Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507

Auto-Encoding Variational Bayes

Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067

Intriguing properties of neural networks

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143

Overcoming catastrophic forgetting in neural networks

Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611835114 · Cited by 7,899

Extracting and composing robust features with denoising autoencoders

Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436

Calibrating Noise to Sensitivity in Private Data Analysis

Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272

Robust estimation of a location parameter

Huber, P. J. (1964) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177703732 · Cited by 7,228

Federated Learning for Secure and Resilient AI Systems

Farooqi, S. A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2200-1.ch010 · Cited by 1

DP-CAKA: Defending Local Model Poisoning Attacks Based on Differential Privacy and Complex Acc-based multi-Krum Algorithm in Distributed Federated Learning

Huo, L., Wu, L., Feng, J., Fan, X., Wang, E., & Li, X. (2024) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc64274.2024.00188 · Cited by 1

Can Cross-Layer Intrusion Detection Secure Agriculture 4.0 Systems?

Ioannou, C., & Chrysostomou, C. (2025) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot65416.2025.00148 · Cited by 1

T-TNet: A dual-dependency trigger framework for active defense and hierarchical access control via multi-domain information fusion

Zhang, Y., Huang, J., Li, P., Zhang, Z., & Ding, C. (2025) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2025.103544 · Cited by 1

Dual-Model Backdoor Defense with Contrast Learning

Chen, Y., & Yin, J.-L. (2025) · International Conference on Big Data and Privacy Computing (BDPC) · doi:10.1109/bdpc63545.2025.11135804 · Cited by 1

SecurePoL: Integration of Watermarking With Proof-of-Learning to Enhance Security Against Spoofing Attacks

Ural, Ö., & Yoshigoe, K. (2025) · IEEE Access · doi:10.1109/access.2025.3642198 · Cited by 1

LLM Unlearning on Noisy Forget Sets: A Study of Incomplete, Rewritten, and Watermarked Data

Wang, C., Zhang, Y., Wei, D., Jia, J., Chen, P.-Y., & Liu, S. (2025) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3733799.3762973 · Cited by 1

PBA: Persistent backdoor attack on federated learning via distributed generative triggers

Yang, W., Wu, W., Liu, F., Ma, L., Zhang, Z., & Yang, B. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112038 · Cited by 1

UPGP:Backdoor defense via unlearning perturbation and orthogonality-constraint gradient projection

Li, J., Yuan, X., Tian, J., LU, S., & Yu, D. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113211 · Cited by 1

FFGSM: A Novel Fuzzy Logic-Based FGSM Attack for Deep Neural Networks Vulnerability Assessment

Liouane, O., & Messaoud, S. (2026) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2026.3681137 · Cited by 1

Provenance-based advanced persistent threat detection via holistic contrastive learning with heuristic augmentation

Qiu, X., Lv, M., Zhu, T., Song, Q., & Chen, T. (2026) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2026.104457 · Cited by 1

Prototype Guided Backdoor Defense via Activation Space Manipulation

Amula, V. A., Samavedam, S., Saini, S., Gupta, A., & Narayanan, P. J. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00212 · Cited by 2

Differentially private federated adversarial learning–based intrusion prevention for V2X communications

Sonker, S. K., Raina, V. K., Sagar, B. B., & Bansal, R. C. (2026) · Discover Computing · doi:10.1007/s10791-026-10398-1 · Cited by 1

Beta Poisoning Attacks Against Machine Learning Models: Extensions, Limitations and Defenses

Kara, A., Koprucu, N., & Gürsoy, M. E. (2022) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa56441.2022.00031 · Cited by 2

Image-Synthesis-Based Backdoor Attack Approach for Face Classification Task

Na, H., & Choi, D. (2023) · Electronics · doi:10.3390/electronics12214535 · Cited by 1

Method for testing NLP models with text adversarial examples

Менисов, А. Б., Lomako, A. G., & Sabirov, T. R. (2023) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2023-23-5-946-954 · Cited by 1

The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive Contamination

Canonne, C. L., Hopkins, S. B., Li, J. Z., Liu, A., & Narayanan, S. (2023) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs57990.2023.00133 · Cited by 1

