Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead

Currently, machine learning (ML) techniques are at the heart of smart cyber-physical systems (CPS) and Internet-of-Things (IoT). This article discusses various challenges and probable solutions for security attacks on these ML-inspired hardware and software techniques. —Partha Pratim Pande, Washington State University

cs.CR cs.AI cs.AR cs.LG eess.SY Computer Science Engineering

Paper

Similar papers

Robust Machine Learning Systems: Challenges,Current Trends, Perspectives, and the Road AheadMuhammad Shafique, Mahum Naseer, Theocharis Theocharides et al.2020 · IEEE Design and Test In Library

Safety, Security and Privacy in Machine Learning Based Internet of ThingsGhulam Abbas, Amjad Mehmood, C. Maple et al.2022 · J. Sens. Actuator Networks · 58 citations In Library

Machine Learning-Based Security Solutions for Critical Cyber-Physical SystemsAsad Raza, S. Memon, M. A. Nizamani et al.2022 · International Symposium on Digital Forensics and Security · 12 citations In Library

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPSFelix O. Olowononi, D. Rawat, Chunmei Liu2021 · IEEE Communications Surveys and Tutorials · 182 citations In Library

Security and privacy-preserving for machine learning models: attacks, countermeasures, and future directionsFatema EL-Husseini, Hassan Noura, Flavien Vernier2025 · Annals of Telecommunications · 4 citations In Library

Machine Learning for Security and the Internet of Things: The Good, the Bad, and the UglyFan Liang, W. G. Hatcher, Weixian Liao et al.2019 · IEEE Access · 116 citations In Library

Roadmap of Adversarial Machine Learning in Internet of Things-Enabled Security SystemsYasmine Harbi, Khedidja Medani, Chirihane Gherbi et al.2024 · Italian National Conference on Sensors · 10 citations In Library

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent ThreatsZhiyan Chen, Jinxin Liu, Yuling Shen et al.2022 · ACM Computing Surveys · 127 citations In Library

Secure Machine Learning Architectures for Edge-Based IoT Systems: Threat Models, Vulnerabilities, and Mitigation StrategiesEvans Addo2026 · International Journal For Multidisciplinary Research In Library

Hardware-assisted Machine Learning in Resource-constrained IoT Environments for Security: Review and Future ProspectiveG. Kornaros2022 · IEEE Access · 70 citations In Library

Source of record

arXiv

44B

Welcome.

This panel sticks with you. Pick anything below and it opens right beside it, so you can dig through 60,000-plus records without ever losing your spot here.

Travel 44B

LibraryPapers, policy, standards, statute LabsEvery organization building AI ModelsIntelligence, price, and speed BenchmarksThe evaluation catalog IncidentsAI failures and harms SearchOne field across everything DashboardThe state of AI accountability in NY 44B RegistryThe Art. 44-B compliance portal