The Internet of Things: Secure Distributed Inference

Abstract

The growth in the number of devices connected to the Internet of Things (IoT) poses major challenges in security. The integrity and trustworthiness of data and data analytics are increasingly important concerns in IoT applications. These are compounded by the highly distributed nature of IoT devices, making it infeasible to prevent attacks and intrusions on all data sources. Adversaries may hijack devices and compromise their data. As a result, reactive countermeasures, such as intrusion detection and resilient analytics, become vital components of security. This paper overviews algorithms for secure distributed inference in IoT.

References in corpus (6)

• Secure estimation and control for cyber-physical systems under adversarial attacks
• Distributed Bayesian Detection with Byzantine Data
• Resilient Distributed Estimation Through Adversary Detection
• Consensus based Detection in the Presence of Data Falsification Attacks
• Quantized Consensus by the ADMM: Probabilistic versus Deterministic Quantizers
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Cited by in corpus (9)

• Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks
• Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning: An Overview of Recent Advances Under the Byzantine Threat Model
• Resilient Distributed Parameter Estimation with Heterogeneous Data
• Event-triggered Approximate Byzantine Consensus with Multi-hop Communication
• BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify Communication-Efficient Federated Learning
• Robust Distributed Optimization With Randomly Corrupted Gradients
• Resilient Distributed Recovery of Large Fields
• Resilient Distributed Field Estimation
• Resilient Consensus Against Mobile Malicious Agents