Stjepan Picek
- Martín Abadi and David G. Andersen. Learning to protect communications with adversarial neural cryptography. CoRR, abs/1610.06918, 2016.Google Scholar
- Hernán Aguirre, Hiroyuki Okazaki, and Yasushi Fuwa. An evolutionary multiobjective approach to design highly non-linear boolean functions. In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO '07, page 749--756, New York, NY, USA, 2007. Association for Computing Machinery.Google ScholarDigital Library
- Timo Bartkewitz and Kerstin Lemke-Rust. Efficient template attacks based on probabilistic multi-class support vector machines. In Stefan Mangard, editor, Smart Card Research and Advanced Applications, pages 263--276, Berlin, Heidelberg, 2013. Springer Berlin Heidelberg.Google ScholarDigital Library
- Lejla Batina, Domagoj Jakobovic, Nele Mentens, Stjepan Picek, Antonio de la Piedra, and Dominik Sisejkovic. S-box pipelining using genetic algorithms for high-throughput aes implementations: How fast can we go? In Willi Meier and Debdeep Mukhopadhyay, editors, Progress in Cryptology - INDOCRYPT 2014, pages 322--337, Cham, 2014. Springer International Publishing.Google ScholarDigital Library
- G. T. Becker. On the pitfalls of using arbiter-pufs as building blocks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 34(8):1295--1307, 2015.Google ScholarDigital Library
- Georg T. Becker. The gap between promise and reality: On the insecurity of xor arbiter pufs. In Tim Güneysu and Helena Handschuh, editors, Cryptographic Hardware and Embedded Systems - CHES 2015, pages 535--555, Berlin, Heidelberg, 2015. Springer Berlin Heidelberg.Google ScholarDigital Library
- Hans-Georg Beyer and Hans-Paul Schwefel. Evolution strategies -a comprehensive introduction. Natural Computing: An International Journal, 1(1):3--52, May 2002.Google ScholarDigital Library
- Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006.Google ScholarDigital Library
- Linda Burnett, W Millan, Edward Dawson, and A Clark. Simpler methods for generating better boolean functions with good cryptographic properties. Australas. J. Combin., 29:231--248, 2004.Google Scholar
- Linda Dee Burnett. Heuristic Optimization of Boolean Functions and Substitution Boxes for Cryptography. PhD thesis, Queensland University of Technology, 2005.Google Scholar
- Eleonora Cagli, Cécile Dumas, and Emmanuel Prouff. Convolutional Neural Networks with Data Augmentation Against Jitter-Based Countermeasures - Profiling Attacks Without Pre-processing. In Cryptographic Hardware and Embedded Systems - CHES 2017 - 19th International Conference, Taipei, Taiwan, September 25-28, 2017, Proceedings, pages 45--68, 2017.Google ScholarCross Ref
- Claude Carlet. Vectorial Boolean Functions for Cryptography. In Yves Crama and Peter L. Hammer, editors, Boolean Models and Methods in Mathematics, Computer Science, and Engineering, pages 398--469. Cambridge University Press, New York, NY, USA, 1st edition, 2010.Google ScholarCross Ref
- Claude Carlet. Boolean functions for cryptography and error-correcting codes. In Y. Crama, and P. L. Hammer, editors, Boolean Models and Methods in Mathematics, Computer Science, and Engineering, pages 257--397. Cambridge University Press, New York, 2011.Google Scholar
- Claude Carlet and Sylvain Guilley. Correlation-immune Boolean functions for easing counter measures to side-channel attacks, pages 41 -- 70. De Gruyter, Berlin, Boston, 2014.Google Scholar
- Claude Carlet, Annelie Heuser, and Stjepan Picek. Trade-offs for s-boxes: Cryptographic properties and side-channel resilience. In Dieter Gollmann, Atsuko Miyaji, and Hiroaki Kikuchi, editors, Applied Cryptography and Network Security, pages 393--414, Cham, 2017. Springer International Publishing.Google ScholarCross Ref
- Rafael Boix Carpi, Stjepan Picek, Lejla Batina, Federico Menarini, Domagoj Jakobovic, and Marin Golub. Glitch it if you can: Parameter search strategies for successful fault injection. In Aurélien Francillon and Pankaj Rohatgi, editors, Smart Card Research and Advanced Applications, pages 236--252, Cham, 2014. Springer International Publishing.Google ScholarDigital Library
- Jung-Wei Chou, Shou-De Lin, and Chen-Mou Cheng. On the effectiveness of using state-of-the-art machine learning techniques to launch cryptographic distinguishing attacks. In Proceedings of the 5th ACM Workshop on Security and Artificial Intelligence, AISec '12, page 105--110, New York, NY, USA, 2012. Association for Computing Machinery.Google ScholarDigital Library
- Andrew J. Clark. Optimisation heuristics for cryptology. PhD thesis, Queensland University of Technology, 1998.Google Scholar
- J. A. Clark, J. L. Jacob, S. Maitra, and P. Stanica. Almost boolean functions: the design of boolean functions by spectral inversion. In The 2003 Congress on Evolutionary Computation, 2003. CEC '03., volume 3, pages 2173--2180 Vol. 3, 2003.Google ScholarCross Ref
- J. A. Clark, J. L. Jacob, and S. Stepney. The design of s-boxes by simulated annealing. In Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753). volume 2, pages 1533--1537 Vol. 2, 2004.Google ScholarCross Ref
- John A. Clark and Jeremy L. Jacob. Two-stage optimisation in the design of boolean functions. In E. P. Dawson, A. Clark, and Colin Boyd, editors, Information Security and Privacy, pages 242--254, Berlin, Heidelberg, 2000. Springer Berlin Heidelberg.Google ScholarCross Ref
