CoChat
Sign Up
Cited by 97Adversarial Robustness in Machine LearningInformation and Cyber SecurityNetwork Security and Intrusion Detection

The Threat of Offensive AI to Organizations

Open access
Mirsky, Yisroel, Demontis, Ambra, Kotak, Jaidip, Shankar, Ram, Gelei, Deng, Yang, Liu, Zhang, Xiangyu, Pintor, Maura +3 more·2023·Computers & Security
DOI 10.1016/j.cose.2022.103006
Open this paper in CoChat — free accountOpen PDF

Abstract

AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. However, positive tools can also be used for negative purposes. In particular, cyber adversaries can use AI to enhance their attacks and expand their campaigns. Although offensive AI has been discussed in the past, there is a need to analyze and understand the threat in the context of organizations. For example, how does an AI-capable adversary impact the cyber kill chain? Does AI benefit the attacker more than the defender? What are the most significant AI threats facing organizations today and what will be their impact on the future? In this study, we explore the threat of offensive AI on organizations. First, we present the background and discuss how AI changes the adversary’s methods, strategies, goals, and overall attack model. Then, through a literature review, we identify 32 offensive AI capabilities which adversaries can use to enhance their attacks. Finally, through a panel survey spanning industry, government and academia, we rank the AI threats and provide insights on the adversaries.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.cose.2022.103006

Cites

12 of 12 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Multiple studies deem other uses of speech deepfakes as more concerning, such as misleading people through voice impersonations.

    “Multiple studies deem other uses of speech deepfakes as more concerning, such as misleading people through voice impersonations [5, 6].”

    From Warning: Humans cannot reliably detect speech deepfakes (2023) · cites this paper as [6]

    The ‘Impersonation (Identity Theft)’ section describes deepfakes that reenact a victim’s voice for scams and phishing. ‘From the Literature Survey’ ranks AI-enabled impersonation among the highest threats, but compares it with other AI threats, not specifically with other uses of speech deepfakes. The paper therefore supports the concern about misleading voice impersonation, but not the claim’s narrower comparison. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Adversaries will have more opportunities to scale their operations, particularly for disinformation such as impersonations and spear phishing.

    “Adversaries will have more opportunities to scale their operations, particularly for disinformation such as impersonations and spear phishing [6].”

    From Warning: Humans cannot reliably detect speech deepfakes (2023) · cites this paper as [6]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 210

