A repo for some of the most influential papers, books, & other media on machine learning operations (MLOps), adversarial machine learning, AIML policy and compliance, & more.
Read my work on MLSecOps:
Securing AIML Systems in the Age of Information Warfare
An OODA Loop-based game theoretic model of information warfare in AI/ML systems, with boolean-path threat modeling & cyber resiliency metrics, + a secure, operationalized, platform-agnostic, fully audited debiasing architecture adapted to the canonical MLOps development cycle
- AIML Security Surveys & Taxonomies
- Adversarial AIML Attacks
- AIML Security for Cyber-Physical Systems & IoT
- AIML Cloud Security
- AIML Intrusion & Malware Detection Systems
- Federation
- MLOps
- Threat Modeling & Secure Design
- Datasets
Barreno, Marco, Blaine Nelson, Anthony D. Joseph and J. Doug Tygar. “The security of machine learning.” Machine Learning 81 (2010): 121-148.
Brundage, Miles, Shahar Avin, Jack Clark, Helen Toner, Peter Eckersley, Ben Garfinkel, Allan Dafoe, Paul Scharre, Thomas Zeitzoff, Bobby Filar, H. Anderson, Heather Roff, Gregory C. Allen, Jacob Steinhardt, Carrick Flynn, Seán Ó hÉigeartaigh, Simon Beard, Haydn Belfield, Sebastian Farquhar, Clare Lyle, Rebecca Crootof, Owain Evans, Michael Page, Joanna J. Bryson, Roman V. Yampolskiy and Dario Amodei. “The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation.” ArXiv abs/1802.07228 (2018): n. pag.
Kaloudi, Nektaria and Jingyue Li. “The AI-Based Cyber Threat Landscape.” ACM Computing Surveys (CSUR) 53 (2020): 1 - 34.
Bae, Ho, Jaehee Jang, Dahuin Jung, Hyemi Jang, Heonseok Ha and Sungroh Yoon. “Security and Privacy Issues in Deep Learning.” ArXiv abs/1807.11655 (2018): n. pag.
Chen, Huaming and M Ali Babar. “Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges.” ArXiv abs/2201.04736 (2022): n. pag.
Liu, Ximeng, Lehui Xie, Yaopeng Wang, Jian Zou, Jinbo Xiong, Zuobin Ying and Athanasios V. Vasilakos. “Privacy and Security Issues in Deep Learning: A Survey.” IEEE Access 9 (2021): 4566-4593.
Muñoz-González, Luis and Emil C. Lupu. “The Security of Machine Learning Systems.” AI in Cybersecurity (2018): n. pag.
Singh, Shivani, Razia Sulthana, Tanvi Shewale, Vinay Chamola, Abderrahim Benslimane and Biplab Kumar Sikdar. “Machine-Learning-Assisted Security and Privacy Provisioning for Edge Computing: A Survey.” IEEE Internet of Things Journal 9 (2022): 236-260.
Rosenberg, Ishai, Asaf Shabtai, Yuval Elovici and Lior Rokach. “Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain.” ACM Computing Surveys (CSUR) 54 (2021): 1 - 36.
Qiu, Shilin, Qihe Liu, Shijie Zhou and Chunjiang Wu. “Review of Artificial Intelligence Adversarial Attack and Defense Technologies.” Applied Sciences (2019): n. pag.
Masys, Anthony, Mamoun Alazab and Mingjian Tang. “Deep Learning Applications for Cyber Security.” Deep Learning Applications for Cyber Security (2019): n. pag.
Wang, Xianmin, Jing Li, Xiaohui Kuang, Yu‐an Tan and Jin Li. “The security of machine learning in an adversarial setting: A survey.” J. Parallel Distributed Comput. 130 (2019): 12-23.
Salloum, Said A., Muhammad Turki Alshurideh, Ashraf Elnagar and Khaled F. Shaalan. “Machine Learning and Deep Learning Techniques for Cybersecurity: A Review.” International Conferences on Artificial Intelligence and Computer Vision (2020).
Li, Jian-hua. “Cyber security meets artificial intelligence: a survey.” Frontiers of Information Technology & Electronic Engineering 19 (2018): 1462-1474.
