Embedded Systems in IoT: Rising Threat of Side-Channel Attacks
London, Monday, 11 August 2025.
The growing field of Internet of Things exposes embedded systems to side-channel attacks, exploiting signal leakages for extracting sensitive data, necessitating robust security measures.
Understanding Side-Channel Attacks on Embedded Systems
Side-channel attacks (SCAs) on embedded systems in the Internet of Things (IoT) pose significant risks. These attacks exploit unintentional physical emissions, such as power or electromagnetic leakages, to extract sensitive data from devices [1]. The vulnerability is particularly concerning as these attacks can compromise the cryptographic keys and confidential information of IoT devices, such as asymmetric secret keys [1].
Mitigation Strategies and Their Effectiveness
To counteract these vulnerabilities, various mitigation strategies have been proposed. Techniques like constant-time algorithms and masking are prominent in protecting devices from power analysis and timing attacks [2]. However, while these methods can be effective in academic settings, they often introduce performance overheads that are not feasible for resource-constrained IoT devices [1][2].
Case Study: Industrial IoT Vulnerabilities
A recent case study in the industrial IoT sector illustrates the practical challenges of implementing robust security measures against side-channel attacks. These devices, often deployed in critical operations, must balance security with cost and performance. The study revealed that even when utilizing the latest profiling techniques leveraging deep learning, resource limitations often expose devices to side-channel attacks [2].
Advancements in Deep Learning Techniques
Recent advancements in deep learning have significantly enhanced the capabilities of profiling side-channel attacks. By utilizing algorithms that adapt to the lightweight cryptographic primitives like the SPECK cipher, these attacks can successfully extract cryptographic keys with minimal traces [3]. The development of ensemble deep learning models has further optimized attack efficiency, reducing the number of traces needed to breach security measures significantly [3].
Future Considerations and Recommendations
As the prevalence of IoT devices continues to grow, developing scalable security measures with minimal performance impact is crucial. Future research should focus on enhancing profiling methods to work efficiently even on protected implementations and exploring advanced cryptographic protections, such as hardware-based security features or noise injection, to guard against these sophisticated side-channel attacks[3][4].