Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

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Membership Inference Attacks Against Machine Learning Models

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RAI4IoE: Responsible AI for Enabling the Internet of Energy

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VeriCNN: Integrity verification of large-scale CNN training process based on zk-SNARK

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Federated Learning's Dynamic Defense Against Byzantine Attacks: Integrating SIFT-Wavelet and Differential Privacy for Byzantine Grade Levels Detection

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Securing the Autonomous Future A Comprehensive Analysis of Security Challenges and Mitigation Strategies for AI Agents

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Adaptive and Blind Regression for Mobile Crowd Sensing

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Mitigating Adversarial Attack for Compute-in-Memory Accelerator Utilizing On-chip Finetune

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Data Poisoning Attacks on Crowdsourcing Learning

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Optimal Feature Manipulation Attacks Against Linear Regression

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A Survey of Adversarial Attacks on Wireless Communications

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Classification Auto-Encoder Based Detector Against Diverse Data Poisoning Attacks

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FedCom: Byzantine-Robust Federated Learning Using Data Commitment

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Law and Adversarial Machine LearningPreprint

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FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated LearningPreprint

Zhao, B., Sun, P., Fang, L., Wang, T., & Jiang, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08020 · Cited by 5

Adversarial Attacks Detection Method for Tabular Data

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Robustness of Updatable Learning-based Index Advisors against Poisoning Attack

Zheng, Y., Lin, C., Lyu, X., Zhou, X., Li, G., & Wang, T. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3639265 · Cited by 4

DNN Partitioning and Assignment for Distributed Inference in SGX Empowered Edge Cloud

Li, Y.-P., Zeng, D.-Z., Gut, L., Guo, S., & Zomaya, A. Y. (2024) · IEEE International Conference on Distributed Computing Systems · doi:10.1109/icdcs60910.2024.00065 · Cited by 6

Defense-Guided Adaptive Attack on Byzantine-Robust Federated Learning

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From Accuracy to Vulnerability: Quantifying the Impact of Adversarial Perturbations on Healthcare AI Models

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Risk Assessment in Smart Aging Care Systems: An Elderly-Centered Perspective

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Byzantine detection for federated learning under highly non-IID data and majority corruptions

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Unlearning Attacks for Regression Learning

Chen, J., Shi, W., Lin, W., Wang, C., Liu, W., Sun, H., & Liu, G. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3553821 · Cited by 4

Mitigating Impact of Data Poisoning Attacks on CPS Anomaly Detection with Provable Guarantees

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On the Risks of Trusting Artificial Intelligence: The Case of Cybersecurity

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An Adversarial Risk Analysis Framework for Batch Acceptance Problems

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Machine Learning Integrity and Privacy in Adversarial Environments

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Adversarial Machine Learning

Hernández‐Castro, C. J., Liu, Z., Serban, A., Tsingenopoulos, I., & Joosen, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-98795-4_12 · Cited by 5

On developing deep learning models with particle swarm optimization in the presence of poisoning attacks

Maabreh, M., Darwish, O., Karajeh, O., & Tashtoush, Y. M. (2022) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit57182.2022.9994126 · Cited by 4

AAIA: an efficient aggregation scheme against inverting attack for federated learning

Yang, Z., Yang, S., Huang, Y., Martí­nez-Ortega, J.-F., López, L., & Chen, Y. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00670-6 · Cited by 5

Reducing Certified Regression to Certified Classification for General Poisoning Attacks

Hammoudeh, Z., & Lowd, D. (2023) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00040 · Cited by 5

A Survey on Security Threats to Machine Learning Systems at Different Stages of its Pipeline

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PALOR: Poisoning Attacks Against Logistic Regression

Wen, J., Zhao, B. Z. H., Xue, M., & Qian, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-55304-3_23 · Cited by 4

Analysis of Security of Machine Learning and a proposition of assessment pattern to deal with adversarial attacks

Ftaimi, A., & Mazri, T. (2021) · E3S Web of Conferences · doi:10.1051/e3sconf/202122901004 · Cited by 4