Xin, Yang, Lingshuang Kong, Zhi Liu, Yuling Chen, Yanmiao Li, Hongliang Zhu, Mingcheng Gao, Haixia Hou and Chunhua Wang. “Machine Learning and Deep Learning Methods for Cybersecurity.” IEEE Access 6 (2018): 35365-35381.
Rodríguez, Eva, Beatriz Otero, Norma Gutiérrez and Ramon Canal. “A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks.” IEEE Communications Surveys & Tutorials 23 (2021): 1920-1955.
Mahdavifar, Samaneh and Ali A. Ghorbani. “Application of deep learning to cybersecurity: A survey.” Neurocomputing 347 (2019): 149-176.
Dixit, Priyanka and Sanjay Silakari. “Deep Learning Algorithms for Cybersecurity Applications: A Technological and Status Review.” Comput. Sci. Rev. 39 (2021): 100317.
Liu, Qiang, Pan Li, Wentao Zhao, Wei Cai, Shui Yu and Victor C. M. Leung. “A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View.” IEEE Access 6 (2018): 12103-12117.
Kong, Zixiao, Jingfeng Xue, Yong Wang, Lu Huang, Zequn Niu and Feng Li. “A Survey on Adversarial Attack in the Age of Artificial Intelligence.” Wirel. Commun. Mob. Comput. 2021 (2021): 4907754:1-4907754:22.
Chakraborty, Anirban, Manaar Alam, Vishal Dey, Anupam Chattopadhyay and Debdeep Mukhopadhyay. “Adversarial Attacks and Defences: A Survey.” ArXiv abs/1810.00069 (2018): n. pag.
Raji, Inioluwa Deborah, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron and Parker Barnes. “Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing.” Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (2020): n. pag.
Kurakin, Alexey, Ian J. Goodfellow and Samy Bengio. “Adversarial Machine Learning at Scale.” ArXiv abs/1611.01236 (2016): n. pag.
Narodytska, Nina and Shiva Prasad Kasiviswanathan. “Simple Black-Box Adversarial Attacks on Deep Neural Networks.” 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017): 1310-1318.
Oseni, Ayodeji, Nour Moustafa, Helge Janicke, Peng Liu, Zahir Tari and Athanasios V. Vasilakos. “Security and Privacy for Artificial Intelligence: Opportunities and Challenges.” ArXiv abs/2102.04661 (2021): n. pag.
Clements, Joseph, Yuzhe Yang, Ankur A Sharma, Hongxin Hu and Yingjie Lao. “Rallying Adversarial Techniques against Deep Learning for Network Security.” 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (2019): 01-08.
Biggio, Battista and Fabio Roli. “Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning.” Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (2017): n. pag.
Kuppa, Aditya, Slawomir Grzonkowski, Muhammad Rizwan Asghar and Nhien-An Le-Khac. “Black Box Attacks on Deep Anomaly Detectors.” Proceedings of the 14th International Conference on Availability, Reliability and Security (2019): n. pag.
Lin, Zilong, Yong-yu Shi and Zhi Xue. “IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection.” Pacific-Asia Conference on Knowledge Discovery and Data Mining (2018).
Akhtar, Naveed and Ajmal S. Mian. “Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey.” IEEE Access 6 (2018): 14410-14430.
Tramèr, Florian, Fan Zhang, Ari Juels, Michael K. Reiter and Thomas Ristenpart. “Stealing Machine Learning Models via Prediction APIs.” ArXiv abs/1609.02943 (2016): n. pag.
Wu, Baoyuan, Li Liu, Zihao Zhu, Qin Liu, Zhaofeng He and Siwei Lyu. “Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective.” (2023).
Geiping, Jonas, Liam Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller and Tom Goldstein. “Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching.” ArXiv abs/2009.02276 (2020): n. pag.
Schwarzschild, Avi, Micah Goldblum, Arjun Gupta, John P. Dickerson and Tom Goldstein. “Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks.” International Conference on Machine Learning (2020).
Lécuyer, Mathias, Vaggelis Atlidakis, Roxana Geambasu, Daniel J. Hsu and Suman Sekhar Jana. “On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning.” ArXiv abs/1802.03471 (2018): n. pag.
Tramèr, Florian, R. Shokri, Ayrton San Joaquin, Hoang M. Le, Matthew Jagielski, Sanghyun Hong and Nicholas Carlini. “Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets.” Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (2022): n. pag.