  1. T. A. Mattei, "Privacy, confidentiality, and security of health care information: Lessons from the recent wannacry cyberattack," World neurosurgery, vol. 104, p. 972-974, August 2017.
  2. N. Tariq, "Impact of cyberattacks on financial institutions," The Journal of Internet Banking and Commerce, vol. 23, pp. 1-11, 2018.
  3. Y. Mirsky, T. Doitshman, Y. Elovici, and A. Shabtai, "Kitsune: an ensemble of autoencoders for online network intrusion detection," arXiv preprint arXiv:1802.09089, 2018. · arXiv
  4. H. Liu and B. Lang, "Machine learning and deep learning methods for intrusion detection systems: A survey," applied sciences, vol. 9, no. 20, p. 4396, 2019.
  5. N. A. Mahadi, M. A. Mohamed, A. I. Mohamad, M. Makhtar, M. F. A. Kadir, and M. Mamat, "A survey of machine learning techniques for behavioral-based biometric user authentication," Recent Advances in Cryptography and Network Security, pp. 43-54, 2018.
  6. "Deepreflect: Discovering malicious functionality through binary reconstruction," in 30th USENIX Security Symposium (USENIX Security 21), USENIX Association, Aug. 2021.
  7. D. Ucci, L. Aniello, and R. Baldoni, "Survey of machine learning techniques for malware analysis," Computers & Security, vol. 81, pp. 123-147, 2019.
  8. D. Cohen, Y. Mirsky, M. Kamp, T. Martin, Y. Elovici, R. Puzis, and A. Shabtai, "Dante: A framework for mining and monitoring darknet traffic," in European Symposium on Research in Computer Security, pp. 88-109, Springer, 2020.
  9. M. Barreno, B. Nelson, A. Joseph, and J. Tygar, "The security of machine learning," Machine Learning, vol. 81, pp. 121-148, 2010.
  10. L. Huang, A. D. Joseph, B. Nelson, B. Rubinstein, and J. D. Tygar, "Ad- versarial machine learning," in 4th ACM Workshop on Artificial Intelli- gence and Security (AISec 2011), (Chicago, IL, USA), pp. 43-57, 2011.
  11. A. D. Joseph, B. Nelson, B. I. P. Rubinstein, and J. Tygar, Adversarial Machine Learning. Cambridge University Press, 2018.
  12. B. Biggio and F. Roli, "Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning," Pattern Recognition, vol. 84, pp. 317-331, 2018.
  13. A. Chakraborty, M. Alam, V. Dey, A. Chattopadhyay, and D. Mukhopadhyay, "Adversarial Attacks and Defences: A Survey," arXiv:1810.00069 [cs, stat], vol. ACM Computing Survey, Sept. 2018. arXiv: 1810.00069. · arXiv
  14. N. Papernot, P. McDaniel, A. Sinha, and M. P. Wellman, "Sok: Security and privacy in machine learning," in 2018 IEEE European Symposium on Security and Privacy (EuroS P), pp. 399-414, 2018.
  15. I. Zelinka, S. Das, L. Sikora, and R. Šenkeřík, "Swarm virus-next- generation virus and antivirus paradigm?," Swarm and Evolutionary Computation, vol. 43, pp. 207-224, 2018.
  16. T. C. Truong, I. Zelinka, and R. Senkerik, "Neural swarm virus," in Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing, pp. 122-134, Springer, 2019.
  17. M. Caldwell, J. Andrews, T. Tanay, and L. Griffin, "Ai-enabled future crime," Crime Science, vol. 9, no. 1, pp. 1-13, 2020.
  18. G. Lin, S. Wen, Q.-L. Han, J. Zhang, and Y. Xiang, "Software vulnerability detection using deep neural networks: A survey," Proceedings of the IEEE, vol. 108, no. 10, pp. 1825-1848, 2020.
  19. S. Calzavara, G. Tolomei, A. Casini, M. Bugliesi, and S. Orlando, "A supervised learning approach to protect client authentication on the web," ACM Trans. Web, vol. 9, June 2015.
  20. R. R. Yager, "Approximate reasoning as a basis for rule-based expert systems," IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-14, no. 4, pp. 636-643, 1984.
  21. W. Zeng and R. L. Church, "Finding shortest paths on real road networks: The case for a*," Int. J. Geogr. Inf. Sci., vol. 23, p. 531-543, Apr. 2009.
  22. I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets," in Proceedings of the 27th International Conference on Neural Information Processing Systems -Volume 2, NIPS'14, (Cambridge, MA, USA), p. 2672-2680, MIT Press, 2014.
  23. Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni, "Generalizing from a few examples: A survey on few-shot learning," ACM Comput. Surv., vol. 53, June 2020.
  24. M. Hussain, A. Al-Haiqi, A. Zaidan, B. Zaidan, M. M. Kiah, N. B. Anuar, and M. Abdulnabi, "The rise of keyloggers on smartphones: A survey and insight into motion-based tap inference attacks," Pervasive and Mobile Computing, vol. 25, pp. 1-25, 2016.
  25. A. R. Javed, M. O. Beg, M. Asim, T. Baker, and A. H. Al- Bayatti, "Alphalogger: Detecting motion-based side-channel attack using smartphone keystrokes," Journal of Ambient Intelligence and Humanized Computing, pp. 1-14, 2020.
  26. P. Marquardt, A. Verma, H. Carter, and P. Traynor, "(sp) iphone: Decoding vibrations from nearby keyboards using mobile phone accelerometers," in Proceedings of the 18th ACM conference on Computer and communications security, pp. 551-562, 2011.
  27. Y. Abid, A. Imine, and M. Rusinowitch, "Sensitive attribute prediction for social networks users," in EDBT/ICDT Workshops, 2018.
  28. J. Jiang, X. Yu, Y. Sun, and H. Zeng, "A survey of the software vulnerability discovery using machine learning techniques," in International Conference on Artificial Intelligence and Security, pp. 308-317, Springer, 2019.
  29. S. A. Mokhov, J. Paquet, and M. Debbabi, "The use of nlp techniques in static code analysis to detect weaknesses and vulnerabilities," in Advances in Artificial Intelligence (M. Sokolova and P. van Beek, eds.), (Cham), pp. 326-332, Springer International Publishing, 2014.
  30. Y. Mirsky, T. Mahler, I. Shelef, and Y. Elovici, "Ct-gan: Malicious tampering of 3d medical imagery using deep learning," in 28th USENIX Security Symposium (USENIX Security 19), (Santa Clara, CA), pp. 461-478, USENIX Association, Aug. 2019.
  31. B. Hitaj, P. Gasti, G. Ateniese, and F. Perez-Cruz, "Passgan: A deep learning approach for password guessing," in International Conference on Applied Cryptography and Network Security, pp. 217-237, Springer, 2019.
  32. V. Garg and L. Ahuja, "Password guessing using deep learning," in 2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC), pp. 38-40, IEEE, 2019.
  33. C. Novo and R. Morla, "Flow-based Detection and Proxy-based Evasion of Encrypted Malware C2 Traffic," in Proceedings of the 13th ACM Workshop on Artificial Intelligence and Security, AISec'20, (New York, NY, USA), pp. 83-91, Association for Computing Machinery, Nov. 2020.
  34. D. Han, Z. Wang, Y. Zhong, W. Chen, J. Yang, S. Lu, X. Shi, and X. Yin, "Practical traffic-space adversarial attacks on learning-based nidss," arXiv preprint arXiv:2005.07519, 2020. · arXiv
  35. M. T. Ribeiro, S. Singh, and C. Guestrin, ""Why Should I Trust You?": Explaining the Predictions of Any Classifier," in 22nd ACM SIGKDD Int'l Conf. Knowl. Disc. Data Mining, KDD '16, (New York, NY, USA), pp. 1135-1144, ACM, 2016.