Poisoning Attack for Inter-agent Transfer Learning

Cheng, Z., & Li, Z. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-90022-9_21 · Cited by 4

Oriole: Thwarting Privacy Against Trustworthy Deep Learning Models

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Automated Binary Analysis: A Survey

Liu, Z., Chen, C., Ahmed, E., Liu, D., & Zhang, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-22677-9_21 · Cited by 4

EdgeInfer: Robust Truth Inference under Data Poisoning Attack

Tahmasebian, F., Xiong, L., Sotoodeh, M., & Sunderam, V. S. (2020) · IEEE International Conference on Smart Data Services (SMDS) · doi:10.1109/smds49396.2020.00013 · Cited by 3

Mitigation of poisoning attack in federated learning by using historical distance detection

Shi, Z., Ding, X., Li, F., Chen, Y., & Li, C. (2021) · Cyber Security in Networking Conference (CSNet) · doi:10.1109/csnet52717.2021.9614278 · Cited by 4

A Sub-Linear Time Framework for Geometric Optimization with Outliers in High DimensionsPreprint

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Quantifying and Mitigating Privacy Risks of Contrastive LearningPreprint

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Evaluating Adversarial Learning on Different Types of Deep Learning-based Intrusion Detection Systems using min-max optimization

Khamis, R. A. (2020) · Carleton University's Institutional Repository (MacOdrum Library, Carleton University) · doi:10.22215/etd/2020-14257 · Cited by 5

Proceedings of the 2023 Secure and Trustworthy Deep Learning Systems Workshop

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Pseudo-Labeling for Enhanced User Privacy in Approximate Machine Unlearning

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Broken Bags: Disrupting Service Through the Contamination of Large Language Models With Misinformation

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Multi-Agent AI Framework for Threat Mitigation and Resilience in Machine Learning Systems

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A Data-Centric Anomaly-Based Detection System for Interactive Machine Learning Setups

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Improving Social Bot Detection Through Aid and Training

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RMF: A Risk Measurement Framework for Machine Learning Models

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Towards Fair Federated Learning via Unbiased Feature Aggregation

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VVUQ, Reliability, and the Law

Kemper, B. (2025) · IEEE reliability magazine · doi:10.1109/mrl.2025.3532564 · Cited by 3

Multifeature Fusion-Based Closed-Box Attack Detection Method for Automatic Modulation Classification

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Maverick: Personalized Edge-Assisted Federated Learning with Contrastive Training

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Privacy and Security in Distributed Data Markets

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Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes

Jia, J., Wang, B., & Gong, N. Z. (2021) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3433210.3437519 · Cited by 4

Towards Evaluating Adversarial Attacks Robustness in Wireless Communication

Ftaimi, A., & Mazri, T. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120675 · Cited by 3

Data complexity-based batch sanitization method against poison in distributed learning

Wang, S., Fan, K., Zhang, K., Li, H., & Yang, Y. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.12.001 · Cited by 3

Safety and Security in AI Systems

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Clean-label Backdoor Attack on Machine Learning-based Malware Detection Models and Countermeasures

Zheng, W., & Omote, K. (2022) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom56396.2022.00171 · Cited by 3

Support Vector Machine, Naïve Baye’s, and Recurrent Neural Network to Detect Data Poisoning Attacks on Dataset

Bhadle, R. V., & Rathod, D. (2023) · Biennial International Conference on Nascent Technologies in Engineering (ICNTE) · doi:10.1109/icnte56631.2023.10146665 · Cited by 3

A Novel Poisoning Attack on Few-Shot based Network Intrusion Detection

Alhussien, N., & AlEroud, A. F. (2023) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms56928.2023.10154453 · Cited by 3

PPFL-IDS: Privacy-Preserving Federated Learning Based IDS Against Poisoning Attacks

Xu, M., & Li, X. (2023) · Mobile Networks and Applications · doi:10.1007/s11036-023-02231-6 · Cited by 3

State data security backed by Artificial Intelligence and Zero Knowledge Proofs in the context of sanctions and economic pressure