- John A. Clark, Jeremy L. Jacob, Susan Stepney, Subhamoy Maitra, and William Millan. Evolving boolean functions satisfying multiple criteria. In Alfred Menezes and Palash Sarkar, editors, Progress in Cryptology --- INDOCRYPT 2002, pages 246--259, Berlin, Heidelberg, 2002. Springer Berlin Heidelberg.Google ScholarCross Ref
- N. Cruz-Cortes, F. Rodriguez-Henriquez, and C. A. Coello Coello. An artificial immune system heuristic for generating short addition chains. IEEE Transactions on Evolutionary Computation, 12(1):1--24, 2008.Google ScholarDigital Library
- Nareli Cruz-Cortés, Francisco Rodríguez-Henríquez, Raúl Juárez-Morales, and Carlos A. Coello Coello. Finding optimal addition chains using a genetic algorithm approach. In Yue Hao, Jiming Liu, Yuping Wang, Yiu-ming Cheung, Hujun Yin, Licheng Jiao, Jianfeng Ma, and Yong-Chang Jiao, editors, Computational Intelligence and Security, pages 208--215, Berlin, Heidelberg, 2005. Springer Berlin Heidelberg.Google ScholarDigital Library
- Thomas W. Cusick and Pantelimon Stănică. Cryptographic Boolean Functions and Applications. Elsevier Inc., San Diego, USA, 2009.Google Scholar
- M. Danziger and M. A. A. Henriques. Improved cryptanalysis combining differential and artificial neural network schemes. In 2014 International Telecommunications Symposium (ITS), pages 1--5, 2014.Google ScholarCross Ref
- Flávio Luis de Mello and José A. M. Xexéo. Identifying encryption algorithms in ECB and CBC modes using computational intelligence. J. UCS, 24(1):25--42, 2018.Google Scholar
- J. Delvaux. Machine-learning attacks on polypufs, ob-pufs, rpufs, Ihs-pufs, and puf-fsms. IEEE Transactions on Information Forensics and Security, 14(8):2043--2058, 2019.Google ScholarDigital Library
- Saúl Domínguez-Isidro, Efrén Mezura-Montes, and Luis Guillermo Osorio-Hernández. Addition chain length minimization with evolutionary programming. In 13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Companion Material Proceedings, Dublin, Ireland, July 12-16, 2011, pages 59--60, 2011.Google ScholarDigital Library
- Saúl Domínguez-Isidro, Eirén Mezura-Montes, and Luis Guillermo Osorio-Hernández. Evolutionary programming for the length minimization of addition chains. Eng. Appl. of AI, 37:125--134, 2015.Google ScholarCross Ref
- Kamil Dworak and Urszula Boryczka. Cryptanalysis of sdes using modified version of binary particle swarm optimization. In Manuel Núñez, Ngoc Thanh Nguyen, David Camacho, and Bogdan Trawiński, editors, Computational Collective Intelligence, pages 159--168, Cham, 2015. Springer International Publishing.Google ScholarCross Ref
- B. Ege, K. Papagiannopoulos, L. Batina, and S. Picek. Improving dpa resistance of s-boxes: How far can we go? In 2015 IEEE International Symposium on Circuits and Systems (ISCAS), pages 2013--2016, 2015.Google ScholarCross Ref
- A. E. Eiben and James E. Smith. Introduction to Evolutionary Computing. Springer Publishing Company, Incorporated, 2nd edition, 2015.Google ScholarCross Ref
- J. Fuller, W. Millan, and E. Dawson. Multi-objective optimisation of bijective s-boxes. In Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), volume 2, pages 1525--1532 Vol. 2, 2004.Google ScholarCross Ref
- Samaneh Ghandali, Georg T. Becker, Daniel Holcomb, and Christof Paar. A design methodology for stealthy parametric trojans and its application to bug attacks. In Benedikt Gierlichs and Axel Y. Poschmann, editors, Cryptographic Hardware and Embedded Systems - CHES 2016, pages 625--647, Berlin, Heidelberg, 2016. Springer Berlin Heidelberg.Google ScholarCross Ref
- Ashrujit Ghoshal, Rajat Sadhukhan, Sikhar Patranabis, Nilanjan Datta, Stjepan Picek, and Debdeep Mukhopadhyay. Lightweight and side-channel secure 4 × 4 s-boxes from cellular automata rules. IACR Transactions on Symmetric Cryptology, 2018(3):311--334, Sep. 2018.Google ScholarCross Ref
- R. Gilmore, N. Hanley, and M. O'Neill. Neural network based attack on a masked implementation of AES. In 2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pages 106--111, May 2015.Google ScholarCross Ref
- Aron Gohr. Improving attacks on round-reduced speck32/64 using deep learning. In Alexandra Boldyreva and Daniele Micciancio, editors, Advances in Cryptology - CRYPTO 2019, pages 150--179, Cham, 2019. Springer International Publishing.Google ScholarDigital Library
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.Google ScholarDigital Library
- K. Hasegawa, Y. Shi, and N. Togawa. Hardware trojan detection utilizing machine learning approaches. In 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), pages 1891--1896, 2018.Google ScholarCross Ref
- J. C. Hernandez, A. Seznec, and P. Isasi. On the design of state-of-the-art pseudorandom number generators by means of genetic programming. In Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), volume 2, pages 1510--1516 Vol. 2, 2004.Google ScholarCross Ref
- Benjamin Hettwer, Stefan Gehrer, and Tim Güneysu. Profiled power analysis attacks using convolutional neural networks with domain knowledge. In Carlos Cid and Michael J. Jacobson Jr., editors, Selected Areas in Cryptography - SAC 2018 - 25th International Conference, Calgary, AB, Canada, August 15-17, 2018, Revised Selected Papers, volume 11349 of Lecture Notes in Computer Science, pages 479--498. Springer, 2018.Google Scholar
- A. Heuser, S. Picek, S. Guilley, and N. Mentens. Lightweight ciphers and their side-channel resilience. IEEE Transactions on Computers, PP(99):1--1, 2017.Google ScholarCross Ref