Security of NVMe Offloaded Data in Large-Scale Machine Learning

Krauß, T., Götz, R., & Dmitrienko, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-51482-1_8 · Cited by 1

CTP: Defending Against Data Poisoning in Attack Traffic Detection Based Deep Neural Networks

Wang, L., Wang, X., Lv, Q., Wang, Y., Zhou, X., & Huang, W. (2023) · IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) · doi:10.1109/ic-nidc59918.2023.10390737 · Cited by 1

Analysis of Vulnerabilities of Neural Network Image Recognition Technologies

Trusov, A. V., Limonova, E. E., Arlazarov, V. V., & Zatsarinny, A. A. (2023) · Programming and Computer Software · doi:10.1134/s0361768823100079 · Cited by 1

Decoding HDF5: Machine Learning File Forensics and Data Injection

Walker, C., Baggili, I., & Wang, H. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-56580-9_12 · Cited by 1

Dye4AI: Assuring Data Boundary on Generative AI Services

Wang, S., Sun, K., & Zhai, Y. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3670299 · Cited by 1

Supvirus: A Scenario-Oriented Feature Poisoning Attack Approach in SplitFed Learning

Zhao, Y., Wu, G., Hou, J., & Li, X.-Y. (2023) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn60784.2023.00073 · Cited by 1

HiddenSpeaker: Generate Imperceptible Unlearnable Audios for Speaker Verification System

Zhang, Z., & Huang, P. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651155 · Cited by 1

Vulnerabilities of Artificial Intelligence Systems

Potapov, A. K., & Sidorenko, V. G. (2024) · International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT&QM&IS) · doi:10.1109/qmtisit63393.2024.10762915 · Cited by 1

Detectable Mislabeling – Can Faulty AI Models be Recognized from Incomplete Memory Traces?

Krzywiecki, Ł., Kulczycki, T., Nteranya, C. E., & Stós, A. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00329 · Cited by 1

Security and Privacy in AG-IoT-Enabled Unmanned Aerial Vehicles for Smart Cities

khan, Y., & Manzoor, S. (2025) · Apress eBooks · doi:10.1007/979-8-8688-1047-3_10 · Cited by 1

Prevention of Data Poisonous Threats on Machine Learning Models in e-Health

Alruwaili, E., & Moulahi, T. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3728369 · Cited by 1

Securing Visually-Aware Recommender Systems: An Adversarial Image Reconstruction and Detection Framework

Yin, M., Liu, B., Gong, N. Z., & Li, X. (2025) · ACM Transactions on Management Information Systems · doi:10.1145/3743681 · Cited by 1

GraphGuard: An adaptive approach for restoring accuracy in backdoor-compromised GNNs

Ahmad, A., Shah, A., Alnumay, W. S., & Ali, B. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107990 · Cited by 1

Indiscriminate Poisoning Attacks Are Shortcuts.Preprint

Yu, D., Zhang, H., Chen, W. R., Yin, J., & Liu, T. (2021) · arXiv (Cornell University) · Cited by 1

A Separation Result Between Data-oblivious and Data-aware Poisoning AttacksPreprint

Deng, S., Garg, S., Jha, S., Mahloujifar, S., Mahmoody, M., & Thakurta, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12020 · Cited by 2

AI Supply Chain Security: MBOM-PQC Provenance, PQC Attestation, and a Maturity Model for Quantum-Resistant Assurance

Campbell, R. (2026) · Systems · doi:10.3390/systems14050593 · Cited by 3

Protecting Language Models Against Unauthorized Distillation through Trace Rewriting

Ma, X., Yeoh, W., Zhang, N., & Vorobeychik, Y. (2026) · arXiv · doi:10.48550/arxiv.2602.15143

DISTIN螩T: Data poISoning atTacks dectectIon usiNg opt蟤ized jaCcard燿isTance

Sameen, M., & Hwang, S. O. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.031091

Training-time adversarial attacks

Chen, P., & Hsieh, C. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824020-5.00014-4