Jalili, A. Q., Dziatkovskii, A., & Australia, P. V. &. I. (2023) · Economic Annals-ХХI · doi:10.21003/ea.v202-01 · Cited by 3

Enhancing Trust and Reliability in AI and ML: Assessing Blockchain's Potential to Ensure Data Integrity and Security

Siddika, A., & Zhao, L. (2023) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/cbdcom/cy59711.2023.10361446 · Cited by 4

FedChallenger: Challenge-Response-Based Defence for Federated Learning Against Byzantine Attacks

Moyeen, M. A., Kaur, K., Agarwal, A., Ricardo Manzano, S., Zaman, M., & Goel, N. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437878 · Cited by 3

Study on Poisoning Attacks: Application Through an IoT Temperature Dataset

Vuseghesa, F. K., & Messai, M. (2023) · IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) · doi:10.1109/wetice57085.2023.10477844 · Cited by 3

Research Progress on Security and Privacy of Federated Learning: A Survey

Ma, X., & Yan, M. (2024) · Wireless Personal Communications · doi:10.1007/s11277-024-11372-0 · Cited by 3

Interactive Trimming Against Evasive Online Data Manipulation Attacks: A Game-Theoretic Approach

Fu, Y., Ye, Q., Du, R., & Hu, H. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00276 · Cited by 5

Achieving Secure On-Orbit Comparison in LEO-Satellite-Enabled Offshore Wind Farm Surveillance

Kong, Q., Wang, Y., Zhang, S., Chen, B., Xiao, S., Bao, H., & Shao, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3454765 · Cited by 3

Security Requirements for Fully Automated AI Systems to Exercise and Ensure the Rights of Data Subjects

Park, J., You, G., Ji, Y., & Youm, H. Y. (2024) · Asia Joint Conference on Information Security (AsiaJCIS) · doi:10.1109/asiajcis64263.2024.00026 · Cited by 3

Contrastive Learning for Distortion Tolerable Network Slice Prediction in Open RAN

He, Z., Alimohammadi, H., Chatzimiltis, S., Mayhoub, S., Akbari, M., & Shojafar, M. (2025) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc61545.2025.10978529 · Cited by 3

Preference Poisoning Attacks on Reward Model Learning

Wu, J., Wang, J., Xiao, C., Wang, C., Zhang, N., & Vorobeychik, Y. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00094 · Cited by 17

Poisoning finite-horizon Markov decision processes at design time

Caballero, W. N., Jenkins, P. R., & Keith, A. J. (2020) · Computers & Operations Research · doi:10.1016/j.cor.2020.105185 · Cited by 3

In Pursuit of Preserving the Fidelity of Adversarial Images

Clements, J., & Lao, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747529 · Cited by 3

One-pixel and X-pixel adversarial attacks based on smell bees optimization algorithm

Ali, Y. M. B. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.07.028 · Cited by 3

A Framework for Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systems.Preprint

Bitton, R., Maman, N., Singh, I., Momiyama, S., Elovici, Y., & Shabtai, A. (2021) · arXiv (Cornell University) · Cited by 3

Reliability analysis and data driven modelling of railway component failure

Wang, J. (2021) · doi:10.32657/10356/159237 · Cited by 3

Secure and Privacy-preserving Federated Learning with Explainable Artificial Intelligence for Smart Healthcare System

Raza, A. (2023) · theses.fr (ABES) · doi:10.70675/ac35dc8az89ccz4977z8f25zb7c8937f7cf0 · Cited by 4

Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents

de Witt, C. S., Krawiecka, K., Krawczuk, I., Hagag, B., Anderson, W. L., Belcak, P., Bucknall, B., Cai, X., Chopra, A., Cohen, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.02077 · Cited by 108

FedPerturb: Covert Poisoning Attack on Federated Learning via Partial Perturbation

Jin, T., Fu, Z., Meng, D., Wang, J., Qi, Y., & Cao, G. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230393 · Cited by 2

Enhancing Model Poisoning Attacks to Byzantine-Robust Federated Learning via Critical Learning Periods