- Annelie Heuser, Stjepan Picek, Sylvain Guilley, and Nele Mentens. Side-channel analysis of lightweight ciphers: Does lightweight equal easy? In Radio Frequency Identification and IoT Security - 12th International Workshop, RFIDSec 2016, Hong Kong, China, November 30 - December 2, 2016, Revised Selected Papers, pages 91--104, 2016.Google Scholar
- Annelie Heuser and Michael Zohner. Intelligent Machine Homicide - Breaking Cryptographic Devices Using Support Vector Machines. In Werner Schindler and Sorin A. Huss, editors, COSADE, volume 7275 of LNCS, pages 249--264. Springer, 2012.Google Scholar
- John H. Holland. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge, MA, USA, 1992.Google ScholarCross Ref
- G Hospodar, E De Mulder, and B Gierlichs. Least squares support vector machines for side-channel analysis. Center for Advanced Security Research Darmstadt, pages 99--104, 01 2011.Google Scholar
- Gabriel Hospodar, Benedikt Gierlichs, Elke De Mulder, Ingrid Verbauwhede, and Joos Vandewalle. Machine learning in side-channel analysis: a first study. Journal of Cryptographic Engineering, 1:293--302, 2011. Google ScholarCross Ref
- Radek Hrbacek and Vaclav Dvorak. Bent function synthesis by means of cartesian genetic programming. In Thomas Bartz-Beielstein, Jürgen Branke, Bogdan Filipič, and Jim Smith, editors, Parallel Problem Solving from Nature - PPSN XIII, pages 414--423, Cham, 2014. Springer International Publishing.Google ScholarCross Ref
- Georgi Ivanov, Nikolay Nikolov, and Svetla Nikova. Cryptographically strong s-boxes generated by modified immune algorithm. In Enes Pasalic and Lars R. Knudsen, editors, Cryptography and Information Security in the Balkans, pages 31--42, Cham, 2016. Springer International Publishing.Google ScholarCross Ref
- Georgi Ivanov, Nikolay Nikolov, and Svetla Nikova. Reversed genetic algorithms for generation of bijective s-boxes with good cryptographic properties. Cryptography Commun., 8(2):247--276, April 2016.Google ScholarDigital Library
- T. Iwase, Y. Nozaki, M. Yoshikawa, and T. Kumaki. Detection technique for hardware trojans using machine learning in frequency domain. In 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE), pages 185--186, 2015.Google ScholarCross Ref
- Domagoj Jakobovic, Stjepan Picek, Marcella S. R. Martins, and Markus Wagner. A characterisation of s-box fitness landscapes in cryptography. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '19, page 285--293, New York, NY, USA, 2019. Association for Computing Machinery.Google ScholarDigital Library
- N. Karimian, F. Tehranipoor, M. T. Rahman, S. Kelly, and D. Forte. Genetic algorithm for hardware trojan detection with ring oscillator network (ron). In 2015 IEEE International Symposium on Technologies for Homeland Security (HST), pages 1--6, 2015.Google ScholarCross Ref
- Jonathan Katz and Yehuda Lindell. Introduction to Modern Cryptography (Chapman & Hall/Crc Cryptography and Network Security Series). Chapman & Hall/CRC, 2007.Google ScholarDigital Library
- Jaehun Kim, Stjepan Picek, Annelie Heuser, Shivam Bhasin, and Alan Hanjalic. Make some noise. unleashing the power of convolutional neural networks for profiled side-channel analysis. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2019(3):148--179, May 2019.Google ScholarCross Ref
- Lars R. Knudsen and Matthew J. B. Robshaw. The Block Cipher Companion. Springer Publishing Company, Incorporated, 2011.Google ScholarCross Ref
- John Koza. Evolving a computer program to generate random numbers using the genetic programming paradigm. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 37--44. Morgan Kaufmann, 1991.Google Scholar
- John R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.Google ScholarDigital Library
- W. O. Krawec. A genetic algorithm to analyze the security of quantum cryptographic protocols. In 2016 IEEE Congress on Evolutionary Computation (CEC), pages 2098--2105, 2016.Google ScholarDigital Library
- Walter Krawec, Stjepan Picek, and Domagoj Jakobovic. Evolutionary algorithms for the design of quantum protocols. In Paul Kaufmann and Pedro A. Castillo, editors, Applications of Evolutionary Computation, pages 220--236, Cham, 2019. Springer International Publishing.Google ScholarCross Ref
- Walter O. Krawec, Michael G. Nelson, and Eric P. Geiss. Automatic generation of optimal quantum key distribution protocols. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '17, page 1153--1160, New York, NY, USA, 2017. Association for Computing Machinery.Google ScholarDigital Library
- A. Kulkarni, Y. Pino, and T. Mohsenin. Adaptive real-time trojan detection framework through machine learning. In 2016 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pages 120--123, 2016.Google ScholarCross Ref
- Mansoureh Labafniya, Stjepan Picek, Shahram [Etemadi Borujeni], and Nele Mentens. On the feasibility of using evolvable hardware for hardware trojan detection and prevention. Applied Soft Computing, 91:106247, 2020.Google ScholarCross Ref
- Linus Lagerhjelm. Extracting information from encrypted data using deep neural networks. Master's thesis, Umeå University, Department of Applied Physics and Electronics, 2018.Google Scholar
- Carlos Lamenca-Martinez, Julio Cesar Hernandez-Castro, Juan M. Estevez-Tapiador, and Arturo Ribagorda. Lamar: A new pseudorandom number generator evolved by means of genetic programming. In Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervós, L. Darrell Whitley, and Xin Yao, editors, Parallel Problem Solving from Nature - PPSN IX, pages 850--859, Berlin, Heidelberg, 2006. Springer Berlin Heidelberg.Google Scholar