  36. T. Rahman, M. Rochan, and Y. Wang, "Video-based person re- identification using refined attention networks," in 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1-8, 2019.
  37. X. Zhu, X. Jing, X. You, X. Zhang, and T. Zhang, "Video-based person re-identification by simultaneously learning intra-video and inter-video distance metrics," IEEE Transactions on Image Processing, vol. 27, no. 11, pp. 5683-5695, 2018.
  38. Y. Zhang, J. E. Meng, and M. Pratama, "Extractive document summarization based on convolutional neural networks," in IECON 2016 -42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 918-922, 2016.
  39. A. Castiglione, R. D. Prisco, A. D. Santis, U. Fiore, and F. Palmieri, "A botnet-based command and control approach relying on swarm intelligence," Journal of Network and Computer Applications, vol. 38, pp. 22-33, 2014.
  40. J. A. Bland, M. D. Petty, T. S. Whitaker, K. P. Maxwell, and W. A. Cantrell, "Machine learning cyberattack and defense strategies," Computers & Security, vol. 92, p. 101738, 2020.
  41. B. Biggio, B. Nelson, and P. Laskov, "Poisoning attacks against support vector machines," in 29th Int'l Conf. on Machine Learning (J. Langford and J. Pineau, eds.), pp. 1807-1814, Omnipress, 2012.
  42. L. Muñoz-González, B. Biggio, A. Demontis, A. Paudice, V. Wongrassamee, E. C. Lupu, and F. Roli, "Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization," in 10th ACM Workshop on Artificial Intelligence and Security (B. M. Thuraisingham, B. Biggio, D. M. Freeman, B. Miller, and A. Sinha, eds.), AISec '17, (New York, NY, USA), pp. 27-38, ACM, 2017.
  43. P. W. Koh and P. Liang, "Understanding Black-box Predictions via Influence Functions," in International Conference on Machine Learning (ICML), 2017.
  44. T. Gu, B. Dolan-Gavitt, and S. Garg, "BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain," in NIPS Workshop on Machine Learning and Computer Security, vol. abs/1708.06733, 2017. · arXiv
  45. X. Chen, C. Liu, B. Li, K. Lu, and D. Song, "Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning," ArXiv e-prints, vol. abs/1712.05526, 2017. · arXiv
  46. A. Shafahi, W. R. Huang, M. Najibi, O. Suciu, C. Studer, T. Dumitras, and T. Goldstein, "Poison frogs! targeted clean-label poisoning attacks on neural networks," in Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS'18, (Red Hook, NY, USA), p. 6106-6116, Curran Associates Inc., 2018.
  47. H. Aghakhani, D. Meng, Y.-X. Wang, C. Kruegel, and G. Vigna, "Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability," arXiv preprint arXiv:2005.00191, 2020. · arXiv
  48. B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli, "Evasion attacks against machine learning at test time," in Machine Learning and Knowledge Discovery in Databases (ECML PKDD), Part III (H. Blockeel, K. Kersting, S. Nijssen, and F. Železný, eds.), vol. 8190 of LNCS, pp. 387-402, Springer Berlin Heidelberg, 2013.
  49. C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Good- fellow, and R. Fergus, "Intriguing properties of neural networks," in International Conference on Learning Representations, 2014.
  50. M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter, "Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition," in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 1528-1540, ACM, 2016.
  51. I. Shumailov, Y. Zhao, D. Bates, N. Papernot, R. Mullins, and R. Anderson, "Sponge examples: Energy-latency attacks on neural networks," arXiv preprint arXiv:2006.03463, 2020. · arXiv
  52. A. Narayanan and V. Shmatikov, "Robust de-anonymization of large sparse datasets," in 2008 IEEE Symposium on Security and Privacy (sp 2008), pp. 111-125, 2008.
  53. S. Hidano, T. Murakami, S. Katsumata, S. Kiyomoto, and G. Hanaoka, "Model inversion attacks for prediction systems: Without knowledge of non-sensitive attributes," in 2017 15th Annual Conference on Privacy, Security and Trust (PST), pp. 115-11509, 2017.
  54. H. Zhang, H. Chen, Z. Song, D. S. Boning, I. S. Dhillon, and C. Hsieh, "The limitations of adversarial training and the blind-spot attack," in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, OpenReview.net, 2019.
  55. S. Woh and J. Lee, "Game state prediction with ensemble of machine learning techniques," in 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS), pp. 89-92, 2018.
  56. Y. Yao, H. Li, H. Zheng, and B. Y. Zhao, "Latent backdoor attacks on deep neural networks," in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, CCS '19, (New York, NY, USA), p. 2041-2055, Association for Computing Machinery, 2019.
  57. S. Wang, S. Nepal, C. Rudolph, M. Grobler, S. Chen, and T. Chen, "Backdoor attacks against transfer learning with pre-trained deep learn- ing models," IEEE Transactions on Services Computing, pp. 1-1, 2020.
  58. J. Breier, X. Hou, D. Jap, L. Ma, S. Bhasin, and Y. Liu, "Practical fault attack on deep neural networks," in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 2204-2206, 2018.
  59. A. Demontis, M. Melis, M. Pintor, M. Jagielski, B. Biggio, A. Oprea, C. Nita-Rotaru, and F. Roli, "Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks," in 28th USENIX Security Symposium (USENIX Security 19), USENIX Association, 2019.
  60. M. Melis, A. Demontis, M. Pintor, A. Sotgiu, and B. Biggio, "secml: A python library for secure and explainable machine learning," arXiv preprint arXiv:1912.10013, 2019. · arXiv
  61. M.-I. Nicolae, M. Sinn, M. N. Tran, B. Buesser, A. Rawat, M. Wistuba, V. Zantedeschi, N. Baracaldo, B. Chen, H. Ludwig, I. Molloy, and B. Edwards, "Adversarial robustness toolbox v1.2.0," CoRR, vol. 1807.01069, 2018.
  62. N. Papernot, F. Faghri, N. Carlini, I. Goodfellow, R. Feinman, A. Ku- rakin, C. Xie, Y. Sharma, T. Brown, A. Roy, A. Matyasko, V. Behzadan, K. Hambardzumyan, Z. Zhang, Y.-L. Juang, Z. Li, R. Sheatsley, A. Garg, J. Uesato, W. Gierke, Y. Dong, D. Berthelot, P. Hendricks, J. Rauber, and R. Long, "Technical report on the cleverhans v2.1.0 adversarial examples library," arXiv preprint arXiv:1610.00768, 2018. · arXiv
  63. F. Croce and M. Hein, "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks," in ICML, 2020.
  64. B. I. Messaoud, K. Guennoun, M. Wahbi, and M. Sadik, "Advanced persistent threat: new analysis driven by life cycle phases and their chal- lenges," in 2016 International Conference on Advanced Communica- tion Systems and Information Security (ACOSIS), pp. 1-6, IEEE, 2016.
  65. A. Alshamrani, S. Myneni, A. Chowdhary, and D. Huang, "A survey on advanced persistent threats: Techniques, solutions, challenges, and research opportunities," IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1851-1877, 2019.
  66. M. H. Abd El-Jawad, R. Hodhod, and Y. M. K. Omar, "Sentiment analysis of social media networks using machine learning," in 2018 14th International Computer Engineering Conference (ICENCO), pp. 174-176, 2018.
