Due to the greatly improved capabilities of devices, massive data, and increasing concern about data privacy, Federated Learning (FL) has been increasingly considered for applications to wireless communication networks (WCNs). Wireless FL (WFL) is a distributed method of training a global deep learning model in which a large number of participants each train a local model on their training datasets and then upload the local model updates to a central server. However, in general, nonindependent and identically distributed (non-IID) data of WCNs raises concerns about robustness, as a malicious participant could potentially inject a “backdoor” into the global model by uploading poisoned data or models over WCN. This could cause the model to misclassify malicious inputs as a specific target class while behaving normally with benign inputs. This survey provides a comprehensive review of the latest backdoor attacks and defense mechanisms. It classifies them according to their targets (data poisoning or model poisoning), the attack phase (local data collection, training, or aggregation), and defense stage (local training, before aggregation, during aggregation, or after aggregation). The strengths and limitations of existing attack strategies and defense mechanisms are analyzed in detail. Comparisons of existing attack methods and defense designs are carried out, pointing to noteworthy findings, open challenges, and potential future research directions related to security and privacy of WFL. 

Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey

Due to the greatly improved capabilities of devices, massive data, and increasing concern about data privacy, Federated Learning (FL) has been increasingly considered for applications to wireless communication networks (WCNs). Wireless FL (WFL) is a distributed method of training a global deep learning model in which a large number of participants each train a local model on their training datasets and then upload the local model updates to a central server. However, in general, non-independent and identically distributed (non-IID) data of WCNs raises concerns about robustness, as a malicious participant could potentially inject a"backdoor"into the global model by uploading poisoned data or models over WCN. This could cause the model to misclassify malicious inputs as a specific target class while behaving normally with benign inputs. This survey provides a comprehensive review of the latest backdoor attacks and defense mechanisms. It classifies them according to their targets (data poisoning or model poisoning), the attack phase (local data collection, training, or aggregation), and defense stage (local training, before aggregation, during aggregation, or after aggregation). The strengths and limitations of existing attack strategies and defense mechanisms are analyzed in detail. Comparisons of existing attack methods and defense designs are carried out, pointing to noteworthy findings, open challenges, and potential future research directions related to security and privacy of WFL.

This paper presents a pioneering approach to bolstering network security and privacy by implementing chaotic optical communication with a hybrid optical feedback system (HOFS). The current baseline methods in network security are often less feasible for hybrid feedback systems, including limited robustness, compromised security, and synchronization challenges. Therefore, this paper proposes a hybrid approach to address these shortcomings by integrating the HOFS into chaotic optical communication systems (HOFS-COCS) to overcome the baseline challenges. This paper aims to improve network security while significantly maintaining efficient communication channels. Moreover, We designed two algorithms, one for chaotic maps generation and another for text encryption and decryption, to improve security in the hybrid feedback system. Our findings demonstrate through rigorous experimentation and analysis that the proposed (HOFS-COCS) method significantly improves network security by enabling reliable chaos generation, synchronization, and secure message transmission in chaotic optical communication systems. This research represents a significant advancement towards enhanced secrecy and synchronization in chaotic optical communication systems, promising a paradigm shift in network security protocols. 

Enhancing network security with hybrid feedback systems in chaotic optical communication

Drones are gaining more interest thanks to their advantages and great potential for applications. However, present swarming drones’ stand-alone centralized radio frequency control mode from a base station has non-trivial drawbacks such as severe interference, latency caused localization error, etc. Differently, optical camera communication (OCC) is promising as an alternative for integrated communication and sensing for swarming drones. We propose PoseFly, the first 4-in-1 OCC approach for swarming drones. With exploited rolling shutter effect and the already installed camera and LED nodes, PoseFly provides (1) massive drone indication and identification, (2) multilevel on-site localization, (3) quick-link channel among drones, and (4) basic lighting. The design methodology of PoseFly gives a valuable example for the low-cost integrated sensing and communication for swarming drones. 

Demo: Integrated On-site Localization and Optical Camera Communication for Drones

The existing research on dependent task offloading and resource allocation assumes that edge servers can provide computational and communication resources free of charge. This paper proposes a two-stage resource allocation method to address this issue. In the first stage, users incentivize edge servers to provide resources. We formulate the incentive problem in this stage as a multivariate Stackelberg game, which takes into account both computational and communication resources. In addition, we also analyze the uniqueness of the Stackelberg equilibrium under information sharing conditions. Considering the privacy issues of the participants, the research is extended to scenarios without information sharing, where the multivariable game problem is modeled as a partially observable Markov decision process (POMDP). In order to obtain the optimal incentive decision in this scenario, a reinforcement learning algorithm based on the learning game is designed. In the second stage, we propose a greedy-based deep reinforcement learning algorithm that is aimed at minimizing task execution time by optimizing resource and task allocation strategies. Finally, the simulation results demonstrate that the algorithm designed for non-information sharing scenarios can effectively approximate the theoretical Stackelberg equilibrium, and its performance is found to be better than that of the other three benchmark methods. After the allocation of resources and sub-tasks by the greedy-based deep reinforcement learning algorithm, the execution delay of the dependent task is significantly lower than that in local processing.

A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation

With the demand for economical and high-speed wireless services, Optical Wireless Communication (OWC) has attracted increasing attention in both research and the market. In the past decades, numerous optical-related techniques (e.g., LEDs, displays, cameras) and systems (e.g., VLC, LiFi, LiDAR) have been invented. OWC techniques, which are considered as a competitive mechanics in next-generation networks as an alternative to RF approaches, offer 10,000 times more bandwidth than conventional radio frequency (RF)-based wireless techniques (e.g., WiFi, LoRa, Bluetooth, LTE), as well as tremendous spatial reuse potential with even less interference. Because optical communications have a limited wavelength and travel in the line of sight (LoS) manner, the OWC is commonly thought as a secure wireless approach to confine light transmissions within physical bounds. However, in the real world, it is completely untrue. The privacy leakages and security risks broadly exist in the optical-related wireless applications including OWC networks. These threats and weaknesses have recently been the subject of several initial studies. However, they lack systematic analysis and are isolated. This survey first presents a general workflow of OWC systems which consists of three stages: before signal emission (BSE), during signal propagation (DSP), and after signal receiving (ASV). For each stage, related risks are reviewed. Then, we summarize existing attacks in optical-related wireless applications and corresponding counter-attack solutions. Finally, we outline the future trends for improving OWC security.

https://dl.acm.org/doi/pdf/10.1145/3594718

The Security in Optical Wireless Communication: A Survey

Abstract-5G/4G voice calls are always encrypted for security and privacy. However, in this work, we unveil several vulnerabilities which can unintentionally leak 5G/4G call state information, despite encryption protection. They stem from recent call optimization techniques standardized in the 3GPP specifications and adopted by mobile network operators. While these techniques are effective to enhance 5G/4G call quality and efficiency, they unfortunately expose extra call information, which can be exploited to precisely infer call states and launch side-channel attacks. By leveraging precise call states, we devise a Cross-domain Identity Linkage attack, CrossIL, which aims to infer mobile users’ user identities and cellular identities, thereby enabling powerful cyberattacks or privacy inferences against high-value victims. We have experimentally validated these vulnerabilities and assessed the attack damages with three major U.S. carriers. Our experimental result shows that the success rate on the identity inference ranges from 89% to 98%. Finally, we propose and evaluate a cellular-friendly solution.

When Good Turns Evil: Encrypted 5G/4G Voice Calls Can Leak Your Identities

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The Security in Optical Wireless Communication: A Survey

When Good Turns Evil: Encrypted 5G/4G Voice Calls Can Leak Your Identities