{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:06:32Z","timestamp":1767189992675,"version":"3.48.0"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:00:00Z","timestamp":1767052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Recent advances in machine learning have enabled highly effective ciphertext-based cryptographic algorithm identification, posing a potential threat to encrypted communication. Inspired by adversarial example techniques, we present CSPM (Class-Specific Perturbation Mask Generation), a novel adversarial-defense framework that enhances ciphertext unidentifiability through misleading machine-learning-based cipher classifiers. CPSM constructs lightweight, reversible bit-level perturbations that alter statistical ciphertext features without affecting legitimate decryption. The method leverages class prototypes to capture representative bit-distribution patterns for each cryptographic algorithm and integrates two complementary mechanisms\u2014mimicry-based perturbing, which steers ciphertexts toward similar cipher classes, and distortion-based perturbing, which disrupts distinctive statistical traits\u2014through a ranking-based greedy search. Extensive experiments on seven widely used cryptographic algorithms and fifteen NIST statistical feature configurations demonstrate that CSPM consistently reduces algorithm-identification accuracy by over 25%. These results confirm that perturbation position selection, rather than magnitude, dominates attack efficacy. CSPM provides a practical defense mechanism, offering a new perspective for safeguarding encrypted communications against statistical and machine-learning-based traffic analysis.<\/jats:p>","DOI":"10.3390\/bdcc10010013","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T13:30:52Z","timestamp":1767187852000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adversarial Perturbations for Defeating Cryptographic Algorithm Identification"],"prefix":"10.3390","volume":"10","author":[{"given":"Shuijun","family":"Yin","sequence":"first","affiliation":[{"name":"School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Wuhan Maritime Communication Research Institute, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1990-4323","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haolan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.cose.2013.04.004","article-title":"From Information Security to Cyber Security","volume":"38","author":"Solms","year":"2013","journal-title":"Comput. Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.17648\/enig.v3i1.55","article-title":"Machine Learning for Cryptographic Algorithm Identification","volume":"3","author":"Barbosa","year":"2016","journal-title":"J. Inf. Secur. Cryptogr. (Enigma)"},{"key":"ref_3","unstructured":"Han, S.J., Oh, H.S., and Park, J. (1996, January 25). The Improved Data Encryption Standard (DES) Algorithm. Proceedings of the ISSSTA\u201995 International Symposium on Spread Spectrum Techniques and Applications, Mainz, Germany."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Akkar, M.L., and Giraud, C. (2001). An Implementation of DES and AES, Secure Against Some Attacks. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/3-540-44709-1_26"},{"key":"ref_5","unstructured":"Shand, M., and Vuillemin, J. (July, January 29). Fast Implementations of RSA Cryptography. Proceedings of the IEEE 11th Symposium on Computer Arithmetic, Windsor, ON, Canada."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alani, M.M. (2019, January 19\u201321). Applications of Machine Learning in Cryptography: A Survey. Proceedings of the 3rd International Conference on Cryptography, Security and Privacy, Kuala Lumpur, Malaysia.","DOI":"10.1145\/3309074.3309092"},{"key":"ref_7","first-page":"42","article-title":"Performance evaluation of classifiers used for identification of encryption algorithms","volume":"2","author":"Sharif","year":"2011","journal-title":"ACEEE Int. J. Netw. Secur."},{"key":"ref_8","first-page":"18","article-title":"Review on fifteen Statistical Tests proposed by NIST","volume":"1","author":"Zaman","year":"2012","journal-title":"J. Theor. Phys. Cryptogr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/TIT.1969.1054318","article-title":"Review of \u201cAlgebraic Coding Theory\u201d (Berlekamp, E. R.; 1968)","volume":"15","author":"Chien","year":"1969","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1109\/TIT.1969.1054260","article-title":"Shift-register synthesis and BCH decoding","volume":"15","author":"Massey","year":"1969","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_11","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I.J., and Fergus, R. (2014, January 14\u201316). Intriguing properties of neural networks. Proceedings of the 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada."},{"key":"ref_12","unstructured":"Goodfellow, I.J., Shlens, J., and Szegedy, C. (2015, January 7\u20139). Explaining and Harnessing Adversarial Examples. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA. Conference Track Proceedings."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Carlini, N., and Wagner, D.A. (2017, January 22\u201326). Towards Evaluating the Robustness of Neural Networks. Proceedings of the 2017 IEEE Symposium on Security and Privacy, SP 2017, San Jose, CA, USA.","DOI":"10.1109\/SP.2017.49"},{"key":"ref_14","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (May, January 30). Towards Deep Learning Models Resistant to Adversarial Attacks. Proceedings of the 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada. Conference Track Proceedings."},{"key":"ref_15","unstructured":"Athalye, A., Carlini, N., and Wagner, D.A. (2018, January 10\u201315). Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples. Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden."},{"key":"ref_16","unstructured":"Tram\u00e8r, F., Kurakin, A., Papernot, N., Goodfellow, I.J., Boneh, D., and McDaniel, P.D. (May, January 30). Ensemble Adversarial Training: Attacks and Defenses. Proceedings of the 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada."},{"key":"ref_17","unstructured":"Papernot, N., McDaniel, P.D., and Goodfellow, I.J. (2016). Transferability in Machine Learning: From Phenomena to Black-Box Attacks using Adversarial Samples. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1109\/TEVC.2019.2890858","article-title":"One Pixel Attack for Fooling Deep Neural Networks","volume":"23","author":"Su","year":"2019","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_19","unstructured":"Kurakin, A., Goodfellow, I.J., and Bengio, S. (2017, January 24\u201326). Adversarial examples in the physical world. Proceedings of the 5th International Conference on Learning Representations, ICLR 2017, Toulon, France. Workshop Track Proceedings."},{"key":"ref_20","unstructured":"Ilyas, A., Engstrom, L., Athalye, A., and Lin, J. (2018, January 10\u201315). Black-box Adversarial Attacks with Limited Queries and Information. Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden."},{"key":"ref_21","unstructured":"Kim, Y., Kim, J., Vigna, G., and Shi, E. (2021, January 15\u201319). Structural Attack against Graph Based Android Malware Detection. Proceedings of the CCS \u201921: 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event."},{"key":"ref_22","unstructured":"Calandrino, J.A., and Troncoso, C. (2023, January 9\u201311). Black-box Adversarial Example Attack towards FCG Based Android Malware Detection under Incomplete Feature Information. Proceedings of the 32nd USENIX Security Symposium, USENIX Security 2023, Anaheim, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, H., Yao, Z., Wu, B., Gao, C., Xu, T., Yuan, W., and Luo, X. (2025, January 24\u201328). Automated Mass Malware Factory: The Convergence of Piggybacking and Adversarial Example in Android Malicious Software Generation. Proceedings of the 32nd Annual Network and Distributed System Security Symposium, NDSS 2025, San Diego, CA, USA.","DOI":"10.14722\/ndss.2025.241933"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1109\/TIFS.2023.3333567","article-title":"MalPatch: Evading DNN-Based Malware Detection With Adversarial Patches","volume":"19","author":"Zhan","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_25","unstructured":"Kitsos, P., Galanis, M.D., and Koufopavlou, O. (2004, January 23\u201326). High-Speed Hardware Implementations of the KASUMI Block Cipher. Proceedings of the 2004 IEEE International Symposium on Circuits and Systems, Vancouver, BC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hell, M., Johansson, T., and Maximov, A. (2006, January 9\u201314). A Stream Cipher Proposal: Grain-128. Proceedings of the 2006 IEEE International Symposium on Information Theory, Seattle, WA, USA.","DOI":"10.1109\/ISIT.2006.261549"},{"key":"ref_27","unstructured":"Poschmann, A. (2009). Lightweight Cryptography, Ruhr University Bochum."