References

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A Pragmatic Label-Specific Backdoor Attack

Wang, Y., Yang, H., Li, J., & Ge, M. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-8445-7_10

A Compound Data Poisoning Technique with Significant Adversarial Effects on Transformer-based Text Classification TasksPreprint

Begoli, E., Mahbub, M., Sriniva, S., & Passarella, L. S. (2023) · Research Square · doi:10.21203/rs.3.rs-2443929/v1

Vulnerability and robustness in artificial intelligence for complex networks

Miller, B. A. (2023) · doi:10.17760/d20581983

Blockchain for AI: Smarter Contracts to Secure Artificial Intelligence Algorithms

Badruddoja, S. (2023) · doi:10.12794/metadc2179338

Joint Energy-Based Model for Robust Speech Classification System Against Dirty-Label Backdoor Poisoning Attacks

Sustek, M., Joshi, S., Li, H., Thebaud, T., Villalba, J., Khudanpur, S. P., & Dehak, N. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389697

Perceptrons Under Verifiable Random Data Corruption

Escamilla, J. E. A., & Diochnos, D. I. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53969-5_8

SL-Defender: Decentralized Defense for Adversarial Robustness in Federated Learning

Justine, C., Manamohan, S., & Jiji, C. V. (2023) · IEEE International Carnahan Conference on Security Technology (ICCST) · doi:10.1109/iccst59048.2023.10474244

Machine learning frameworks in IoT systems: A survey, case study, and future research directions

Chen, Z., Tian, P., Qian, C., Liao, W., Hussaini, A., & Yu, W. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13462-3.00015-7

Data Hybrid Attacks Based on Sensitive Users in Recommender Systems

Zhou, H., Gao, M., Chao, M., Song, Y., Xiong, Q., Wen, J., & Zhang, Y. (2023) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait60137.2023.10528484

Personalized Sleep Monitoring Using Smartphones and Semi-supervised Learning

Mammen, P. M., Zakaria, C., & Shenoy, P. J. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-59717-6_22

A Cgan-Based Adversarial Attack Method for Data-Driven State EstimationPreprint

Zhang, J., Wang, Q., & Hou, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4896159

Unveiling Robustness of Spiking Neural Networks against Data Poisoning Attacks

Yadav, S., Pundhir, A., Raman, B., & Kumar, S. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650573

Rethinking security: the resilience of shallow ML models

Teixeira, R., Antunes, M., Barraca, J. P., Gomes, D., & Aguiar, R. L. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00655-1

Learning from Distinction: Mitigating Backdoors Using a Low-Capacity Model

Sun, H., Li, Y., Lv, X., & Ma, J. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680968

Consensus algorithm for maintaining large-scale access-control views of education data

Wu, X., Meng, T., Zhang, J., Yang, Q., & Chen, J. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06625-5

Evaluating Language Model Vulnerability to Poisoning Attacks in Low-Resource Settings

Plant, R., Giuffrida, M. V., Pitropakis, N., & Gkatzia, D. (2024) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3507565 · Cited by 1

Phoneme Semantic Backdoor Attacks with Multiple Task Learning for Speech Classification Task

Ye, X., Yao, W., Li, Z., Yang, J., & Wen, W. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-1045-7_7

A Mini-model can Make Machine Unlearning Better

Zhao, M., Yu, W., & Zhu, C. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-1525-4_8

Restoring Unintended Model Learning by Error Correcting Code

Ashizawa, N., Shibahara, T., Kiribuchi, N., Saisho, O., & Yanai, N. (2024) · Annual Computer Security Applications Conference Workshops (ACSAC Workshops) · doi:10.1109/acsacw65225.2024.00013

Mitigating Iterative Fast Gradient Adversarial Poisoning in Archaeological Image Datasets using Generative Adversarial Networks and Denoising Autoencoders

Bai, K. V. S., Pradeep, K., Harini, G. V. N. L., & Reddy, N. Y. (2025) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears64219.2025.10940731

RTS: A Training-time Backdoor Defense Strategy Based on Weight Residual Tendency

Fan, X., Li, X., & Meng, G. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00119

