# Attacks on IoT: Side-Channel Power Acquisition Framework for Intrusion Detection
## 2 Related Works
[29], presented in 1999, examines specific methods for analysing power consumption measurements to uncover secret keys from tamper-resistant devices. These methods, dubbed \"Simple Power Analysis\" and \"Differential Power Analysis\", broke DES encryption, thereby allowing attackers to discern private keys. They also discuss approaches for building cryptosystems which securely operate in insecure hardware that leaks information. Instead of using the side-channel power data as an attack, other works have been completed which monitor the device power data to detect intrusions. These works are very scarce, however. Some notable examples are listed below. In our other previous work [30], a lightweight convolutional neural network (CNN) Host-Based IDS (HIDS) for anomaly detection in the power data of an IoT device was created. This CNN framework was implemented on the MAX78000 microcontroller on the edge. The proposed framework is scalable and generic, making it relevant for any device. However, a fundamental limitation of that work was the absence of actual attack examples in the power data. As such, synthetic anomalies were relied on. WattsUpDoc [31] utilises the side-channel power consumption of medical devices to allow for run-time malware detection. During experimentation, WattsUpDoc performedwith an accuracy of 94%, when presented with previously known malware examples, and 85% accuracy regarding unseen malware examples on multiple embedded devices. This framework's non-intrusive methodology, which monitors the side-channel power data from the device, allows for the detection of malware with no software, hardware or network modification requirements of the existing system in place. DeepPower [32] is another approach which detects malware on IoT devices by analysing their non-intrusive side-channel power signals. This framework utilises deep learning to detect anomalies in the power data. DeepPower initially filters the raw side-channel power data to find suspect power traces. A fine-grained analysis is then performed on these traces to determine which activities they correspond to on the device. The DeepPower framework can detect malicious activity with high accuracy while maintaining a non-intrusive nature, meaning no modifications need to be made to the monitored devices. In a departure from the theme of IoT devices, the work presented in \"Catch Me if You Can\" [33] demonstrates how the side-channel power data obtained from High-Powered Computing Platforms (HPCs) can be used to determine what programs are running on a machine and, thus, if any un-authorised programs are running. Using a variety of scientific benchmarks, the proposed framework was tested on an HPC rack at Lawrence Berkeley National Laboratory. This framework can detect if specific programs are running with a recall of up to 95% and a precision of 97%. This work is essential, as it illustrates that using side-channel power data is not simply confined to the IoT field but applies to the entire security sector. This study proposes a data acquisition framework for non-intrusive side-channel power data for IDS to detect attacks on edge devices. To this end, we create a testbed and subject it to common cyber attacks. The data is then collected and analysed. A unique capture-the-flag scenario is subsequently enacted, which combines the individually studied cyber attacks into a single scenario in which the testbeds are compromised, and a secret file is stolen. One fundamental area for improvement with all the side-channel power-related works presented here is that there needs to be an in-depth explanation of the power signatures they are studying. We seek to demonstrate that a broader understanding of common attack signatures in the power spectrum is crucial. The insight and intuitions gained from analysing the side-channel power data are valuable and should provide insight into how the IoT and other fields can implement more effective IDS frameworks.