  67. J. Liu, Y. Wang, G. Kar, Y. Chen, J. Yang, and M. Gruteser, "Snooping keystrokes with mm-level audio ranging on a single phone," in Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pp. 142-154, 2015.
  68. I. Shumailov, L. Simon, J. Yan, and R. Anderson, "Hearing your touch: A new acoustic side channel on smartphones," arXiv preprint arXiv:1903.11137, 2019. · arXiv
  69. J. V. Monaco, "What are you searching for? a remote keylogging attack on search engine autocomplete," in 28th USENIX Security Symposium (USENIX Security 19), (Santa Clara, CA), pp. 959-976, USENIX Association, Aug. 2019.
  70. A. Malhotra, L. Totti, W. Meira Jr., P. Kumaraguru, and V. Almeida, "Studying user footprints in different online social networks," in Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), ASONAM '12, (USA), p. 1065-1070, IEEE Computer Society, 2012.
  71. J. Li, L. Zhou, H. Li, L. Yan, and H. Zhu, "Dynamic Traffic Feature Camouflaging via Generative Adversarial Networks," in 2019 IEEE Conference on Communications and Network Security (CNS), pp. 268-276, June 2019.
  72. G. Beni, "Swarm intelligence," Complex Social and Behavioral Systems: Game Theory and Agent-Based Models, pp. 791-818, 2020.
  73. H. Otsuka, Y. Watanabe, and Y. Matsumoto, "Learning from before and after recovery to detect latent misconfiguration," in 2015 IEEE 39th Annual Computer Software and Applications Conference, vol. 3, pp. 141-148, IEEE, 2015.
  74. W. Chen, X. Qiao, J. Wei, H. Zhong, and X. Huang, "Detecting inter-component configuration errors in proactive: a relation-aware method," in 2014 14th International Conference on Quality Software, pp. 184-189, IEEE, 2014.
  75. M. Yousefi, N. Mtetwa, Y. Zhang, and H. Tianfield, "A reinforcement learning approach for attack graph analysis," in 2018 17th IEEE Inter- national Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), pp. 212-217, 2018.
  76. R. Wu, J. Gong, W. Tong, and B. Fan, "Network attack path selection and evaluation based on q-learning," Applied Sciences, vol. 11, no. 1, 2021.
  77. X. Ou, S. Govindavajhala, and A. W. Appel, "MulVAL: A Logic-based Network Security Analyzer," in USENIX Security Symposium, 2005.
  78. M. Matta, G. C. Cardarilli, L. Di Nunzio, R. Fazzolari, D. Giardino, M. re, F. Silvestri, and S. Spanò, "Q-rts: a real-time swarm intelligence based on multi-agent q-learning," Electronics Letters, 03 2019.
  79. S. Singh and H. K. Thakur, "Survey of various ai chatbots based on technology used," in 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), pp. 1074-1079, IEEE, 2020.
  80. Y. Rebryk and S. Beliaev, "Convoice: Real-time zero-shot voice style transfer with convolutional network," arXiv preprint arXiv:2005.07815, 2020. · arXiv
  81. Y. Jia, Y. Zhang, R. Weiss, Q. Wang, J. Shen, F. Ren, z. Chen, P. Nguyen, R. Pang, I. Lopez Moreno, and Y. Wu, "Transfer learning from speaker verification to multispeaker text-to-speech synthesis," in Advances in Neural Information Processing Systems 31 (S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, eds.), pp. 4480-4490, Curran Associates, Inc., 2018.
  82. N. O. Leslie, R. E. Harang, L. P. Knachel, and A. Kott, "Statistical models for the number of successful cyber intrusions," CoRR, vol. abs/1901.04531, 2019. · arXiv
  83. M. Manning, G. T. Wong, T. Graham, T. Ranbaduge, P. Christen, K. Taylor, R. Wortley, T. Makkai, and P. Skorich, "Towards a 'smart'cost-benefit tool: using machine learning to predict the costs of criminal justice policy interventions," Crime Science, vol. 7, no. 1, p. 12, 2018.
  84. R. Bitton, T. Gluck, O. Stan, M. Inokuchi, Y. Ohta, Y. Yamada, T. Yagyu, Y. Elovici, and A. Shabtai, "Deriving a cost-effective digital twin of an ics to facilitate security evaluation," in European Symposium on Research in Computer Security, pp. 533-554, Springer, 2018.
  85. G. Pasandi, S. Nazarian, and M. Pedram, "Approximate logic synthesis: A reinforcement learning-based technology mapping approach," in 20th International Symposium on Quality Electronic Design (ISQED), pp. 26-32, IEEE, 2019.
  86. Z. Fang, J. Wang, B. Li, S. Wu, Y. Zhou, and H. Huang, "Evading anti-malware engines with deep reinforcement learning," IEEE Access, vol. 7, pp. 48867-48879, 2019.
  87. T. Perianin, S. Carré, V. Dyseryn, A. Facon, and S. Guilley, "End- to-end automated cache-timing attack driven by machine learning," Journal of Cryptographic Engineering, pp. 1-12, 2020.
  88. Y. Wang, T. Bao, C. Ding, and M. Zhu, "Face recognition in real-world surveillance videos with deep learning method," in Image, Vision and Computing (ICIVC), 2017 2nd International Conference on, pp. 239-243, IEEE, 2017.
  89. C. Ding, K. Huang, V. M. Patel, and B. C. Lovell, "Special issue on video surveillance-oriented biometrics," Pattern Recognition Letters, vol. 107, pp. 1-2, 2018.
  90. P. Bontrager, A. Roy, J. Togelius, N. Memon, and A. Ross, "Deep- masterprints: Generating masterprints for dictionary attacks via latent variable evolution," in 2018 IEEE 9th International Conference on Bio- metrics Theory, Applications and Systems (BTAS), pp. 1-9, IEEE, 2018.
  91. X. Wang, J. Yamagishi, M. Todisco, H. Delgado, A. Nautsch, N. Evans, M. Sahidullah, V. Vestman, T. Kinnunen, K. A. Lee, et al., "The asvspoof 2019 database," arXiv preprint arXiv:1911.01601, 2019. · arXiv
  92. D. Wang, A. Neupane, Z. Qian, N. B. Abu-Ghazaleh, S. V. Krishnamurthy, E. J. Colbert, and P. Yu, "Unveiling your keystrokes: A cache-based side-channel attack on graphics libraries.," in NDSS, 2019.
  93. X. Liu, Z. Zhou, W. Diao, Z. Li, and K. Zhang, "When good becomes evil: Keystroke inference with smartwatch," in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1273-1285, 2015.
  94. A. Maiti, M. Jadliwala, J. He, and I. Bilogrevic, "Side-channel inference attacks on mobile keypads using smartwatches," IEEE Trans- actions on Mobile Computing, vol. 17, no. 9, pp. 2180-2194, 2018.
  95. A. Compagno, M. Conti, D. Lain, and G. Tsudik, "Don't skype & type! acoustic eavesdropping in voice-over-ip," in Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, pp. 703-715, 2017.
  96. J. Yu, L. Lu, Y. Chen, Y. Zhu, and L. Kong, "An indirect eavesdropping attack of keystrokes on touch screen through acoustic sensing," IEEE Transactions on Mobile Computing, 2019.
  97. L. Lu, J. Yu, Y. Chen, Y. Zhu, X. Xu, G. Xue, and M. Li, "Keylisterber: Inferring keystrokes on qwerty keyboard of touch screen through acoustic signals," in IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 775-783, IEEE, 2019.
  98. Y. Chen, T. Li, R. Zhang, Y. Zhang, and T. Hedgpeth, "Eyetell: Video-assisted touchscreen keystroke inference from eye movements," in 2018 IEEE Symposium on Security and Privacy (SP), pp. 144-160, IEEE, 2018.