TE-PADN: A poisoning attack defense model based on temporal margin samples

He, H., Liu, K., Zhang, L., Xu, K., Li, J., & Ren, J. (2025) · Big Data Research · doi:10.1016/j.bdr.2025.100528 · Cited by 2

MCL++ : Strengthening Defenses Against Backdoor Poisoning Attacks

Shymala Gowri, S., & Sadasivam, G. S. (2025) · Cybernetics & Systems · doi:10.1080/01969722.2025.2483718

Data Optimization for LLMs: A Survey

Wu, O. (2026) · ACM Transactions on Knowledge Discovery from Data · doi:10.36227/techrxiv.174776562.20873028/v1

Adversarial Landscapes: A Comprehensive Analysis of Vulnerabilities, Attacks, and Red Teaming in Machine Learning and Large Language Models

Mettu, B. P. R. (2025) · Technix International Journal for Engineering Research · doi:10.56975/tijer.v12i6.158478

Proof of Training: Obtaining Verifiable ML Models by Delegating Training to a Blockchain Network

Krzywiecki, Ł., & Wechta, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97629-2_15

Online Reward Poisoning in Reinforcement Learning With Convergence Guarantee

Li, J., Zhang, B., Niu, Y., Wu, S., Ding, K., & Wu, J. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3595415

New Construction Method for Weightwise Perfectly Balanced Boolean Functions

Wang, F., & Zhao, K. (2025) · doi:10.1109/icnlp65360.2025.11108582

DMTL : An Adaptive Integrity Verification Scheme for Dynamic Cloud Datasets

He, X., & Zhou, Q. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70264

Backdoor Attacks on DNN and GBDT: A Case Study From the Insurance Domain

Kühlem, R., Otten, D., Ludwig, D., Hudde, A., Rosenbaum, A., & Mauthe, A. (2025) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.70029

Data Poisoning Attack Detection Based On Reconstruction

Xu, W., Liang, Z., Yang, Y., Zhang, X., Jiang, Y., Hong, C., Xu, P., Zhang, Y., & Bi, L. (2025) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccc65529.2025.11148722

Manipulating Digital Twin Networks by Poisoning Semantic Knowledge Base

Han, B., He, Y., Xu, R., Xiao, D., Ruan, N., & Li, J. (2025) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops67674.2025.11162402

Industrial large models as carriers for malicious payloads: A fast and robust approach

Yuan, Y. P., Deng, R., Lu, Z., & Hung, P. C. K. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113967

Adversarial Attacks on Machine Learning Models in Cybersecurity

Ahmed, S., Bamadhaf, M. A., & Mehmood, A. (2025) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa65407.2025.11166409

MACTDiffusion: Multi-Agent Chaotic Trigger-Controlled Backdoor Attack Framework for Diffusion ModelsPreprint

Bikkina, L. B., & Maddumala, V. R. (2025) · Research Square · doi:10.21203/rs.3.rs-7585237/v1

Making Audio Data Unlearnable

Fan, W., Li, H., Jiang, W., & Deng, H. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161689

Chatbots integrated in the metaverse: a conceptual model based on interaction ritual chain theory

Yan, Y., & Fan, M. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3235

Exploring Recommendations Attacks Through Blind Optimization

Corona, J., Teixeira, R., Antunes, M., & Aguiar, R. L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05461-6_38

Responsible Machine Learning via Mixed-Integer Optimization

Justin, N., Sun, Q., Gómez, A., & Vayanos, P. (2025) · doi:10.1287/educ.2025.0292 · Cited by 3

Towards Building Robust, Reliable and Private AI/ML Security Solutions in Open Radio Access Networks

Chatzimiltis, S., Shojafar, M., Mashhadi, M. B., & Tafazolli, R. (2025) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc67675.2025.00157

SPBA: Utilizing Speech Large Language Model for Backdoor Attacks on Speech Classification Models

Yao, W., Xiao, F., Chen, X., Liu, J., He, Y., & Wen, W. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228852

A Practical and Secure Byzantine Robust Aggregator

Lee, D. Z., Kolluri, A., Saxena, P., & Chang, E. (2025) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3719027.3744851 · Cited by 2