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Aoki, K., Ichikawa, T., Kanda, M., Matsui, M., Moriai, S., Nakajima, J., and Tokita, T. (2000). Camellia: A 128-Bit Block Cipher Suitable for Multiple Platforms\u2014Design and Analysis. Selected Areas in Cryptography 2000; Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/3-540-44983-3_4"},{"key":"ref_29","unstructured":"Ramzan, Z. (1998). On Using Neural Networks to Break Cryptosystems, Unpublished work."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dileep, A.D., and Sekhar, C.C. (2006, January 16\u201321). Identification of block ciphers using support vector machines. Proceedings of the The 2006 IEEE International Joint Conference on Neural Network Proceedings, Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2006.247172"},{"key":"ref_31","unstructured":"Nagireddy, S. (2008). A Pattern Recognition Approach to Block Cipher Identification. [Master\u2019s Thesis, Indian Institute of Technology Madras]."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Manjula, R., and Anitha, R. (2011). Identification of Encryption Algorithm Using Decision Tree. Communications in Computer and Information Science, Springer.","DOI":"10.1007\/978-3-642-17881-8_23"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chou, J.W., Lin, S.D., and Cheng, C.M. (2012, January 19). On the Effectiveness of Using State-of-the-Art Machine Learning Techniques to Launch Cryptographic Distinguishing Attacks. Proceedings of the 5th ACM Workshop on Security and Artificial Intelligence, Raleigh, NC, USA.","DOI":"10.1145\/2381896.2381912"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mishra, S., and Bhattacharjya, A. (2013, January 25\u201327). Pattern Analysis of Cipher Text: A Combined Approach. Proceedings of the 2013 International Conference on Recent Trends in Information Technology, Chennai, India.","DOI":"10.1109\/ICRTIT.2013.6844236"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sharif, S.O., Kuncheva, L.I., and Mansoor, S.P. (2010, January 17\u201319). Classifying encryption algorithms using pattern recognition techniques. Proceedings of the 2010 IEEE International Conference on Information Theory and Information Security (ICITIS 2010), Beijing, China.","DOI":"10.1109\/ICITIS.2010.5689769"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"De Souza, W.A.R., and Tomlinson, A. (2013, January 7\u201310). A Distinguishing Attack with a Neural Network. Proceedings of the 2013 IEEE 13th International Conference on Data Mining Workshops, Dallas, TX, USA.","DOI":"10.1109\/ICDMW.2013.116"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pamidiparthi, S., and Velampalli, S. (2020). Cryptographic Algorithm Identification Using Deep Learning Techniques. Advances in Intelligent Systems and Computing, Springer.","DOI":"10.1007\/978-981-15-5788-0_74"},{"key":"ref_38","unstructured":"Cao, L.R. (2021). Research on Cryptographic Algorithm Recognition Based on Deep Learning. [Ph.D. Thesis, University of Electronic Science and Technology of China]."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cui, X., Zhang, H., Fang, X., Wang, Y., Wang, D., Fan, F., and Shu, L. (2023). A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning. Appl. Sci., 13.","DOI":"10.3390\/app132112025"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yuan, K., Zhang, B., Zhou, Y., Sun, H., Yang, W., and Jia, C. (2024). A Block Cipher Recognition Scheme Based on Deep Learning Transformer Algorithm. IEEE\/ResearchGate.","DOI":"10.21203\/rs.3.rs-3920602\/v1"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hu, H., and Yuan, K. (2025, January 10\u201312). Identification of Cryptographic Algorithms Based on CNN. Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control Engineering, CAICE \u201925, Heifei, China.","DOI":"10.1145\/3727648.3727680"},{"key":"ref_42","first-page":"103984","article-title":"A generic cryptographic algorithm identification scheme based on ciphertext features","volume":"89","author":"Li","year":"2025","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3823","DOI":"10.1007\/s00500-025-10595-y","article-title":"Identification of block cipher algorithms using multi-layer perception algorithm","volume":"29","author":"Yuan","year":"2025","journal-title":"Soft Comput."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/10\/1\/13\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:04:51Z","timestamp":1767189891000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/10\/1\/13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,30]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["bdcc10010013"],"URL":"https:\/\/doi.org\/10.3390\/bdcc10010013","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,30]]}}}