Steinhardt, Jacob, Pang Wei Koh and Percy Liang. “Certified Defenses for Data Poisoning Attacks.” ArXiv abs/1706.03691 (2017): n. pag.
Shokri, R., Marco Stronati, Congzheng Song and Vitaly Shmatikov. “Membership Inference Attacks Against Machine Learning Models.” 2017 IEEE Symposium on Security and Privacy (SP) (2016): 3-18.
Hu, Hongsheng, Zoran A. Salcic, Lichao Sun, Gillian Dobbie, P. Yu and Xuyun Zhang. “Membership Inference Attacks on Machine Learning: A Survey.” ACM Computing Surveys (CSUR) 54 (2021): 1 - 37.
Hu, Hongsheng, Zoran A. Salcic, Lichao Sun, Gillian Dobbie, P. Yu and Xuyun Zhang. “Membership Inference Attacks on Machine Learning: A Survey.” ACM Computing Surveys (CSUR) 54 (2021): 1 - 37.
Salem, A., Yang Zhang, Mathias Humbert, Mario Fritz and Michael Backes. “ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models.” ArXiv abs/1806.01246 (2018): n. pag.
Carlini, Nicholas, Steve Chien, Milad Nasr, Shuang Song, A. Terzis and Florian Tramèr. “Membership Inference Attacks From First Principles.” 2022 IEEE Symposium on Security and Privacy (SP) (2021): 1897-1914.
Hilprecht, Benjamin, Martin Härterich and Daniel Bernau. “Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models.” Proceedings on Privacy Enhancing Technologies 2019 (2019): 232 - 249.
Choquette-Choo, Christopher A., Florian Tramèr, Nicholas Carlini and Nicolas Papernot. “Label-Only Membership Inference Attacks.” ArXiv abs/2007.14321 (2020): n. pag.
Li, Zheng and Yang Zhang. “Membership Leakage in Label-Only Exposures.” Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security (2020): n. pag.
Salem, A., Apratim Bhattacharyya, Michael Backes, Mario Fritz and Yang Zhang. “Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning.” USENIX Security Symposium (2019).
Nasr, Milad, R. Shokri and Amir Houmansadr. “Machine Learning with Membership Privacy using Adversarial Regularization.” Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (2018): n. pag.
Song, Liwei and Prateek Mittal. “Systematic Evaluation of Privacy Risks of Machine Learning Models.” USENIX Security Symposium (2020).
Truex, Stacey, Ling Liu, Mehmet Emre Gursoy, Lei Yu and Wenqi Wei. “Towards Demystifying Membership Inference Attacks.” ArXiv abs/1807.09173 (2018): n. pag.
Li, Zheng and Yang Zhang. “Label-Leaks: Membership Inference Attack with Label.” ArXiv abs/2007.15528 (2020): n. pag.
Yeom, Samuel, Irene Giacomelli, Matt Fredrikson and Somesh Jha. “Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting.” 2018 IEEE 31st Computer Security Foundations Symposium (CSF) (2017): 268-282.
Long, Yunhui, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A. Gunter and Kai Chen. “Understanding Membership Inferences on Well-Generalized Learning Models.” ArXiv abs/1802.04889 (2018): n. pag.
Zou, Yang, Zhikun Zhang, Michael Backes and Yang Zhang. “Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning.” ArXiv abs/2009.04872 (2020): n. pag.
Liu, Yugeng, Rui Wen, Xinlei He, A. Salem, Zhikun Zhang, Michael Backes, Emiliano De Cristofaro, Mario Fritz and Yang Zhang. “ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models.” USENIX Security Symposium (2021).
Rahimian, Shadi, Tribhuvanesh Orekondy and Mario Fritz. “Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queries.” ArXiv abs/2009.00395 (2020): n. pag.
He, Xinlei, Zheng Li, Weilin Xu, Cory Cornelius and Yang Zhang. “Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models.” ArXiv abs/2208.10445 (2022): n. pag.
Wang, Chen, Gaoyang Liu, Haojun Huang, Weijie Feng, Kai Peng and Lizhe Wang. “MIASec: Enabling Data Indistinguishability Against Membership Inference Attacks in MLaaS.” IEEE Transactions on Sustainable Computing 5 (2020): 365-376.