Attack Information Spectrum: A Taxonomy of Adversarial Attack Methods for Images

Jin, Y. (2025) · IEEE Conference on Dependable and Secure Computing (DSC) · doi:10.1109/dsc65356.2025.11260868

Data Poisoning Offensive and Defensive System in FL-Enabled Low-Altitude IoT Scenario: Unlearnable Example Attack and Model Similarity Defend

Wang, H., Ouyang, W., Ren, X., & Lv, W. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3640212

A case study on mirror- and twin-based bypass of smartphone face unlock systems

López, J.-L. C., Muñoz, S. R., Ordoñez, E. L., & Torres, D. D. S. L. (2025) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit67332.2025.11267881

Characterizing Cross-Contamination on Multitask Multimodal Large Language Models

Ibañez-Lissen, L., González‐Manzano, L., Fuentes, J. M. D., & García-Alfaro, J. (2025) · Information Systems Frontiers · doi:10.1007/s10796-025-10656-5

Enhancing Security in DAWN-MANET for Disaster Management: Challenges and Solutions

Namee, K., Choorod, P., Sritha, K. N., Meny, A. H., Kaewsaeng‐on, R., & Polpinij, J. (2025) · Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C) · doi:10.1109/ri2c67120.2025.11282760

Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models

Wang, Z., Zhang, J., Shan, S., & Chen, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3644016 · Cited by 8

Smart Mobility and Coverage in Ghats: Terrain-Aware Routing and Cost-Optimized MCDS Deployment

Jambur, P. V., Suma, B. J., Choudhari, M. M., & Roodagi, P. (2025) · International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) · doi:10.1109/csitss67709.2025.11294762

IBSD: Iterable Black-Box Self-Defense Against Backdoor Attacks

Wu, Z., Wen, J., Peng, W., Zhou, Y., & Zhang, Z. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3617292

DSBP: Data-Free and Swift Backdoor Purification for Trustworthy Federated Learning via Multi-Teacher Adversarial Distillation

Li, G.-L., Wu, J., Li, J.-H., Zhao, Y.-Y., Lian, H.-J., & Zheng, L.-F. (2025) · Journal of Computer Science and Technology · doi:10.1007/s11390-025-4366-7

Investigating the Impact of Noise Injection on BERT for Offensive Arabic Text Detection

Abdulgawad, K. E. A., & Saud, N. B. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-00232-7_43

İç Kontrol ve Risk Yönetimi

Özcan, M., & Günlük, M. (2025) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1159.c4843

Understanding and Mitigating Data Poisoning in LLMs

Shah, A., Mathur, I., & Kanakia, H. T. (2025) · International Conference on Responsible, Generative and Explainable AI (ResGenXAI) · doi:10.1109/resgenxai64788.2025.11344010

ATSTA: Efficient Audio Backdoor Attack Based on Alternating Training With Limited Knowledge

Wang, B., Yan, Y., Zhang, M., & Wang, W. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3650252

When Reputation Meets Auction: A Dual Incentive Mechanism for Mitigating Clean Label Attack in Data Trading Markets

Li, Q., Zhuansun, C., Lin, Y., Yang, Q., Li, P., Liu, Y., & Tian, Z. (2025) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc67331.2025.00056

Application of Generative Artificial Intelligence in Transport and Road Complex Control Systems

Ostroukh, A. V., Kuftinova, N. G., Александрович, П. А., Pronin, C. B., & Makeev, D. M. (2025) · Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED) · doi:10.1109/tirved67702.2025.11367204

Improving smart contract security with transformer-based anomaly detection

Gu, T., Han, M., He, S., Chen, X., & Wang, Z. (2026) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2026.104451

Enhanced Contactless Palmprint Backdoor Attack with Invisible Sample-Specific Triggers

Chen, Y., Zhao, S., Fei, L., Chai, T., Cui, J., & Lai, Q. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-6123-0_6

dataRLsec: Safety, Security, and Reliability With Robust Offline Reinforcement Learning for DPAs

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