Yan, G., Wang, H., Yuan, X., & Li, J. (2024) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1145/3678890.3678915 · Cited by 2

SLAPP: Poisoning Prevention in Federated Learning and Differential Privacy via Stateful Proofs of Execution

Rattanavipanon, N., & Nunes, I. D. O. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3555179 · Cited by 5

Deep Learning for Network Anomaly Detection Under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation

Nkashama, D. K., Masakuna, J. F., Soltani, A., Verdier, J.-C., Frappier, M., Tardif, P.-M., & Kabanza, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-82362-6_5 · Cited by 2

Robust Aggregation with Adversarial Experts

Guo, Y., & Kong, Y. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714557 · Cited by 2

Reducing Defense Vulnerabilities in Federated Learning: A Neuron-Centric Approach

Erdöl, E. S., Erdöl, H., Üstübioğlu, B., Ulutaş, G., & Symeonidis, I. (2025) · Applied Sciences · doi:10.3390/app15116007 · Cited by 2

Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning

Gong, Z., Zhang, Y., Zhang, L. Y., Zhang, Z., Xiang, Y., & Pan, S. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00251 · Cited by 2

Untargeted Poisoning Membership Inference With Sample Selection and Enhancement

Li, J., Yang, X., Ma, W., Wang, B., Wen, S., & Xiang, Y. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3601844 · Cited by 2

Resilient automatic model selection for mobility prediction

Atiiq, S. A., Gehrmann, C., Khalil, K., Sternby, J., & Yuan, Y. (2025) · Cluster Computing · doi:10.1007/s10586-025-05661-x · Cited by 2

Ethical and equitable approaches in AI for vector-borne disease management

Williams, J. J., Angelidou, I. A., Cholvi-Simó, M., Kadriaj, P., Martinou, A. F., Nadejda, M., Ong, S., Sadak, F., Skuhrovec, J., Velo, E., et al. (2025) · AI and Ethics · doi:10.1007/s43681-025-00933-z · Cited by 4

EndPCA: Ensemble Defense With Provably Convergent Aggregation Against Poisoning Attacks in Federated Learning

Zhang, M., Hu, C., Cao, X., Sajjanhar, A., Yang, Z., Hassan, M. U., Jin, Z. G., & Li, J. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3655513 · Cited by 2

Poisoning Attack against Online Regression Learning with Maximum Loss for Edge Intelligence

Zhu, Y., Wen, H., Zhang, P., Han, W. M., Sun, F., & Jia, J. (2022) · International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT) · doi:10.1109/ccpqt56151.2022.00037 · Cited by 2

Depth-2 neural networks under a data-poisoning attack

Karmakar, S., Mukherjee, A., & Papamarkou, T. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.034 · Cited by 8

An Artificial Intelligence Enabled Self Replication System Against Cyber Attacks

Mokkapati, R., & Dasari, V. L. (2023) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit55814.2023.10061089 · Cited by 2

Trust and Physical Layer Security for 6G Cyber-Physical Systems

Chorti, A. (2023) · Harvard Data Science Review · doi:10.1162/99608f92.3a330a0b · Cited by 2

MPHM: Model poisoning attacks on federal learning using historical information momentum

Shi, L., Chen, Z., Shi, Y., Lin, W., Tao, Y., He, M., Wang, Q., Zhou, Y. H., & Gao, Y. (2023) · Security and Safety · doi:10.1051/sands/2023006 · Cited by 2

Poisoning Online Learning Filters by Shifting on the Move

Tann, W. J.-W., & Chang, E. (2023) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn58367.2023.00033 · Cited by 2

Double Momentum Backdoor Attack in Federated Learning

Panigrahi, S., Bouacida, N., & Mohapatra, P. (2023) · Journal of Student Research · doi:10.47611/jsrhs.v12i1.3644 · Cited by 2

Adversarial Text Rewriting for Text-aware Recommender Systems

Oh, S., Verma, G., & Kumar, S. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679592 · Cited by 2

Stealthy Backdoor Attack against Federated Learning through Frequency Domain by Backdoor Neuron Constraint and Model Camouflage

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