Long, Yunhui, Lei Wang, Diyue Bu, Vincent Bindschaedler, Xiaofeng Wang, Haixu Tang, Carl A. Gunter and Kai Chen. “A Pragmatic Approach to Membership Inferences on Machine Learning Models.” 2020 IEEE European Symposium on Security and Privacy (EuroS&P) (2020): 521-534.
Tramèr, Florian, R. Shokri, Ayrton San Joaquin, Hoang M. Le, Matthew Jagielski, Sanghyun Hong and Nicholas Carlini. “Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets.” Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (2022): n. pag.
Li, Jiacheng, Ninghui Li and Bruno Ribeiro. “Membership Inference Attacks and Defenses in Classification Models.” Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy (2020): n. pag.
Ye, Jiayuan, Aadyaa Maddi, Sasi Kumar Murakonda and R. Shokri. “Enhanced Membership Inference Attacks against Machine Learning Models.” Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (2021): n. pag.
Farokhi, Farhad and Mohamed Ali Kâafar. “Modelling and Quantifying Membership Information Leakage in Machine Learning.” ArXiv abs/2001.10648 (2020): n. pag.
Balle, Borja, Giovanni Cherubin and Jamie Hayes. “Reconstructing Training Data with Informed Adversaries.” 2022 IEEE Symposium on Security and Privacy (SP) (2022): 1138-1156.
Liu, Changchang, Supriyo Chakraborty and Prateek Mittal. “Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples.” Network and Distributed System Security Symposium (2016).
Truex, Stacey, Ling Liu, Mehmet Emre Gursoy, Wenqi Wei and Lei Yu. “Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability.” 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) (2019): 82-91.
Rahman, Md.Atiqur, Tanzila Rahman, Robert Laganière and Noman Mohammed. “Membership Inference Attack against Differentially Private Deep Learning Model.” Trans. Data Priv. 11 (2018): 61-79.
Cao, Yang, Masatoshi Yoshikawa, Yonghui Xiao and Li Xiong. “Quantifying Differential Privacy under Temporal Correlations.” 2017 IEEE 33rd International Conference on Data Engineering (ICDE) (2016): 821-832.
Jin, Jiankai, Eleanor McMurtry, Benjamin I. P. Rubinstein and Olga Ohrimenko. “Are We There Yet? Timing and Floating-Point Attacks on Differential Privacy Systems.” 2022 IEEE Symposium on Security and Privacy (SP) (2021): 473-488.
Cao, Yang, Masatoshi Yoshikawa, Yonghui Xiao and Li Xiong. “Quantifying Differential Privacy in Continuous Data Release Under Temporal Correlations.” IEEE Transactions on Knowledge and Data Engineering 31 (2017): 1281-1295.
Kato, Fumiyuki, Yang Cao and Masatoshi Yoshikawa. “Preventing Manipulation Attack in Local Differential Privacy using Verifiable Randomization Mechanism.” ArXiv abs/2104.06569 (2021): n. pag.
Zhang, Bo, Ruotong Yu, Haipei Sun, Yanying Li, Jun Xu and Wendy Hui Wang. “Privacy for All: Demystify Vulnerability Disparity of Differential Privacy against Membership Inference Attack.” ArXiv abs/2001.08855 (2020): n. pag.
Zhao, Jun, Junshan Zhang and H. Vincent Poor. “Dependent Differential Privacy for Correlated Data.” 2017 IEEE Globecom Workshops (GC Wkshps) (2017): 1-7.
Murakami, Takao and Yusuke Kawamoto. “Utility-Optimized Local Differential Privacy Mechanisms for Distribution Estimation.” USENIX Security Symposium (2018).
Humphries, Thomas, Matthew Rafuse, Lindsey Tulloch, Simon Oya, Ian Goldberg and Florian Kerschbaum. “Differentially Private Learning Does Not Bound Membership Inference.” ArXiv abs/2010.12112 (2020): n. pag.
Haney, Samuel, Damien Desfontaines, Luke Hartman, Ruchit Shrestha and Michael Hay. “Precision-based attacks and interval refining: how to break, then fix, differential privacy on finite computers.” ArXiv abs/2207.13793 (2022): n. pag.
