2024/07/29 by Uzma Maroof, Maroof, Uzma, Gustavo Batista +5
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Network Security and Intrusion Detection #Smart Grid Security and Resilience
paper · pdf · doi:10.48550/arxiv.2407.19662
openalex publication_date 2024/07/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Internet of Things (IoT) devices have grown in popularity since they can directly interact with the real world. Home automation systems automate these interactions. IoT events are crucial to these systems' decision-making but are often unreliable. Security vulnerabilities allow attackers to impersonate events. Using statistical machine learning, IoT event fingerprints from deployed sensors have been used to detect spoofed events. Multivariate temporal data from these sensors has structural and temporal properties that statistical machine learning cannot learn. These schemes' accuracy depends on the knowledge base; the larger, the more accurate. However, the lack of huge datasets with enough samples of each IoT event in the nascent field of IoT can be a bottleneck. In this work, we deployed advanced machine learning to detect event-spoofing assaults. The temporal nature of sensor data lets us discover important patterns with fewer events. Our rigorous investigation of a publicly available real-world dataset indicates that our time-series-based solution technique learns temporal features from sensor data faster than earlier work, even with a 100- or 500-fold smaller training sample, making it a realistic IoT solution.