  99. Y. Wang, W. Cai, T. Gu, W. Shao, I. Khalil, and X. Xu, "Gazerevealer: Inferring password using smartphone front camera," in Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, pp. 254-263, 2018.
  100. Y. Wang, W. Cai, T. Gu, and W. Shao, "Your eyes reveal your secrets: an eye movement based password inference on smartphone," IEEE transactions on mobile computing, 2019.
  101. J. Sun, X. Jin, Y. Chen, J. Zhang, Y. Zhang, and R. Zhang, "Visible: Video-assisted keystroke inference from tablet backside motion.," in NDSS, 2016.
  102. K. S. Balagani, M. Conti, P. Gasti, M. Georgiev, T. Gurtler, D. Lain, C. Miller, K. Molas, N. Samarin, E. Saraci, et al., "Silk-tv: Secret infor- mation leakage from keystroke timing videos," in European Symposium on Research in Computer Security, pp. 263-280, Springer, 2018.
  103. J. Lim, T. Price, F. Monrose, and J.-M. Frahm, "Revisiting the threat space for vision-based keystroke inference attacks," arXiv preprint arXiv:2009.05796, 2020. · arXiv
  104. S. Nam, S. Jeon, H. Kim, and J. Moon, "Recurrent gans password cracker for iot password security enhancement," Sensors, vol. 20, no. 11, p. 3106, 2020.
  105. J. Seymour and P. Tully, "Generative models for spear phishing posts on social media," arXiv preprint arXiv:1802.05196, 2018. · arXiv
  106. P. Kocher, J. Jaffe, and B. Jun, "Differential power analysis," in Annual international cryptology conference, pp. 388-397, Springer, 1999.
  107. L. Lerman, G. Bontempi, and O. Markowitch, "Power analysis attack: an approach based on machine learning," International Journal of Applied Cryptography, vol. 3, no. 2, pp. 97-115, 2014.
  108. K. Gandolfi, C. Mourtel, and F. Olivier, "Electromagnetic analysis: Concrete results," in International workshop on cryptographic hardware and embedded systems, pp. 251-261, Springer, 2001.
  109. D. Brumley and D. Boneh, "Remote timing attacks are practical," Computer Networks, vol. 48, no. 5, pp. 701-716, 2005.
  110. L. Lerman, G. Bontempi, S. B. Taieb, and O. Markowitch, "A time series approach for profiling attack," in International Conference on Security, Privacy, and Applied Cryptography Engineering, pp. 75-94, Springer, 2013.
  111. L. Weissbart, S. Picek, and L. Batina, "One trace is all it takes: Machine learning-based side-channel attack on eddsa," in Security, Privacy, and Applied Cryptography Engineering (S. Bhasin, A. Mendelson, and M. Nandi, eds.), (Cham), pp. 86-105, Springer International Publishing, 2019.
  112. S. Picek, A. Heuser, A. Jovic, S. Bhasin, and F. Regazzoni, "The curse of class imbalance and conflicting metrics with machine learning for side-channel evaluations," IACR Transactions on Cryptographic Hardware and Embedded Systems, vol. 2019, no. 1, pp. 1-29, 2019.
  113. E. Cagli, C. Dumas, and E. Prouff, "Convolutional neural networks with data augmentation against jitter-based countermeasures," in International Conference on Cryptographic Hardware and Embedded Systems, pp. 45-68, Springer, 2017.
  114. A. Heuser, S. Picek, S. Guilley, and N. Mentens, "Side-channel analysis of lightweight ciphers: Does lightweight equal easy?," in International Workshop on Radio Frequency Identification: Security and Privacy Issues, pp. 91-104, Springer, 2016.
  115. S. Picek, I. P. Samiotis, J. Kim, A. Heuser, S. Bhasin, and A. Legay, "On the performance of convolutional neural networks for side- channel analysis," in International Conference on Security, Privacy, and Applied Cryptography Engineering, pp. 157-176, Springer, 2018.
  116. H. Maghrebi, T. Portigliatti, and E. Prouff, "Breaking cryptographic implementations using deep learning techniques," in International Conference on Security, Privacy, and Applied Cryptography Engineering, pp. 3-26, Springer, 2016.
  117. G. Perin, Chmielewski, L. Batina, and S. Picek, "Keep it unsupervised: Horizontal attacks meet deep learning," IACR Transactions on Cryptographic Hardware and Embedded Systems, vol. 2021, pp. 343- 372, Dec. 2020.
  118. D. X. Song, D. A. Wagner, and X. Tian, "Timing analysis of keystrokes and timing attacks on ssh.," in USENIX Security Symposium, vol. 2001, 2001.
  119. E. C. R. Shin, D. Song, and R. Moazzezi, "Recognizing functions in binaries with neural networks," in 24th {USENIX} Security Symposium ({USENIX} Security 15), pp. 611-626, 2015.
  120. X. Xu, C. Liu, Q. Feng, H. Yin, L. Song, and D. Song, "Neural network-based graph embedding for cross-platform binary code similarity detection," Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Oct 2017.
  121. T. Bao, J. Burket, M. Woo, R. Turner, and D. Brumley, "{BYTEWEIGHT}: Learning to recognize functions in binary code," in 23rd {USENIX} Security Symposium ({USENIX} Security 14), pp. 845-860, 2014.
  122. B. Liu, W. Huo, C. Zhang, W. Li, F. Li, A. Piao, and W. Zou, "↵diff: cross-version binary code similarity detection with dnn," in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, pp. 667-678, 2018.
  123. S. H. Ding, B. C. Fung, and P. Charland, "Asm2vec: Boosting static representation robustness for binary clone search against code obfuscation and compiler optimization," in 2019 IEEE Symposium on Security and Privacy (SP), pp. 472-489, IEEE, 2019.
  124. Y. Duan, X. Li, J. Wang, and H. Yin, "Deepbindiff: Learning program- wide code representations for binary diffing," in Proceedings of the 27th Annual Network and Distributed System Security Symposium (NDSS'20), 2020.
  125. F. Ye, S. Zhou, A. Venkat, R. Marucs, N. Tatbul, J. J. Tithi, P. Petersen, T. Mattson, T. Kraska, P. Dubey, et al., "Misim: An end-to-end neural code similarity system," arXiv preprint arXiv:2006.05265, 2020. · arXiv
  126. S. Yun, M. Jeong, R. Kim, J. Kang, and H. J. Kim, "Graph transformer networks," arXiv preprint arXiv:1911.06455, 2019. · arXiv
  127. T. Huybrechts, Y. Vanommeslaeghe, D. Blontrock, G. Van Barel, and P. Hellinckx, "Automatic reverse engineering of can bus data using ma- chine learning techniques," in International Conference on P2P, Paral- lel, Grid, Cloud and Internet Computing, pp. 751-761, Springer, 2017.
  128. H. Li, B. Shuai, J. Wang, and C. Tang, "Protocol reverse engineering using lda and association analysis," in 2015 11th International Conference on Computational Intelligence and Security (CIS), pp. 312-316, IEEE, 2015.
  129. G. Bossert, F. Guihéry, and G. Hiet, "Towards automated protocol reverse engineering using semantic information," in Proceedings of the 9th ACM symposium on Information, computer and communications security, pp. 51-62, 2014.
  130. Y. Wang, Z. Zhang, D. D. Yao, B. Qu, and L. Guo, "Inferring protocol state machine from network traces: a probabilistic approach," in International Conference on Applied Cryptography and Network Security, pp. 1-18, Springer, 2011.
