Solar insecticidal lamps (SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things (IoT) has formed a new type of agricultural IoT, known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues. These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL, etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability, and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

While side-channel attacks (SCAs) have become a significant threat to cryptographic algorithms, masking is considered as an effective countermeasure against SCAs. On the one hand, securely implementing the scheme is a challenging and error-prone task. It is essential to detect leakage in a complicated cryptographic circuit. However, the traditional method of leakage detection is always inaccuracy or time consumption. On the other hand, the deep learning-based power attacks have shown their threat to the masking without combining functions. Compared to the leakage detection done under the traditional provable security framework, the security evaluation against deep learning-based attacks at the pre-silicon stage has not been discussed. To this end, this paper investigates the strategies of leveraging the deep learning techniques to achieve an efficient leakage location method. In this paper, we present the first approach utilizing deep learning-based leakage location for both unprotected and protected implementations at the pre-silicon stage. Firstly, we propose the leakage location method named Gradient Visualization-based location (GVL), which provides leakage location at the different levels of design. Gradient visualization is known as a sensitivity analysis method to understand better how a natural network can learn to predict the sensitive label based on the input. We theoretically show how the gradient visualization can be used to locate leakage components in the netlist efficiently. Moreover, we link the result with the metric in deep learning-based leakage assessment, which fills the lack of leakage evaluation at the pre-silicon stage against deep learning-based SCAs. We further confirm the effectiveness of the proposed method on unprotected implementation, low entropy masked implementation, and provable secure masked implementation. The results show that the proposed methodology outperforms the traditional location methods in the masked cases, where the time consumption is reduced by about 2x to 10x with fewer false negatives and no false positives.

The widely deployed devices in Internet of Things (IoT) have opened up a large amount of IoT data. Recently, federated learning emerges as a promising solution aiming to protect user privacy on IoT devices by training a globally shared model. However, the devices in the complex IoT environments pose great challenge to federate learning, which is vulnerable to gradient-based reconstruction attacks. In this paper, we discuss the relationships between the security of federated learning model and optimization technologies of decreasing communication overhead comprehensively. To promote the efficiency and security, we propose a defence strategy of federated learning which is suitable to resource-constrained IoT devices. The adaptive communication strategy is to adjust the frequency and parameter compression by analysing the training loss to ensure the security of the model. The experiments show the efficiency of our proposed method to decrease communication overhead, while preventing privacy data leakage.