# Attacks on IoT: Side-Channel Power Acquisition Framework for Intrusion Detection
## 3 Materials and Methods  
Two separate IoT devices were chosen for this testbed: a Raspberry Pi 3 Model B [34] and a DragonBoard 410 [35]. Based on the Qualcomm Snapdragon 410 processor [36] and manufactured by Arrow Electronics, the DragonBoard 410c comes equipped with advanced processing power, Bluetooth and Wi-Fi. The Snapdragon 410 was an extremely popular processor of which Qualcomm shipped over 200 million units, with a large amount of integration with the mobile-phone market in the mid-2010s. With over 200 million units shipped [37], any signatures obtained for the side-channel power data of this chip can be considered highly valuable. The Raspberry Pi 3 Model B was chosen for a similar reason to the Snapdragon-based system, its extreme popularity. The Raspberry Pi is one of the most popular single-board computers on the market today. Indeed, since the launch of its first version over ten years ago, the Raspberry Pi has sold over 40 million units [38].  
The DragonBoard and the Raspberry Pi feature Quad Core 1.2 GHz 64-bit CPUs and 1.2 GB of RAM. In the case of the DragonBoard, this quad-core CPU is integrated within the Snapdragon 410 along with other components, such as a Qualcomm APQ8016E application processor and a Qualcomm Adreno 306 graphics chip. Regardless of their different manufacturers, manufacturing periods and usual use cases, both systems have startlingly similar specifications. These choices in the device will give a broader understanding of what common attack signatures look like, when viewed in the device current across different devices. Part of this study investigates whether common attacks' signatures are general. While an exact match in the signature cannot be expected due to the different hardware specifications, any generalisation in signatures could greatly assist in understanding and detecting these attacks against IoT devices and networks comprised of various devices. With approximately 245 million devices (combined, between the DragonBoard 410c and the Raspberry Pi), extensive coverage of devices can be assumed during this study. The testbed was kept as simple as possible to maximise explainability while maintaining realism.  
Very little functionality was enabled on each board. The Raspberry Pi, running PI OS [39], came with crucial features, such as Bluetooth, installed. These features needed to be installed on the DragonBoard, on which Linaro Debian [40], a specifically designed Debian distribution for the Qualcomm Snapdragon 410, was installed.  
Both boards were connected to the internet over Wi-Fi, and the SSH protocol was enabled to broaden the attack surface. SSH [41] or \"Secure Shell\" is a network protocol allowing users to communicate between computers, share data and remotely log into other devices. SSH would regularly be enabled on IoT devices for maintenance purposes, and, in some cases, normal operating behaviour would require users to log in via SSH to view sensor data or interact with components. Recent research has revealed that 15% of IoT device owners do not change the default passwords. As a result, five passwords can give an attacker access to 10% of all IoT devices worldwide [42]. With an estimated 55 billion IoT devices installed worldwide by 2025, this number of devices with default credentials will be over 5 billion [15].  
Bluetooth was enabled on each board to communicate with a TI CC2650 STK Sensortag [43]. These Sensortags come equipped with ten low-power MEMS sensors. A simple Python script [44] running on the testbed requested the sensor data over Bluetooth and interpreted the packets received once every 5 s. The Dragonboard and the Raspberry Pi were connected to an Agilent technologies N6705A DC Power Analyser [45]. This device supplied the power to the testbed while also collecting side-channel device current data. This method allows for the non-intrusive monitoring of the side-channel IoT power behaviour. Figure 1 describes a proposal for a generic data acquisition framework for IoT.