  131. Q. Feng, R. Zhou, C. Xu, Y. Cheng, B. Testa, and H. Yin, "Scalable graph-based bug search for firmware images," in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 480-491, 2016.
  132. Z. Li, D. Zou, S. Xu, X. Ou, H. Jin, S. Wang, Z. Deng, and Y. Zhong, "Vuldeepecker: A deep learning-based system for vulnerability detection," arXiv preprint arXiv:1801.01681, 2018. · arXiv
  133. Z. Li, D. Zou, J. Tang, Z. Zhang, M. Sun, and H. Jin, "A comparative study of deep learning-based vulnerability detection system," IEEE Access, vol. 7, pp. 103184-103197, 2019.
  134. S. Chakraborty, R. Krishna, Y. Ding, and B. Ray, "Deep learning based vulnerability detection: Are we there yet?," arXiv preprint arXiv:2009.07235, 2020. · arXiv
  135. D. She, K. Pei, D. Epstein, J. Yang, B. Ray, and S. Jana, "Neuzz: Efficient fuzzing with neural program smoothing," in 2019 IEEE Symposium on Security and Privacy (SP), pp. 803-817, IEEE, 2019.
  136. D. She, R. Krishna, L. Yan, S. Jana, and B. Ray, "Mtfuzz: Fuzzing with a multi-task neural network," arXiv preprint arXiv:2005.12392, 2020. · arXiv
  137. Y. Wang, P. Jia, L. Liu, C. Huang, and Z. Liu, "A systematic review of fuzzing based on machine learning techniques," PloS one, vol. 15, no. 8, p. e0237749, 2020.
  138. Y. Li, S. Ji, C. Lyu, Y. Chen, J. Chen, Q. Gu, C. Wu, and R. Beyah, "V-fuzz: Vulnerability prediction-assisted evolutionary fuzzing for binary programs," IEEE Transactions on Cybernetics, 2020.
  139. L. Cheng, Y. Zhang, Y. Zhang, C. Wu, Z. Li, Y. Fu, and H. Li, "Optimizing seed inputs in fuzzing with machine learning," in 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), pp. 244-245, IEEE, 2019.
  140. V. Atlidakis, R. Geambasu, P. Godefroid, M. Polishchuk, and B. Ray, "Pythia: Grammar-based fuzzing of rest apis with coverage- guided feedback and learning-based mutations," arXiv preprint arXiv:2005.11498, 2020. · arXiv
  141. M. Janota, "Towards generalization in qbf solving via machine learning.," in AAAI, pp. 6607-6614, 2018.
  142. H. Samulowitz and R. Memisevic, "Learning to solve qbf," in AAAI, vol. 7, pp. 255-260, 2007.
  143. J. H. Liang, C. Oh, M. Mathew, C. Thomas, C. Li, and V. Ganesh, "Machine learning-based restart policy for cdcl sat solvers," in International Conference on Theory and Applications of Satisfiability Testing, pp. 94-110, Springer, 2018.
  144. V. Kurin, S. Godil, S. Whiteson, and B. Catanzaro, "Improving sat solver heuristics with graph networks and reinforcement learning," arXiv preprint arXiv:1909.11830, 2019. · arXiv
  145. Y. Ghazi, Z. Anwar, R. Mumtaz, S. Saleem, and A. Tahir, "A supervised machine learning based approach for automatically extracting high-level threat intelligence from unstructured sources," in 2018 International Conference on Frontiers of Information Technology (FIT), pp. 129-134, IEEE, 2018.
  146. L. Akoglu, H. Tong, and D. Koutra, "Graph based anomaly detection and description: a survey," Data mining and knowledge discovery, vol. 29, no. 3, pp. 626-688, 2015.
  147. J. Schwartz and H. Kurniawati, "Autonomous penetration testing using reinforcement learning," arXiv preprint arXiv:1905.05965, 2019. · arXiv
  148. R. Dabre, C. Chu, and A. Kunchukuttan, "A survey of multilingual neural machine translation," ACM Computing Surveys (CSUR), vol. 53, no. 5, pp. 1-38, 2020.
  149. Z. Nasar, S. W. Jaffry, and M. K. Malik, "Textual keyword extraction and summarization: State-of-the-art," Information Processing & Management, vol. 56, no. 6, p. 102088, 2019.
  150. J. R. G. Evangelista, R. J. Sassi, M. Romero, and D. Napolitano, "Systematic literature review to investigate the application of open source intelligence (osint) with artificial intelligence," Journal of Applied Security Research, pp. 1-25, 2020.
  151. B. Wang and N. Z. Gong, "Stealing hyperparameters in machine learning," in 2018 IEEE Symposium on Security and Privacy (SP), pp. 36-52, IEEE, 2018.
  152. L. Batina, S. Bhasin, D. Jap, and S. Picek, "CSI NN: Reverse engineering of neural network architectures through electromagnetic side channel," in 28th USENIX Security Symposium (USENIX Security 19), (Santa Clara, CA), pp. 515-532, USENIX Association, Aug. 2019.
  153. J. Breier, D. Jap, X. Hou, S. Bhasin, and Y. Liu, "Sniff: Reverse engineering of neural networks with fault attacks," arXiv preprint arXiv:2002.11021, 2020. · arXiv
  154. B. Zhou, M. Elbadry, R. Gao, and F. Ye, "Batmapper: Acoustic sensing based indoor floor plan construction using smartphones," in Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, pp. 42-55, 2017.
  155. L. Jiao, F. Zhang, F. Liu, S. Yang, L. Li, Z. Feng, and R. Qu, "A survey of deep learning-based object detection," IEEE Access, vol. 7, pp. 128837-128868, 2019.
  156. Y. Ren, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, "Almost unsupervised text to speech and automatic speech recognition," arXiv preprint arXiv:1905.06791, 2019. · arXiv
  157. A. White, A. Matthews, K. Snow, and F. Monrose, "Phonotactic reconstruction of encrypted voip conversations: Hookt on fon-iks," pp. 3 -18, 06 2011.
  158. A. Al-Hababi and S. C. Tokgoz, "Man-in-the-middle attacks to detect and identify services in encrypted network flows using machine learn- ing," in 2020 3rd International Conference on Advanced Communica- tion Technologies and Networking (CommNet), pp. 1-5, IEEE, 2020.
  159. Y. Nirkin, Y. Keller, and T. Hassner, "Fsgan: Subject agnostic face swapping and reenactment," in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7184-7193, 2019.
  160. A. Siarohin, S. Lathuilière, S. Tulyakov, E. Ricci, and N. Sebe, "First order motion model for image animation," in Conference on Neural Information Processing Systems (NeurIPS), December 2019.
  161. M. Workman, "Wisecrackers: A theory-grounded investigation of phishing and pretext social engineering threats to information security," Journal of the American Society for Information Science and Technology, vol. 59, no. 4, pp. 662-674, 2008.
  162. J. Salminen, S.-g. Jung, and B. J. Jansen, "The future of data-driven personas: A marriage of online analytics numbers and human attributes.," in ICEIS (1), pp. 608-615, 2019.
  163. J. Salminen, R. G. Rao, S.-g. Jung, S. A. Chowdhury, and B. J. Jansen, "Enriching social media personas with personality traits: A deep learn- ing approach using the big five classes," in International Conference on Human-Computer Interaction, pp. 101-120, Springer, 2020.
  164. D. Spiliotopoulos, D. Margaris, and C. Vassilakis, "Data-assisted persona construction using social media data," Big Data and Cognitive Computing, vol. 4, no. 3, p. 21, 2020.
  165. S. Shan, E. Wenger, J. Zhang, H. Li, H. Zheng, and B. Y. Zhao, "Fawkes: Protecting privacy against unauthorized deep learning models," in 29th {USENIX} Security Symposium ({USENIX} Security 20), pp. 1589-1604, 2020.