- E. C. Laskari, G. C. Meletiou, Y. C. Stamatiou, and M. N. Vrahatis. Cryptography and cryptanalysis through computational intelligence. In Nadia Nedjah, Ajith Abraham, and Luiza de Macedo Mourelle, editors, Computational Intelligence in Information Assurance and Security, pages 1--49, Berlin, Heidelberg, 2007. Springer Berlin Heidelberg.Google ScholarCross Ref
- Alejandro León-Javier, Nareli Cruz-Cortés, Marco A. Moreno-Armendáriz, and Sandra Orantes-Jiménez. Finding minimal addition chains with a particle swarm optimization algorithm. In Arturo Hernández Aguirre, Raúl Monroy Borja, and Carlos Alberto Reyes Garciá, editors, MICAI 2009: Advances in Artificial Intelligence, pages 680--691, Berlin, Heidelberg, 2009. Springer Berlin Heidelberg.Google ScholarDigital Library
- Liran Lerman, Romain Poussier, Gianluca Bontempi, Olivier Markowitch, and François-Xavier Standaert. Template Attacks vs. Machine Learning Revisited (and the Curse of Dimensionality in Side-Channel Analysis). In COSADE 2015, Berlin, Germany, 2015. Revised Selected Papers, pages 20--33, 2015.Google ScholarDigital Library
- Liran Lerman, Nikita Veshchikov, Stjepan Picek, and Olivier Markowitch. Higher order side-channel attack resilient s-boxes. In Proceedings of the 15th ACM International Conference on Computing Frontiers, CF '18, page 336--341, New York, NY, USA, 2018. Association for Computing Machinery.Google ScholarDigital Library
- Houssem Maghrebi, Thibault Portigliatti, and Emmanuel Prouff. Breaking cryptographic implementations using deep learning techniques.Google Scholar
- In Security, Privacy, and Applied Cryptography Engineering - 6th International Conference, SPACE 2016, Hyderabad, India, December 14-18, 2016, Proceedings, pages 3--26, 2016.Google Scholar
- A. Maldini, N. Samwel, S. Picek, and L. Batina. Genetic algorithm-based electromagnetic fault injection. In 2018 Workshop on Fault Diagnosis and Tolerance in Cryptography (FDTC), pages 35--42, 2018.Google ScholarCross Ref
- Antun Maldini, Niels Samwel, Stjepan Picek, and Lejla Batina. Optimizing electromagnetic fault injection with genetic algorithms. In Jakub Breier, Xiaolu Hou, and Shivam Bhasin, editors, Automated Methods in Cryptographic Fault Analysis, pages 281--300, Cham, 2019. Springer International Publishing.Google ScholarCross Ref
- Stefan Mangard, Elisabeth Oswald, and Thomas Popp. Power Analysis Attacks: Revealing the Secrets of Smart Cards (Advances in Information Security). Springer-Verlag, Berlin, Heidelberg, 2007.Google Scholar
- Luca Manzoni, Luca Mariot, and Eva Tuba. Does constraining the search space of ga always help? the case of balanced crossover operators. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '19, page 151--152, New York, NY, USA, 2019. Association for Computing Machinery.Google ScholarDigital Library
- Luca Manzoni, Luca Mariot, and Eva Tuba. Balanced crossover operators in genetic algorithms. Swarm and Evolutionary Computation, 54:100646, 2020.Google ScholarCross Ref
- Luca Mariot, Domagoj Jakobovic, Alberto Leporati, and Stjepan Picek. Hyper-bent boolean functions and evolutionary algorithms.Google Scholar
- In Lukas Sekanina, Ting Hu, Nuno Lourenço, Hendrik Richter, and Pablo García-Sánchez, editors, Genetic Programming, pages 262--277, Cham, 2019. Springer International Publishing.Google ScholarDigital Library
- Luca Mariot and Alberto Leporati. A genetic algorithm for evolving plateaued cryptographic boolean functions. In Adrian-Horia Dediu, Luis Magdalena, and Carlos Martín-Vide, editors, Theory and Practice of Natural Computing, pages 33--45, Cham, 2015. Springer International Publishing.Google ScholarDigital Library
- Luca Mariot and Alberto Leporati. Heuristic search by particle swarm optimization of boolean functions for cryptographic applications. In Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO Companion '15, page 1425--1426, New York, NY, USA, 2015. Association for Computing Machinery.Google ScholarDigital Library
- Luca Mariot, Stjepan Picek, Domagoj Jakobovic, and Alberto Leporati. Evolutionary search of binary orthogonal arrays. In Anne Auger, Carlos M. Fonseca, Nuno Lourenço, Penousal Machado, Luís Paquete, and Darrell Whitley, editors, Parallel Problem Solving from Nature - PPSN XV, pages 121--133, Cham, 2018. Springer International Publishing.Google ScholarCross Ref
- Luca Mariot, Stjepan Picek, Domagoj Jakobovic, and Alberto Leporati. An evolutionary view on reversible shift-invariant transformations. In Ting Hu, Nuno Lourenço, Eric Medvet, and Federico Divina, editors, Genetic Programming, pages 118--134, Cham, 2020. Springer International Publishing.Google ScholarDigital Library
- Luca Mariot, Stjepan Picek, Alberto Leporati, and Domagoj Jakobovic. Cellular automata based s-boxes. Cryptography and Communications, 11(1):41--62, 2019.Google ScholarDigital Library
- Alfred J. Menezes, Paul C. van Oorschot, and Scott A. Vanstone. Handbook of Applied Cryptography. CRC Press, 2001.Google ScholarDigital Library
- W. Millan, L. Burnett, G. Carter, A. Clark, and E. Dawson. Evolutionary heuristics for finding cryptographically strong s-boxes. In Vijay Varadharajan and Yi Mu, editors, Information and Communication Security, pages 263--274, Berlin, Heidelberg, 1999. Springer Berlin Heidelberg.Google ScholarCross Ref