Holohan, Naoise and Stefano Braghin. “Secure Random Sampling in Differential Privacy.” European Symposium on Research in Computer Security (2021).
Yan, Haonan, Xiaoguang Li, Hui Li, Jiamin Li, Wenhai Sun and Fenghua Li. “Monitoring-Based Differential Privacy Mechanism Against Query Flooding-Based Model Extraction Attack.” IEEE Transactions on Dependable and Secure Computing 19 (2021): 2680-2694.
Meiser, Sebastian and Esfandiar Mohammadi. “Tight on Budget?: Tight Bounds for r-Fold Approximate Differential Privacy.” Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (2018): n. pag.
Gao, Jie, Ruobin Gong and Fang-Yi Yu. “Subspace Differential Privacy.” AAAI Conference on Artificial Intelligence (2021).
Almadhoun, Nour, Erman Ayday and Özgür Ulusoy. “Differential privacy under dependent tuples - the case of genomic privacy.” Bioinformatics (2019): n. pag.
Ji, Tianxi, Pan Li, Emre Yilmaz, Erman Ayday, Yanfang Ye and Jinyuan Sun. “Differentially Private Binary- and Matrix-Valued Data Query: An XOR Mechanism.” Proc. VLDB Endow. 14 (2021): 849-862.
Almadhoun, Nour, Erman Ayday and Özgür Ulusoy. “Inference attacks against differentially private query results from genomic datasets including dependent tuples.” Bioinformatics 36 (2020): i136 - i145.
Wang, Hongya, Zhengquan Xu, Shan Jia, Ying Xia and Xu Zhang. “Why current differential privacy schemes are inapplicable for correlated data publishing?” World Wide Web 24 (2020): 1-23.
Boenisch, Franziska, Philip Sperl and Konstantin Böttinger. “Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning.” ArXiv abs/2105.07985 (2021): n. pag.
Chen, Junjie, Wendy Hui Wang and Xinghua Shi. “Differential Privacy Protection Against Membership Inference Attack on Machine Learning for Genomic Data.” bioRxiv (2020): n. pag.
Choi, Seung Geol, Dana Dachman-Soled, Mukul Kulkarni and Arkady Yerukhimovich. “Differentially-Private Multi-Party Sketching for Large-Scale Statistics.” Proceedings on Privacy Enhancing Technologies 2020 (2020): 153 - 174.
Rassouli, Borzoo, Fernando E. Rosas and Deniz Gündüz. “Data Disclosure Under Perfect Sample Privacy.” IEEE Transactions on Information Forensics and Security 15 (2019): 2012-2025.
Liu, Changchang, Xi He, Thee Chanyaswad, Shiqiang Wang and Prateek Mittal. “Investigating Statistical Privacy Frameworks from the Perspective of Hypothesis Testing.” Proceedings on Privacy Enhancing Technologies 2019 (2019): 233 - 254.
Wang, Jincheng, Zhuohua Li, John C.S. Lui and Mingshen Sun. “Topology-Theoretic Approach To Address Attribute Linkage Attacks In Differential Privacy.” IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (2021): 1-6.
Olowononi, Felix O., Danda B. Rawat and Chunmei Liu. “Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS.” IEEE Communications Surveys & Tutorials 23 (2021): 524-552.
Kim, Sangjun and Kyung-Joon Park. “A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems.” Applied Sciences 11 (2021): 5458.
Freitas de Araujo-Filho, Paulo, Georges Kaddoum, Divanilson R. Campelo, Aline Gondim Santos, David Macêdo and C. Zanchettin. “Intrusion Detection for Cyber–Physical Systems Using Generative Adversarial Networks in Fog Environment.” IEEE Internet of Things Journal 8 (2020): 6247-6256.
Tan, Sen, Josep M. Guerrero, Peilin Xie, Renke Han and Juan. C. Vasquez. “Brief Survey on Attack Detection Methods for Cyber-Physical Systems.” IEEE Systems Journal 14 (2020): 5329-5339.
Khalid, Faiq, Semeen Rehman and Muhammad Akmal Shafique. “Overview of Security for Smart Cyber-Physical Systems.” (2020).