  166. Q. Sun, A. Tewari, W. Xu, M. Fritz, C. Theobalt, and B. Schiele, "A hybrid model for identity obfuscation by face replacement," in Proceedings of the European Conference on Computer Vision (ECCV), pp. 553-569, 2018.
  167. Y. Li, X. Yang, B. Wu, and S. Lyu, "Hiding faces in plain sight: Disrupting ai face synthesis with adversarial perturbations," arXiv preprint arXiv:1906.09288, 2019. · arXiv
  168. Q. Wang, W. Zhao, J. Yang, J. Wu, W. Hu, and Q. Xing, "Deeptrust: A deep user model of homophily effect for trust prediction," in 2019 IEEE International Conference on Data Mining (ICDM), pp. 618-627, IEEE, 2019.
  169. R. Kong and X. Tong, "Dynamic weighted heuristic trust path search algorithm," IEEE Access, vol. 8, pp. 157382-157390, 2020.
  170. J. Seymour and P. Tully, "Weaponizing data science for social engineering: Automated e2e spear phishing on twitter," Black Hat USA, vol. 37, pp. 1-39, 2016.
  171. Z. Fu, X. Tan, N. Peng, D. Zhao, and R. Yan, "Style transfer in text: Exploration and evaluation," arXiv preprint arXiv:1711.06861, 2017. · arXiv
  172. Z. Yang, Z. Hu, C. Dyer, E. P. Xing, and T. Berg-Kirkpatrick, "Unsupervised text style transfer using language models as discriminators," in Advances in Neural Information Processing Systems, pp. 7287-7298, 2018.
  173. A. Panagiotou, B. Ghita, S. Shiaeles, and K. Bendiab, "Facewallgraph: Using machine learning for profiling user behaviour from facebook wall," in Internet of Things, Smart Spaces, and Next Generation Networks and Systems (O. Galinina, S. Andreev, S. Balandin, and Y. Koucheryavy, eds.), (Cham), pp. 125-134, Springer International Publishing, 2019.
  174. C. Dhaoui, C. M. Webster, and L. P. Tan, "Social media sentiment analysis: lexicon versus machine learning," Journal of Consumer Marketing, 2017.
  175. M. Ghiassi and S. Lee, "A domain transferable lexicon set for twitter sentiment analysis using a supervised machine learning approach," Expert Systems with Applications, vol. 106, pp. 197-216, 2018.
  176. M. Rathi, A. Malik, D. Varshney, R. Sharma, and S. Mendiratta, "Sentiment analysis of tweets using machine learning approach," in 2018 Eleventh International Conference on Contemporary Computing (IC3), pp. 1-3, 2018. [214"Black Hat USA 2018."
  177. H. Pellet, S. Shiaeles, and S. Stavrou, "Localising social network users and profiling their movement," Computers & Security, vol. 81, pp. 49-57, 2019.
  178. W. Zhang, R. Lau, S. Liao, and R.-W. Kwok, "A probabilistic generative model for latent business networks mining," International Conference on Information Systems, ICIS 2012, vol. 2, pp. 1102-1118, 01 2012.
  179. Z. Ma, O. Sheng, and G. Pant, "Discovering company revenue relations from news: A network approach," Decision Support Systems, vol. 47, pp. 408-414, 11 2009.
  180. A. Kumar and N. Rathore, "Improving attribute inference attack using link prediction in online social networks," in Recent Advances in Mathematics, Statistics and Computer Science, pp. 494-503, World Scientific, 2016.
  181. M. Zhang and Y. Chen, "Link prediction based on graph neural networks," in Advances in Neural Information Processing Systems, pp. 5165-5175, 2018.
  182. Q. Cao, Y. Qiao, and Z. Lyu, "Machine learning to detect anomalies in web log analysis," in 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 519-523, 2017.
  183. B. Debnath, M. Solaimani, M. A. G. Gulzar, N. Arora, C. Lumezanu, J. Xu, B. Zong, H. Zhang, G. Jiang, and L. Khan, "Loglens: A real- time log analysis system," in 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), pp. 1052-1062, 2018.
  184. R. Gilad-Bachrach, N. Dowlin, K. Laine, K. Lauter, M. Naehrig, and J. Wernsing, "Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy," in International Conference on Machine Learning, pp. 201-210, 2016.
  185. J. Breier, X. Hou, D. Jap, L. Ma, S. Bhasin, and Y. Liu, "Deeplaser: Practical fault attack on deep neural networks," arXiv preprint arXiv:1806.05859, 2018. · arXiv
  186. S. Li, S. Ma, M. Xue, and B. Z. H. Zhao, "Deep learning backdoors," arXiv preprint arXiv:2007.08273, 2020. · arXiv
  187. K. Hasegawa, M. Yanagisawa, and N. Togawa, "Trojan-net classification for gate-level hardware design utilizing boundary net structures," IEICE TRANSACTIONS on Information and Systems, vol. 103, no. 7, pp. 1618-1622, 2020.
  188. B. Kolosnjaji, A. Demontis, B. Biggio, D. Maiorca, G. Giacinto, C. Eckert, and F. Roli, "Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables," in 26th European Signal Processing Conf., EUSIPCO, (Rome), pp. 533-537, IEEE, 2018.
  189. A. Demontis, M. Melis, M. Pintor, M. Jagielski, B. Biggio, A. Oprea, C. Nita-Rotaru, and F. Roli, "Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks," in 28th USENIX Security Symposium (USENIX Security 19), USENIX Association, 2019.
  190. D. Maiorca, A. Demontis, B. Biggio, F. Roli, and G. Giacinto, "Adversarial Detection of Flash Malware: Limitations and Open Issues," Computers & Security, vol. 96, p. 101901, 2020.
  191. K. K. Ispoglou and M. Payer, "malwash: Washing malware to evade dynamic analysis," in 10th USENIX Workshop on Offensive Technolo- gies (WOOT 16), (Austin, TX), USENIX Association, Aug. 2016.
  192. O. Suciu, S. E. Coull, and J. Johns, "Exploring adversarial examples in malware detection," in 2019 IEEE Security and Privacy Workshops (SPW), pp. 8-14, 2019.
  193. L. Demetrio, B. Biggio, G. Lagorio, F. Roli, and A. Armando, "Functionality-preserving Black-box Optimization of Adversarial Windows Malware," arXiv:2003.13526 [cs], Sept. 2020. arXiv: 2003.13526. · arXiv
  194. F. Pierazzi, F. Pendlebury, J. Cortellazzi, and L. Cavallaro, "Intriguing properties of adversarial ml attacks in the problem space," in 2020 IEEE Symposium on Security and Privacy (SP), pp. 1332-1349, May 2020.
  195. F. Zhiyang, J. Wang, B. Li, S. Wu, Y. Zhou, and H. Huang, "Evading anti-malware engines with deep reinforcement learning," IEEE Access, vol. PP, pp. 1-1, 03 2019.
  196. A. C. Bahnsen, I. Torroledo, L. D. Camacho, and S. Villegas, "Deepphish: Simulating malicious ai," in 2018 APWG Symposium on Electronic Crime Research (eCrime), pp. 1-8, 2018.
  197. J. Peck, C. Nie, R. Sivaguru, C. Grumer, F. Olumofin, B. Yu, A. Nasci- mento, and M. De Cock, "Charbot: A simple and effective method for evading dga classifiers," IEEE Access, vol. 7, pp. 91759-91771, 2019.
  198. L. Sidi, A. Nadler, and A. Shabtai, "Maskdga: An evasion attack against dga classifiers and adversarial defenses," IEEE Access, vol. 8, pp. 161580-161592, 2020.
  199. Y. Sharon, D. Berend, Y. Liu, A. Shabtai, and Y. Elovici, "Tantra: Timing-based adversarial network traffic reshaping attack," arXiv preprint arXiv:2103.06297, 2021. · arXiv
  200. N. Dalvi, P. Domingos, Mausam, S. Sanghai, and D. Verma, "Adversarial classification," in Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), (Seattle), pp. 99-108, 2004.