- W. Millan, J. Fuller, and E. Dawson. New concepts in evolutionary search for boolean functions in cryptology. In The 2003 Congress on Evolutionary Computation, 2003. CEC '03., volume 3, pages 2157--2164 Vol. 3, 2003.Google ScholarCross Ref
- William Millan, Andrew Clark, and Ed Dawson. An effective genetic algorithm for finding highly nonlinear boolean functions. In Yongfei Han, Tatsuaki Okamoto, and Sihan Qing, editors, Information and Communications Security, pages 149--158, Berlin, Heidelberg, 1997. Springer Berlin Heidelberg.Google ScholarCross Ref
- William Millan, Andrew Clark, and Ed Dawson. Heuristic design of cryptographically strong balanced boolean functions. In Kaisa Nyberg, editor, Advances in Cryptology --- EUROCRYPT98, pages 489--499, Berlin, Heidelberg, 1998. Springer Berlin Heidelberg.Google ScholarCross Ref
- William Millan, Andrew Clark, and Ed Dawson. Boolean function design using hill climbing methods. In Josef Pieprzyk, Rei Safavi-Naini, and Jennifer Seberry, editors, Information Security and Privacy, pages 1--11, Berlin, Heidelberg, 1999. Springer Berlin Heidelberg.Google ScholarCross Ref
- Julian F. Miller. An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach. In Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 2, GECCO'99, page 1135--1142, San Francisco, CA, USA, 1999. Morgan Kaufmann Publishers Inc.Google Scholar
- Julian F. Miller. Cartesian genetic programming. In Julian F. Miller, editor, Cartesian Genetic Programming, pages 17--34, Berlin, Heidelberg, 2011. Springer Berlin Heidelberg.Google ScholarCross Ref
- Thomas M. Mitchell. Machine Learning. McGraw-Hill, Inc., New York, NY, USA, 1 edition, 1997.Google ScholarDigital Library
- Nadia Nedjah and Luiza de Macedo Mourelle. Minimal addition chain for efficient modular exponentiation using genetic algorithms. In Tim Hendtlass and Moonis Ali, editors, Developments in Applied Artificial Intelligence, pages 88--98, Berlin, Heidelberg, 2002. Springer Berlin Heidelberg.Google ScholarCross Ref
- Nadia Nedjah and Luiza de Macedo Mourelle. Minimal addition-subtraction chains using genetic algorithms. In Tatyana Yakhno, editor, Advances in Information Systems, pages 303--313, Berlin, Heidelberg, 2002. Springer Berlin Heidelberg.Google ScholarCross Ref
- Nadia Nedjah and Luiza de Macedo Mourelle. Minimal Addition-Subtraction Sequences for Efficient Pre-processing in Large Window-Based Modular Exponentiation Using Genetic Algorithms. In Jiming Liu, Yiu-ming Cheung, and Hujun Yin, editors, Intelligent Data Engineering and Automated Learning, volume 2690 of Lect. Notes in Comp. Science, pages 329--336. Springer, 2003.Google ScholarCross Ref
- Nadia Nedjah and Luiza de Macedo Mourelle. Finding minimal addition chains using ant colony. In Zheng Rong Yang, Hujun Yin, and Richard M. Everson, editors, Intelligent Data Engineering and Automated Learning - IDEAL 2004, pages 642--647, Berlin, Heidelberg, 2004. Springer Berlin Heidelberg.Google ScholarCross Ref
- Nadia Nedjah and Luiza de Macedo Mourelle. High-performance SoC-based Implementation of Modular Exponentiation Using Evolutionary Addition Chains for Efficient Cryptography. Applied Soft Computing, 11(7):4302--4311, October 2011.Google ScholarDigital Library
- David Oranchak. Evolutionary algorithm for decryption of monoalphabetic homophonic substitution ciphers encoded as constraint satisfaction problems. In Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO '08, page 1717--1718, New York, NY, USA, 2008. Association for Computing Machinery.Google ScholarDigital Library
- L. G. Osorio-Hernandez, E. Mezura-Montes, N. Cruz-Cortes, and F. Rodriguez-Henriquez. A genetic algorithm with repair and local search mechanisms able to find minimal length addition chains for small exponents. In 2009 IEEE Congress on Evolutionary Computation, pages 1422--1429, 2009.Google ScholarCross Ref
- Artem Pavlenko, Alexander Semenov, and Vladimir Ulyantsev. Evolutionary computation techniques for constructing sat-based attacks in algebraic cryptanalysis. In Paul Kaufmann and Pedro A. Castillo, editors, Applications of Evolutionary Computation, pages 237--253, Cham, 2019. Springer International Publishing.Google ScholarCross Ref
- Pedro Peris-Lopez, Julio Cesar Hernandez-Castro, Juan M. Estevez-Tapiador, and Arturo Ribagorda. Lamed - a prng for epc class-1 generation-2 rfid specification. Comput. Stand. Interfaces, 31(1):88--97, January 2009.Google ScholarDigital Library
- S. Picek, L. Batina, D. Jakolović, and R. B. Carpi. Evolving genetic algorithms for fault injection attacks. In 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 1106--1111, 2014.Google ScholarCross Ref
- S. Picek, B. Ege, K. Papagiannopoulos, L. Batina, and D. Jakobović. Optimality and beyond: The case of 4 × 4 s-boxes. In 2014 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST), pages 80--83, 2014.Google ScholarCross Ref
- S. Picek, A. Heuser, A. Jovic, and L. Batina. A systematic evaluation of profiling through focused feature selection. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 27(12):2802--2815, 2019.Google ScholarCross Ref
- S. Picek, K. Knezevic, and D. Jakobovic. On the evolution of bent (n, m) functions. In 2017 IEEE Congress on Evolutionary Computation (CEC), pages 2137--2144, 2017.Google ScholarDigital Library