Keliris, Anastasis, Hossein Salehghaffari, Brian R. Cairl, Prashanth Krishnamurthy, Michail Maniatakos and Farshad Khorrami. “Machine learning-based defense against process-aware attacks on Industrial Control Systems.” 2016 IEEE International Test Conference (ITC) (2016): 1-10.
Sarkar, Esha, Hadjer Benkraouda and Michail Maniatakos. “I came, I saw, I hacked: Automated Generation of Process-independent Attacks for Industrial Control Systems.” Proceedings of the 15th ACM Asia Conference on Computer and Communications Security (2020): n. pag.
Castellanos, John Henry, Martín Ochoa, Alvaro A. Cárdenas, Owen Arden and Jianying Zhou. “AttkFinder: Discovering Attack Vectors in PLC Programs using Information Flow Analysis.” Proceedings of the 24th International Symposium on Research in Attacks, Intrusions and Defenses (2021): n. pag.
Bernieri, Giuseppe, Mauro Conti and Federico Turrin. “KingFisher: an Industrial Security Framework based on Variational Autoencoders.” Proceedings of the 1st Workshop on Machine Learning on Edge in Sensor Systems (2019): n. pag.
Conti, Mauro, Denis Donadel and Federico Turrin. “A Survey on Industrial Control System Testbeds and Datasets for Security Research.” IEEE Communications Surveys & Tutorials 23 (2021): 2248-2294.
Schuster, Franka, Fabian Malte Kopp, Andreas Paul and Hartmut König. “Attack and Fault Detection in Process Control Communication Using Unsupervised Machine Learning.” 2018 IEEE 16th International Conference on Industrial Informatics (INDIN) (2018): 433-438.
Tushar, Wayes, Chau Yuen, T. K. Saha, Sohrab Nizami, Mollah Rezaul Alam, David B. Smith and H. Vincent Poor. “A Survey of Cyber-Physical Systems From a Game-Theoretic Perspective.” IEEE Access 11 (2023): 9799-9834.
Wickramasinghe, Chathurika S., Daniel L. Marino, Kasun Amarasinghe and Milos Manic. “Generalization of Deep Learning for Cyber-Physical System Security: A Survey.” IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society (2018): 745-751.
Al-garadi, Mohammed Ali, Amr M. Mohamed, Abdulla Khalid Al-Ali, Xiaojiang Du, Ihsan Ali and Mohsen Guizani. “A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security.” IEEE Communications Surveys & Tutorials 22 (2018): 1646-1685.
Asharf, Javed, Nour Moustafa, Hasnat Khurshid, Essam Soliman Debie, Waqas Haider and Abdul Wahab. “A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions.” Electronics (2020): n. pag.
Hussain, Fatima, Rasheed Hussain, Syed Ali Hassan and Ekram Hossain. “Machine Learning in IoT Security: Current Solutions and Future Challenges.” IEEE Communications Surveys & Tutorials 22 (2019): 1686-1721.
Zaman, Shakila, Khaled Alhazmi, Mohammed A. S. Aseeri, Muhammad R. Ahmed, Risala Tasin Khan, M. Shamim Kaiser and Mufti Mahmud. “Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey.” IEEE Access 9 (2021): 94668-94690.
Xiao, Liang, Xiaoyue Wan, Xiaozhen Lu, Yanyong Zhang and Di Wu. “IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?” IEEE Signal Processing Magazine 35 (2018): 41-49.
Restuccia, Francesco, Salvatore D’oro and Tommaso Melodia. “Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking.” IEEE Internet of Things Journal 5 (2018): 4829-4842.
Sagduyu, Yalin Evren, Yi Shi and Tugba Erpek. “IoT Network Security from the Perspective of Adversarial Deep Learning.” 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) (2019): 1-9.
Sagduyu, Yalin Evren, Yi Shi, Tugba Erpek, William C. Headley, Bryse Flowers, George Stantchev and Zhuo Lu. “When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions.” ArXiv abs/2001.08883 (2020): n. pag.
Luo, Zhengping, Shangqing Zhao, Zhuo Lu, Yalin Evren Sagduyu and Jie Xu. “Adversarial machine learning based partial-model attack in IoT.” Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning (2020): n. pag.
