# Attacks on IoT: Side-Channel Power Acquisition Framework for Intrusion Detection
## 2 Related Works  
With the massive explosion in the deployment of IoT devices globally, the security of these devices has become a critical concern. IoT devices are resource-constrained by nature. This means conventional security practices tend to be impossible, or impractical, to implement on these devices. The majority of the work focusing on IoT devices in security only considers the network traffic to and from the device. By inspecting this data, it is possible to infer if a device is under attack or has been attacked. Much research has been conducted in the field of Intrusion Detection Systems (IDS) for IoT devices. These IDS can either be network-based (NIDS), which are usually located on the IoT gateway, or host-based (HIDS), which are implemented on the device itself. To combat the limited security most IoT devices offer, Passban was created [20]. Passban is an intelligent IDS which can protect IoT devices to which it is directly connected. Passban is a lightweight solution which can be deployed on cheap, resource-constrained IoT gateways as a HIDS. Trained on the normal behaviour of a device, Passban can detect a wide range of malicious traffic such as brute-force, SYN flood and port-scanning attacks. Passban was designed with scalability in mind, meaning this framework can dynamically scale to new threat definitions without requiring hardware upgrades. In [21], a deep recurrent-neural-network-based IDS for fog security is developed. Fog computing extends cloud services nearer to IoT devices. It acts as a medium between traditional cloud computing and edge devices, such as the IoT. The proposed framework, implemented in the fog-computations layer, comprises a traffic processing engine and a classification engine consisting of a recurrent artificial neural network (ANN). The proposed framework is trained and evaluated using a balanced version of the NSL-KDD [22] dataset and shows high accuracies of 98.27% against denial-of-service attacks, one of the more pervasive attacks against IoT devices. In [23], a convolutional-neural-network (CNN)-based anomaly-detection IDS framework for IoT is proposed. This framework takes advantage of the strengths of IoT devices and can examine traffic across the broad scope of the IoT. The proposed model can detect a wide range of intrusions and anomalous traffic behaviour and was trained on the BotIoT [24] and NID [25] datasets, achieving high accuracies of 92.85% and 99.51%, respectively. This work also presents a framework to incorporate IDS as a program within IoT networks and a strategy to preserve the integrity of IoT networks while seamlessly maintaining availability for legitimate users. In our previous work, we introduced HH-NIDS [26]--a Heterogeneous Hardware-Based Network IDS framework for IoT security. Using hardware accelerators, HH-NIDS implements anomaly-based IDS approaches for IoT devices. Supervised-learning methodologies on the IoT-23 [27] and UNSW-NB15 [28] datasets were trained to generate lightweight ANN models for anomaly detection, achieving high accuracies of 99.66% and 98.57% for these datasets, respectively. These models were evaluated from a performance and resource-usage perspective on the CPU, GPU and FPGA and implemented on the MAXIM 78000 microcontroller. A common theme amongst these works is that they focus on the network traffic of the IoT device to detect malicious activity. One key security area that has been largely overlooked is the use of power data. The side-channel power data of a device is a fundamental primary source of data which every device, regardless of computing resources, has. The following works focus more on using power data in security. The volume of works such as these is insufficient, and we argue that much more research must be performed to integrate power data into security. Earlier works proposed the use of side-channel power data as an attack on the device itself. Work by [PERSON] et al.