- S. Picek, K. Knezevic, D. Jakobovic, and C. Carlet. A search for differentially-6 uniform (n, n-2) functions. In 2018 IEEE Congress on Evolutionary Computation (CEC), pages 1--7, 2018.Google ScholarDigital Library
- S. Picek, K. Knezevic, L. Mariot, D. Jakobovic, and A. Leporati. Evolving bent quaternary functions. In 2018 IEEE Congress on Evolutionary Computation (CEC), pages 1--8, 2018.Google ScholarDigital Library
- Stjepan Picek. Applications of evolutionary computation to cryptology. PhD thesis, Radboud University Nijmegen, The Netherlands, 2015.Google Scholar
- Stjepan Picek. Evolutionary computation and cryptology. In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, GECCO '16 Companion, page 883--909, New York, NY, USA, 2016. Association for Computing Machinery.Google ScholarDigital Library
- Stjepan Picek. Applications of soft computing in cryptology. In Dooho Choi and Sylvain Guilley, editors, Information Security Applications, pages 305--317, Cham, 2017. Springer International Publishing.Google ScholarCross Ref
- Stjepan Picek. Challenges in deep learning-based profiled side-channel analysis. In Shivam Bhasin, Avi Mendelson, and Mridul Nandi, editors, Security, Privacy, and Applied Cryptography Engineering, pages 9--12, Cham, 2019. Springer International Publishing.Google Scholar
- Stjepan Picek, Lejla Batina, Pieter Buzing, and Domagoj Jakobovic. Fault injection with a new flavor: Memetic algorithms make a difference. In Stefan Mangard and Axel Y. Poschmann, editors, Constructive Side-Channel Analysis and Secure Design, pages 159--173, Cham, 2015. Springer International Publishing.Google ScholarDigital Library
- Stjepan Picek, Lejla Batina, and Domagoj Jakobovic. Evolving dpa-resistant boolean functions. In Thomas Bartz-Beielstein, Jürgen Branke, Bogdan Filipič, and Jim Smith, editors, Parallel Problem Solving from Nature - PPSN XIII, pages 812--821, Cham, 2014. Springer International Publishing.Google ScholarCross Ref
- Stjepan Picek, Lejla Batina, Domagoj Jakobović, Barış Ege, and Marin Golub. S-box, set, match: A toolbox for s-box analysis. In David Naccache and Damien Sauveron, editors, Information Security Theory and Practice. Securing the Internet of Things, pages 140--149, Berlin, Heidelberg, 2014. Springer Berlin Heidelberg.Google ScholarDigital Library
- Stjepan Picek, Claude Carlet, Sylvain Guilley, Julian F. Miller, and Domagoj Jakobovic. Evolutionary algorithms for boolean functions in diverse domains of cryptography. Evolutionary Computation, 24(4):667--694, 2016.Google ScholarDigital Library
- Stjepan Picek, Claude Carlet, Domagoj Jakobovic, Julian F. Miller, and Lejla Batina. Correlation immunity of boolean functions: An evolutionary algorithms perspective. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO '15, page 1095--1102, New York, NY, USA, 2015. Association for Computing Machinery.Google ScholarDigital Library
- Stjepan Picek, Carlos A. Coello, Domagoj Jakobovic, and Nele Mentens. Finding short and implementation-friendly addition chains with evolutionary algorithms. Journal of Heuristics, 24(3):457--481, June 2018.Google ScholarDigital Library
- Stjepan Picek, Carlos A. Coello Coello, Domagoj Jakobovic, and Nele Mentens. Evolutionary algorithms for finding short addition chains: Going the distance. In Francisco Chicano, Bin Hu, and Pablo García-Sánchez, editors, Evolutionary Computation in Combinatorial Optimization, pages 121--137, Cham, 2016. Springer International Publishing.Google ScholarCross Ref
- Stjepan Picek, Marko Cupic, and Leon Rotim. A new cost function for evolution of s-boxes. Evolutionary Computation, 24(4):695--718, 2016.Google ScholarDigital Library
- Stjepan Picek, Bariş Ege, Lejla Batina, Domagoj Jakobovic, undefinedukasz Chmielewski, and Marin Golub. On using genetic algorithms for intrinsic side-channel resistance: The case of aes s-box. In Proceedings of the First Workshop on Cryptography and Security in Computing Systems, CS2 '14, page 13--18, New York, NY, USA, 2014. Association for Computing Machinery.Google ScholarDigital Library
- Stjepan Picek, Sylvain Guilley, Claude Carlet, Domagoj Jakobovic, and Julian F. Miller. Evolutionary approach for finding correlation immune boolean functions of order t with minimal hamming weight. In Adrian-Horia Dediu, Luis Magdalena, and Carlos Martín-Vide, editors, Theory and Practice of Natural Computing, pages 71--82 Cham, 2015 Springer International Publishing.Google ScholarDigital Library
- Stjepan Picek, Annelie Heuser, and Sylvain Guilley. Template attack versus Bayes classifier. Journal of Cryptographic Engineering, 7(4):343--351, Nov 2017.Google ScholarCross Ref
- Stjepan Picek, Annelie Heuser, Alan Jovic, Shivam Bhasin, and Francesco Regazzoni. The curse of class imbalance and conflicting metrics with machine learning for side-channel evaluations. IACR Trans. Cryptogr. Hardw. Embed. Syst., 2019(1):209--237, 2019.Google Scholar
- Stjepan Picek, Annelie Heuser, Alan Jovic, Simone A. Ludwig, Sylvain Guilley, Domagoj Jakobovic, and Nele Mentens. Side-channel analysis and machine learning: A practical perspective. In 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, AK, USA, May 14-19, 2017, pages 4095--4102, 2017.Google ScholarCross Ref
- Stjepan Picek and Domagoj Jakobovic. Evolving algebraic constructions for designing bent boolean functions. In Proceedings of the Genetic and Evolutionary Computation Conference 2016, GECCO '16, page 781--788, New York, NY, USA, 2016. Association for Computing Machinery.Google ScholarDigital Library