Shi, Yi, Yalin Evren Sagduyu, Tugba Erpek and Mustafa Cenk Gursoy. “How to Attack and Defend NextG Radio Access Network Slicing With Reinforcement Learning.” IEEE Open Journal of Vehicular Technology 4 (2021): 181-192.
Shi, Yi and Yalin Evren Sagduyu. “Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing.” 2021 IEEE International Conference on Communications Workshops (ICC Workshops) (2021): 1-6.
Pirayesh, Hossein and Huacheng Zeng. “Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey.” IEEE Communications Surveys & Tutorials 24 (2021): 767-809.
Adesina, Damilola, Chung-Chu George Hsieh, Yalin Evren Sagduyu and Lijun Qian. “Adversarial Machine Learning in Wireless Communications using RF Data: A Review.” IEEE Communications Surveys & Tutorials (2020): n. pag.
Sadeghi, Meysam and Erik G. Larsson. “Adversarial Attacks on Deep-Learning Based Radio Signal Classification.” IEEE Wireless Communications Letters 8 (2018): 213-216.
Manoj, B. R., Meysam Sadeghi and Erik G. Larsson. “Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network.” ICC 2021 - IEEE International Conference on Communications (2021): 1-6.
Peng, Shengliang, Shujun Sun and Yu-dong Yao. “A Survey of Modulation Classification Using Deep Learning: Signal Representation and Data Preprocessing.” IEEE Transactions on Neural Networks and Learning Systems 33 (2021): 7020-7038.
Sagduyu, Yalin Evren, Tugba Erpek and Yi Shi. “Adversarial Machine Learning for 5G Communications Security.” ArXiv abs/2101.02656 (2021): n. pag.
Bao, Zhida, Yun Lin, Sicheng Zhang, Zixin Li and Shiwen Mao. “Threat of Adversarial Attacks on DL-Based IoT Device Identification.” IEEE Internet of Things Journal 9 (2022): 9012-9024.
Bout, Emilie, Valeria Loscrí and Antoine Gallais. “How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey.” IEEE Communications Surveys & Tutorials 24 (2022): 248-279.
Shi, Yi and Yalin Evren Sagduyu. “Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning.” ArXiv abs/2107.12173 (2021): n. pag.
Davaslioglu, Kemal and Yalin Evren Sagduyu. “Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning.” 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) (2019): 1-6.
Kim, Brian, Yalin Evren Sagduyu, Kemal Davaslioglu, Tugba Erpek and Sennur Ulukus. “Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers.” IEEE Transactions on Wireless Communications PP (2020): 1-1.
Luo, Zhengping, Shangqing Zhao, Zhuo Lu, Jie Xu and Yalin Evren Sagduyu. “When Attackers Meet AI: Learning-Empowered Attacks in Cooperative Spectrum Sensing.” IEEE Transactions on Mobile Computing 21 (2019): 1892-1908.
Nassif, Ali Bou, Manar Abu Talib, Qassim Nasir, Halah Albadani and Fatima Mohamad Dakalbab. “Machine Learning for Cloud Security: A Systematic Review.” IEEE Access 9 (2021): 20717-20735.
Tabrizchi, Hamed and Marjan Kuchaki Rafsanjani. “A survey on security challenges in cloud computing: issues, threats, and solutions.” The Journal of Supercomputing (2020): 1-40.
Salman, Tara, Deval Bhamare, Aiman Erbad, Raj Jain and Mohammed Samaka. “Machine Learning for Anomaly Detection and Categorization in Multi-Cloud Environments.” 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) (2017): 97-103.
Abbasi, Hossein, Naser Ezzati-Jivan, Martine Bellaïche, Chamseddine Talhi and Michel R. Dagenais. “Machine Learning-Based EDoS Attack Detection Technique Using Execution Trace Analysis.” Journal of Hardware and Systems Security 3 (2019): 164-176.
Bhamare, Deval, Tara Salman, Mohammed Samaka, Aiman Erbad and Raj Jain. “Feasibility of Supervised Machine Learning for Cloud Security.” 2016 International Conference on Information Science and Security (ICISS) (2016): 1-5.
Grosse, Kathrin, Nicolas Papernot, Praveen Manoharan, Michael Backes and Patrick Mcdaniel. “Adversarial Perturbations Against Deep Neural Networks for Malware Classification.” ArXiv abs/1606.04435 (2016): n. pag.
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