200 of 210 shown

Cited by

Sorted by citations

Authors
Mirsky, Y., Demontis, A., Kotak, J., Shankar, R., Gelei, D., Yang, L., Zhang, X., Pintor, M., Lee, W., Elovici, Y., & Biggio, B.
Affiliations
  • Ben-Gurion University of the Negev (IL)
  • University of Cagliari (IT)
  • Microsoft (United States) (US)
  • Purdue University West Lafayette (US)
  • Georgia Institute of Technology (US)
Corresponding author
Mirsky, Yisroel
ORCID
Venue
Computers & Security
Publisher
Elsevier BV
Published
2023-01
Volume
124
Pages
103006
ISSN
0167-4048 (ISSN-L)
DOI
10.1016/j.cose.2022.103006
OpenAlex
W3176850402
Cited by
97
References
12
Citations by year
2026: 24 · 2025: 24 · 2024: 29 · 2023: 14 · 2022: 4 · 2021: 2
FWCI
5.84
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers & Security (publishedVersion, other-oa)
  • arXiv (Cornell University) (submittedVersion)
  • UNICA IRIS Institutional Research Information System (University of Cagliari) (submittedVersion, other-oa)
  • DR-NTU (Nanyang Technological University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Keywords
offensive AI, cyber kill chain, AI capabilities, attack models, organizational security

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review