- Stjepan Picek and Domagoj Jakobovic. On the design of s-box constructions with genetic programming. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '19, page 395--396, New York, NY, USA, 2019. Association for Computing Machinery.Google ScholarDigital Library
- Stjepan Picek, Domagoj Jakobovic, and Marin Golub. Evolving cryptographically sound boolean functions. In Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO '13 Companion, page 191--192, New York, NY, USA, 2013. Association for Computing Machinery.Google ScholarDigital Library
- Stjepan Picek, Domagoj Jakobovic, Julian F. Miller, Lejla Batina, and Marko Cupic. Cryptographic boolean functions: One output, many design criteria. Applied Soft Computing, 40:635 -- 653, 2016.Google Scholar
- Stjepan Picek, Domagoj Jakobovic, Julian F. Miller, Elena Marchiori, and Lejla Batina. Evolutionary methods for the construction of cryptographic boolean functions. In Penousal Machado, Malcolm I. Heywood, James McDermott, Mauro Castelli, Pablo García-Sánchez, Paolo Burelli, Sebastian Risi, and Kevin Sim, editors, Genetic Programming, pages 192--204, Cham, 2015. Springer International Publishing.Google ScholarCross Ref
- Stjepan Picek, Domagoj Jakobovic, and Una-May O'Reilly. Cryptobench: Benchmarking evolutionary algorithms with cryptographic problems. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '17, page 1597--1604, New York, NY, USA, 2017. Association for Computing Machinery.Google ScholarDigital Library
- Stjepan Picek, Karlo Knezevic, Domagoj Jakobovic, and Ante Derek. C3po: Cipher construction with cartesian genetic programming. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '19, page 1625--1633, New York, NY, USA, 2019. Association for Computing Machinery.Google ScholarDigital Library
- Stjepan Picek, Elena Marchiori, Lejla Batina, and Domagoj Jakobovic. Combining evolutionary computation and algebraic constructions to find cryptography-relevant boolean functions. In Thomas Bartz-Beielstein, Jürgen Branke, Bogdan Filipič, and Jim Smith, editors, Parallel Problem Solving from Nature - PPSN XIII, pages 822--831, Cham, 2014. Springer International Publishing.Google ScholarCross Ref
- Stjepan Picek, Luca Mariot, Alberto Leporati, and Domagoj Jakobovic. Evolving s-boxes based on cellular automata with genetic programming. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '17, page 251--252, New York, NY, USA, 2017. Association for Computing Machinery.Google ScholarDigital Library
- Stjepan Picek, Luca Mariot, Bohan Yang, Domagoj Jakobovic, and Nele Mentens. Design of s-boxes defined with cellular automata rules. In Proceedings of the Computing Frontiers Conference, CF'17, page 409--414, New York, NY, USA, 2017. Association for Computing Machinery.Google ScholarDigital Library
- Stjepan Picek, Bodhisatwa Mazumdar, Debdeep Mukhopadhyay, and Lejla Batina. Modified transparency order property: Solution or just another attempt. In Rajat Subhra Chakraborty, Peter Schwabe, and Jon Solworth, editors, Security, Privacy, and Applied Cryptography Engineering, pages 210--227, Cham, 2015. Springer International Publishing.Google Scholar
- Stjepan Picek, Robert I. McKay, Roberto Santana, and Tom D. Gedeon. Fighting the symmetries: The structure of cryptographic boolean function spaces. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO '15, page 457--464, New York, NY, USA, 2015. Association for Computing Machinery.Google ScholarDigital Library
- Stjepan Picek, Julian F. Miller, Domagoj Jakobovic, and Lejla Batina. Cartesian genetic programming approach for generating substitution boxes of different sizes. In Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO Companion '15, page 1457--1458, New York, NY, USA, 2015. Association for Computing Machinery.Google ScholarDigital Library
- Stjepan Picek, Kostas Papagiannopoulos, Barış Ege, Lejla Batina, and Domagoj Jakobovic. Confused by confusion: Systematic evaluation of dpa resistance of various s-boxes. In Willi Meier and Debdeep Mukhopadhyay, editors, Progress in Cryptology - INDOCRYPT 2014, pages 374--390, Cham, 2014. Springer International Publishing.Google ScholarDigital Library
- Stjepan Picek, Ioannis Petros Samiotis, Jaehun Kim, Annelie Heuser, Shivam Bhasin, and Axel Legay. On the performance of convolutional neural networks for side-channel analysis. In Anupam Chattopadhyay, Chester Rebeiro, and Yuval Yarom, editors, Security, Privacy, and Applied Cryptography Engineering, pages 157--176, Cham, 2018. Springer International Publishing.Google Scholar
- Stjepan Picek, Dominik Sisejkovic, and Domagoj Jakobovic. Immunological algorithms paradigm for construction of boolean functions with good cryptographic properties. Engineering Applications of Artificial Intelligence, 62:320 -- 330, 2017.Google Scholar
- Stjepan Picek, Dominik Sisejkovic, Domagoj Jakobovic, Lejla Batina, Bohan Yang, Danilo Sijacic, and Nele Mentens. Extreme pipelining towards the best area-performance trade-off in hardware. In David Pointcheval, Abderrahmane Nitaj, and Tajjeeddine Rachidi, editors, Progress in Cryptology - AFRICACRYPT 2016, pages 147--166, Cham, 2016. Springer International Publishing.Google ScholarDigital Library
- Stjepan Picek, Dominik Sisejkovic, Vladimir Rozic, Bohan Yang, Domagoj Jakobovic, and Nele Mentens. Evolving cryptographic pseudorandom number generators.Google Scholar
