11 45/2939672.2939785.\n\n M. Benedetti\, E. Lloyd\, S. Sack\, and M. Fiore ntini\, "Parameterized\n quantum circuits as machine learning models\," Quantum Sci. Technol.\,\n vol. 4\, no. 4\, p. 043001\, 2019\, doi: 10.10 88/2058-9565/ab4eb5.\n\n M. Schuld\, A. Bocharov\, K. M. Svore\, and N. Wiebe\, "Circuit-centric\n quantum classifiers\," Phys. Rev. A\, vol. 10 1\, no. 3\, p. 032308\, 2020\,\n doi: 10.1103/PhysRevA.101.032308.\n\n K. Mitarai\, M. Negoro\, M. Kitagawa\, and K. Fujii\, "Quantum circuit\n learning\," Phys. Rev. A\, vol. 98\, no. 3\, p. 032309\, 2018\, doi:\n 10.1103/PhysRevA.98.032309.\n\n M. Schuld\, V. Bergholm\, C. Gogolin\, J. Izaac\, and N. Killoran\,\n "Evaluating analytic gradients on quantum hardware\," Phys. Rev. A\,\n vol. 99\, no. 3\, p. 032331\, 2019\, doi: 10.1103/PhysRevA.99.032331.\n\n T. Jones and J. Gacon\, "Efficient calcu lation of gradients in\n classical simulations of variational quantum al gorithms\," arXiv\n preprint arXiv:2009.02823\, 2020.\n\n H. Neven\, V . S. Denchev\, G. Rose\, and W. G. Macready\, "QBoost: Large\n scale cla ssifier training with adiabatic quantum optimization\," in\n Proc. Asian Conf. on Machine Learning (ACML)\, vol. 25\, 2012\, pp.\n 333โ€“348.\n\ n V. Havlicek\, A. D. Corcoles\, K. Temme\, A. W. Harrow\, A. Kandala\, J. M.\n Chow\, and J. M. Gambetta\, "Supervised learning with quantum-en hanced\n feature spaces\," Nature\, vol. 567\, no. 7747\, pp. 209โ€“212\ , 2019\, doi:\n 10.1038/s41586-019-0980-2.\n\n M. Schuld and N. Killor an\, "Quantum machine learning in feature\n Hilbert spaces\," Phys. Rev. Lett.\, vol. 122\, no. 4\, p. 040504\, 2019\,\n doi: 10.1103/PhysRevLet t.122.040504.\n\n H. Suryotrisongko and Y. Musashi\, "Evaluating hybrid quantum-classical\n deep learning for cybersecurity botnet DGA detection \," Procedia\n Comput. Sci.\, vol. 197\, pp. 223โ€“229\, 2022\, doi:\n 10.1016/j.procs.2021.12.135.\n\n E. D. Payares and J. C. Martinez-Santo s\, "Quantum machine learning for\n intrusion detection of distributed d enial of service attacks: a\n comparative overview\," in Proc. SPIE 1169 9\, Quantum Computing\,\n Communication\, and Simulation\, 2021\, p. 116 990B\, doi:\n 10.1117/12.2593297.\n\n J. R. McClean\, S. Boixo\, V. N. Smelyanskiy\, R. Babbush\, and H. Neven\,\n "Barren plateaus in quantum neural network training landscapes\," Nat.\n Commun.\, vol. 9\, no. 1\, p. 4812\, 2018\, doi:\n 10.1038/s41467-018-07090-4.\n\n M. Cerezo\, A . Sone\, T. Volkoff\, L. Cincio\, and P. J. Coles\, "Cost\n function dep endent barren plateaus in shallow parametrized quantum\n circuits\," Nat . Commun.\, vol. 12\, no. 1\, p. 1791\, 2021\, doi:\n 10.1038/s41467-021 -21728-w.\n\n V. Bergholm et al.\, "PennyLane: Automatic differentiation of hybrid\n quantum-classical computations\," arXiv preprint arXiv:1811 .04968\,\n 2018.\n\n E. Grant\, L. Wossnig\, M. Ostaszewski\, and M. B enedetti\, "An\n initialization strategy for addressing barren plateaus in parametrized\n quantum circuits\," Quantum\, vol. 3\, p. 214\, 2019\, doi:\n 10.22331/q-2019-12-09-214.\n\n Speakers:Daniel Justice\,Jae Su ng Kim\,La Alsulaim\,Shreya G Savadatti\n\n SpeakerBio: Daniel Justice\ , Carnegie Mellon University\n No BIO available\n SpeakerBio: Jae Sun g Kim\, Independent Researcher\n\n Jae Sung Kim is an independent resear cher specializing in network\n security and machine learning. His work f ocuses on applying novel\n computational approaches\, including hybrid q uantum-classical\n architectures\, to classical security problems such a s passive OS\n fingerprinting. He is a co-author of OsirisML\, a machine learning\n pipeline for enhanced OS fingerprinting built on the nPrint packet\n representation framework.\n\n SpeakerBio: La Alsulaim\, Univ ersity of Pittsburgh\n\n La Alsulaim is a Computer Science student at th e University of\n Pittsburgh. His research interests include machine lea rning\n applications\n\n SpeakerBio: Shreya G Savadatti\, Carnegie Me llon University\n\n Shreya G Savadatti is a researcher specializing in q uantum computing\n and cybersecurity. Currently a Masterโ€™s student at Carnegie Mellon\n University\, she explores Quantum Reservoir Computing (QRC) and\n cross-platform hardware benchmarking. As an IBM Qiskit Advoc ate\, she\n contributes to open-source compilers and recently placed Top 5 at MIT\n iQuHACK 2026 for quantum circuit optimization. Her published research\n includes work on Quantum Fully Homomorphic Encryption (QFHE) .\n\n '\n\n 1. #LVCCW_Level1_Hall3\n\n\n DTEND:20260807T193000Z DTSTART:20260807T190000Z LOCATION:DEF CON Talks - LVCCW Level 1 Hall 3 1006 (Main Track 1) and DCTV- 1 SUMMARY:Your Packets Are Showing: Hybrid Quantum ML for Passive OS Fingerpr inting END:VEVENT END:VCALENDAR