Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network. Though FL enables a privacy-preserving mobile edge computing framework using IoT devices, recent studies have shown that this approach is susceptible to poisoning attacks from the side of remote clients. To address the poisoning attacks on FL, we provide a two-phase defense algorithm called Local Malicious Factor (LoMar). In phase I, LoMar scores model updates from each remote client by measuring the relative distribution over their neighbors using a kernel density estimation method. In phase II, an optimal threshold is approximated to distinguish malicious and clean updates from a statistical perspective. Comprehensive experiments on four real-world datasets have been conducted, and the experimental results show that our defense strategy can effectively protect the FL system. Specifically, the defense performance on Amazon dataset under a label-flipping attack indicates that, comparing with FG+Krum, LoMar increases the target label testing accuracy from $96.0\%$ to $98.8\%$ , and the overall averaged testing accuracy from 90.1% to $97.0%.

Deepfake technology, which employs advanced generative artificial intelligence to create hyper-realistic synthetic media, poses significant challenges across various sectors, including security, entertainment, and education. This literature review explores the evolution of deepfake generation methods, ranging from traditional techniques to state-of-the-art models such as generative adversarial networks and diffusion models. We navigate through the effectiveness and limitations of various detection approaches, including machine learning, forensic analysis, and hybrid techniques, while highlighting the critical importance of interpretability and real-time performance in detection systems. Furthermore, we discuss the ethical implications and regulatory considerations surrounding deepfake technology, emphasizing the need for comprehensive frameworks to mitigate risks associated with misinformation and manipulation. Through a systematic review of the existing literature, our aim is to identify research gaps and future directions for the development of robust, adaptable detection systems that can keep pace with rapid advancements in deepfake generation.

Due to the open nature of wireless medium, wireless communications are especially vulnerable to eavesdropping attacks. This paper designs a new wireless communication system to deal with eavesdropping attacks. The proposed system can enable a legitimate receiver to get desired messages and meanwhile an eavesdropper to hear ``fake" but meaningful messages by combining confidentiality and deception, thereby confusing the eavesdropper and achieving additional concealment that further protects exchanged messages. Towards this goal, we propose techniques that can conceal exchanged messages by utilizing wireless channel characteristics between the transmitter and the receiver, as well as techniques that can attract an eavesdropper to gradually approach a trap region, where the eavesdropper can get fake messages. We also provide both theoretical and empirical analysis of the established secure channel between the transmitter and the receiver. We develop a prototype system using Universal Software Defined Radio Peripherals (USRPs)Experimental results show that an eavesdropper at a trap location can receive fake information with a bit error rate (BER) close to 0, and the transmitter with multiple antennas can successfully deploy a trap area.

We revisit the traditional framework of wireless secret key generation, where two parties leverage the wireless channel randomness to establish a secret key. The essence in the framework is to quantify channel randomness into bit sequences for key generation. Conducting randomness tests on such bit sequences has been a common practice to provide the confidence to validate whether they are random. Interestingly, despite different settings in the tests, existing studies interpret the results the same: passing tests means that the bit sequences are indeed random. In this paper, we investigate how to properly test the wireless channel randomness to ensure enough security strength and key generation efficiency. In particular, we define an adversary model that leverages the imperfect randomness of the wireless channel to search the generated key, and create a guideline to set up randomness testing and privacy amplification to eliminate security loss and achieve efficient key generation rate. We use theoretical analysis and comprehensive experiments to reveal that common practice misuses randomness testing and privacy amplification: (i) no security insurance of key strength, (ii) low efficiency of key generation rate. After revision by our guideline, security loss can be eliminated and key generation rate can be increased significantly.

Location spoofing attack deceiving a Wi-Fi positioning system has been studied for over a decade. However, it has been challenging to construct a practical spoofing attack in urban areas with dense coverage of legitimate Wi-Fi APs. This paper identifies the vulnerability of the Google Geolocation API, which returns the location of a mobile device based on the information of the Wi-Fi access points that the device can detect. We show that this vulnerability can be exploited by the attacker to reveal the black-box localization algorithms adopted by the Google Wi-Fi positioning system and easily launch the location spoofing attack in dense urban areas with a high success rate. Furthermore, we find that this vulnerability can also lead to severe consequences that hurt user privacy, including the leakage of sensitive information like precise locations, daily activities, and demographics. Ultimately, we discuss the potential countermeasures that may be used to mitigate this vulnerability and location spoofing attack.