- In Julia Handl, Emma Hart, Peter R. Lewis, Manuel López-Ibáñez, Gabriela Ochoa, and Ben Paechter, editors, Parallel Problem Solving from Nature - PPSN XIV, pages 613--622, Cham, 2016. Springer International Publishing.Google Scholar
- Stjepan Picek, Bohan Yang, Vladimir Rozic, and Nele Mentens. On the construction of hardware-friendly 4 × 4 and 5 × 5 s-boxes. In Roberto Avanzi and Howard Heys, editors, Selected Areas in Cryptography - SAC 2016, pages 161--179, Cham, 2017. Springer International Publishing.Google ScholarCross Ref
- Stjepan Picek, Bohan Yang, Vladimir Rozic, Jo Vliegen, Jori Winderickx, Thomas De Cnudde, and Nele Mentens. Prngs for masking applications and their mapping to evolvable hardware. In Kerstin Lemke-Rust and Michael Tunstall, editors, Smart Card Research and Advanced Applications, pages 209--227, Cham, 2017. Springer International Publishing.Google ScholarCross Ref
- A. Poorghanad, A. Sadr, and A. Kashanipour. Generating high quality pseudo random number using evolutionary methods. In 2008 International Conference on Computational Intelligence and Security, volume 1, pages 331--335, 2008.Google ScholarDigital Library
- Emmanuel Prouff, Remi Strullu, Ryad Benadjila, Eleonora Cagli, and Cécile Dumas. Study of deep learning techniques for side-channel analysis and introduction to ASCAD database. IACR Cryptology ePrint Archive, 2018:53, 2018.Google Scholar
- Ulrich Rührmair, Frank Sehnke, Jan Sölter, Gideon Dror, Srinivas Devadas, and Jürgen Schmidhuber. Modeling attacks on physical unclonable functions. In Proceedings of the 17th ACM Conference on Computer and Communications Security, CCS '10, page 237--249, New York, NY, USA, 2010. Association for Computing Machinery.Google ScholarDigital Library
- U. Rührmair, J. Sölter, F. Sehnke, X. Xu, A. Mahmoud, V. Stoyanova, G. Dror, J. Schmidhuber, W. Burleson, and S. Devadas. Puf modeling attacks on simulated and silicon data. IEEE Transactions on Information Forensics and Security, 8(11):1876--1891, 2013.Google ScholarDigital Library
- U. Rührmair and M. van Dijk. Pufs in security protocols: Attack models and security evaluations. In 2013 IEEE Symposium on Security and Privacy, pages 286--300, 2013.Google ScholarDigital Library
- Sayandeep Saha, Rajat Subhra Chakraborty, Srinivasa Shashank Nuthakki, Anshul, and Debdeep Mukhopadhyay. Improved test pattern generation for hardware trojan detection using genetic algorithm and boolean satisfiability. In Tim Güneysu and Helena Handschuh, editors, Cryptographic Hardware and Embedded Systems - CHES 2015 - 17th International Workshop, Saint-Malo, France, September 13-16, 2015, Proceedings, volume 9293 of Lecture Notes in Computer Science, pages 577--596. Springer, 2015.Google ScholarDigital Library
- Sayandeep Saha, Dirmanto Jap, Sikhar Patranabis, Debdeep Mukhopadhyay, Shivam Bhasin, and Pallab Dasgupta. Automatic characterization of exploitable faults: A machine learning approach. IEEE Trans. Information Forensics and Security, 14(4):954--968, 2019.Google ScholarCross Ref
- Bruce Schneier. Applied Cryptography (2nd Ed.): Protocols, Algorithms, and Source Code in C. John Wiley & Sons, Inc., USA, 1995.Google ScholarDigital Library
- Lukáš Sekanina. Virtual reconfigurable circuits for real-world applications of evolvable hardware. In AAndy M. Tyrrell, Pauline C. Haddow, and Jim Torresen, editors, Evolvable Systems: From Biology to Hardware, pages 186--197, Berlin, Heidelberg, 2003. Springer Berlin Heidelberg.Google ScholarCross Ref
- Lee Spector. Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming). Springer-Verlag, Berlin, Heidelberg, 2006.Google Scholar
- Petr Tesař. A New Method for Generating High Non-linearity S-Boxes. Radioengineering, 19(1):23--26, April 2010.Google Scholar
- Johannes Tobisch and Georg T. Becker. On the scaling of machine learning attacks on pufs with application to noise bifurcation. In Stefan Mangard and Patrick Schaumont, editors, Radio Frequency Identification, pages 17--31, Cham, 2015. Springer International Publishing.Google ScholarDigital Library
- Léo Weissbart, Stjepan Picek, and Lejla Batina. One trace is all it takes: Machine learning-based side-channel attack on eddsa. In Shivam Bhasin, Avi Mendelson, and Mridul Nandi, editors, Security, Privacy, and Applied Cryptography Engineering, pages 86--105, Cham, 2019. Springer International Publishing.Google Scholar
- Nils Wisiol, Georg T. Becker, Marian Margraf, Tudor A. A. Soroceanu, Johannes Tobisch, and Benjamin Zengin. Breaking the lightweight secure puf: Understanding the relation of input transformations and machine learning resistance. In Sonia Belaïd and Tim Güneysu, editors, Smart Card Research and Advanced Applications, pages 40--54, Cham, 2020. Springer International Publishing.Google ScholarCross Ref
- Stephen Wolfram. Random sequence generation by cellular automata. Adv. Appl. Math., 7(2):123--169, June 1986.Google ScholarDigital Library
- Lichao Wu, Gerard Ribera, Noemie Beringuier-Boher, and Stjepan Picek. A fast characterization method for semi-invasive fault injection attacks. In Stanislaw Jarecki, editor, Topics in Cryptology - CT-RSA 2020, pages 146--170, Cham, 2020. Springer International Publishing.Google ScholarDigital Library
- Evolutionary computation and machine learning in cryptology
Recommendations
Evolutionary computation and machine learning in cryptology
GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion
Comments