Towards Detecting IoT Event Spoofing Attacks Using Time-Series Classification

TL;DR

This paper presents a time-series classification approach using advanced machine learning to detect IoT event spoofing attacks, demonstrating high accuracy with significantly less training data than previous methods.

Contribution

The work introduces a novel time-series-based detection method that learns temporal features faster and with less data, addressing dataset size limitations in IoT security.

Findings

Faster learning of temporal features compared to previous methods

Effective detection with 100- to 500-fold smaller training datasets

Demonstrated on real-world IoT sensor data

Abstract

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.