As an innovative strategy, edge computing has been considered a viable option to address the limitations of cloud computing in supporting the Internet-of-Things applications. However, due to the instability of the network and the increase of the attack surfaces, the security in edge-assisted IoT needs to be better guaranteed. In this paper, we propose an intelligent intrusion detection mechanism, FedACNN, which completes the intrusion detection task by assisting the deep learning model CNN through the federated learning mechanism. In order to alleviate the communication delay limit of federal learning, we innovatively integrate the attention mechanism, and the FedACNN can achieve ideal accuracy with a 50% reduction of communication rounds.

With the rapid development of WLAN technology, wireless device-free passive human detection becomes a newly-developing technique and holds more potential to worldwide and ubiquitous smart applications. Recently, indoor fine-grained device-free passive human motion detection based on the PHY layer information is rapidly developed. Previous wireless device-free passive human detection systems either rely on deploying specialized systems with dense transmitter-receiver links or elaborate off-line training process, which blocks rapid deployment and weakens system robustness. In the paper, we explore to research a novel fine-grained real-time calibration-free device-free passive human motion via physical layer information, which is independent of indoor scenarios and needs no prior-calibration and normal profile. We investigate sensitivities of amplitude and phase to human motion, and discover that phase feature is more sensitive to human motion, especially to slow human motion. Aiming at lightweight and robust device-free passive human motion detection, we develop two novel and practical schemes: short-term averaged variance ratio (SVR) and long-term averaged variance ratio (LVR). We realize system design with commercial WiFi devices and evaluate it in typical multipath-rich indoor scenarios. As demonstrated in the experiments, our approach can achieve a high detection rate and low false positive rate.

Backdoors on federated learning will be diluted by subsequent benign updates. This is reflected in the significant reduction of attack success rate as iterations increase, ultimately failing. We use a new metric to quantify the degree of this weakened backdoor effect, called attack persistence. Given that research to improve this performance has not been widely noted, we propose a Full Combination Backdoor Attack (FCBA) method. It aggregates more combined trigger information for a more complete backdoor pattern in the global model. Trained backdoored global model is more resilient to benign updates, leading to a higher attack success rate on the test set. We test on three datasets and evaluate with two models across various settings. FCBA's persistence outperforms SOTA federated learning backdoor attacks. On GTSRB, post-attack 120 rounds, our attack success rate rose over 50% from baseline. The core code of our method is available at https://github.com/PhD-TaoLiu/FCBA.

Device-free passive identity identification attracts much attention in recent years, and it is a representative application in sensorless sensing. It can be used in many applications such as intrusion detection and smart building. Previous studies show the sensing potential of WiFi signals in a device-free passive manner. It is confirmed that human’s gait is unique from each other similar to fingerprint and iris. However, the identification accuracy of existing approaches is not satisfactory in practice. In this paper, we present Wii, a device-free WiFi-based Identity Identification approach utilizing human’s gait based on Channel State Information (CSI) of WiFi signals. Principle Component Analysis (PCA) and low pass filter are applied to remove the noises in the signals. We then extract several entities’ gait features from both time and frequency domain, and select the most effective features according to information gain. Based on these features, Wii realizes stranger recognition through Gaussian Mixture Model (GMM) and identity identification through a Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. It is implemented using commercial WiFi devices and evaluated on a dataset with more than 1500 gait instances collected from eight subjects walking in a room. The results indicate that Wii can effectively recognize strangers and can achieves high identification accuracy with low computational cost. As a result, Wii has the potential to work in typical home security systems.

WiFi infrastructures are widely deployed in both public and private buildings. They make the connection to the internet more convenient. Recently, researchers find that WiFi signals have the ability to sense the changes in the environment that can detect human motion and even identify human activities and his identity in a device-free manner, and has many potential security applications in a smart home. Previous human detection systems can only detect human motion of regular moving patterns. However, they may have a significant detection performance degradation when used in intrusion detection. In this study, we propose Robust Device-Free Intrusion Detection (RDFID) system leveraging fine-grained Channel State Information (CSI). The noises in the signals are removed by a Principle Component Analysis (PCA) and a low pass filter. We extract a robust feature of frequency domain utilizing Continuous Wavelet Transform (CWT) from all subcarriers. RDFID captures the changes from the whole wireless channel, and a threshold is obtained self-adaptively, which is calibration-free in different environments, and can be deployed in smart home scenarios. We implement RDFID using commodity WiFi devices and evaluate it in three typical office rooms with different moving patterns. The results show that our system can accurately detect intrusion of different moving patterns and different environments without re-calibration.

With the development of intelligent technologies, Internet of Things (IoT) opens up a new era in the field of automotive networks, namely, Internet of Vehicles (IoV). The main goal of IoV is to provide a secure and reliable network to vehicles so that users can enjoy various services. However, vulnerabilities and incomplete protection mechanisms have led to a proliferation of security threats against IoV networks. Intrusion detection technology is an effective protection solution for IoV security, especially when artificial intelligence (AI) technology has been introduced into intrusion detection study. This article first briefly introduces the concept and features of IoV, and then reviews the related research on AI-based IoV intrusion detection systems. Finally, we discuss the open challenges and future research directions.