Radio environment maps provide a comprehensive spatial view of the wireless channel and are especially useful in on-demand UAV wireless networks where operators are not afforded the typical time spent planning base station deployments. Equipped with an accurate radio environment map, a mobile UAV can quickly locate to an optimal location to serve UEs on the ground. Machine learning has recently been proposed as a tool to create radio environment maps from from satellite images of the target environment. However the highly dynamic nature that precipitates most ad-hoc aerial network deployments likely means that whatever satellite image data is available for the environment is inaccurate. In this paper we present, \system, a hybrid offline/online system for radio environment map creation which leverages a common sensing modality present on most UAVs: visual cameras. \system combines a suite of off-line trained neural network models with an adaptive trajectory planning algorithm to iteratively predict the REM and estimate the most valuable trajectory locations. By using UAV vision, \system arrives at a more accurate map quicker with fewer measurements, than other approaches, is effective even in scenarios where no prior environmental knowledge is available.

Unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services in close proximity to mobile devices. However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction and heterogeneity between MDs and MEC servers, the trajectory control requirements of energy efficiency and timeliness, and the different time-scale dynamics of the network. To address these issues, we first present a hierarchical architecture by incorporating terrestrial-aerial computing capabilities and leveraging UAV flexibility. Furthermore, we formulate a joint computing resource allocation, computation offloading, and trajectory control problem (JCCTP) to maximize the system utility. Since the problem is a non-convex mixed integer nonlinear program (MINLP), we propose a two-time-scale joint optimization approach. In the short time scale, we propose a price-incentive method for on-demand computing resource allocation and a matching-based method for computation offloading. In the long time scale, we propose a convex optimization-based method for UAV trajectory control. Besides, we prove the stability, optimality, and polynomial complexity of TJCCT. Simulation results demonstrate that TJCCT outperforms the comparative algorithms in terms of the total utility of the system, aggregate QoE of MDs, and total revenue of MEC servers.

Given flexible mobility, rapid deployment, and low cost, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) shows great potential to compensate for the lack of terrestrial edge computing coverage. However, limited battery capacity, computing and spectrum resources also pose serious challenges for UAV-enabled MEC, which shorten the service time of UAVs and degrade the quality of experience (QoE) of user devices (UDs) without effective control approach. In this work, we consider a UAV-enabled MEC scenario where a UAV serves as an aerial edge server to provide computing services for multiple ground UDs. Then, a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) is formulated to maximize the QoE of UDs under the UAV energy consumption constraint. To solve the JTRTOP that is proved to be the future-dependent and NP-hard problem, an online joint optimization approach (OJOA) is proposed. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) by using Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes.

The rapid growth of Internet of Things (IoT) applications has spurred to the need for efficient data collection mechanisms. Traditional approaches relying on fixed infrastructure have limitations in coverage, scalability, and deployment costs. Unmanned Aerial Vehicles (UAVs) have emerged as a promising alternative due to their mobility and flexibility. In this paper, we aim to minimize the number of UAVs deployed to collect data in IoT networks while considering a delay budget for energy limitation and data freshness. To this end, we propose a novel 3-approximation dynamic-programming-based algorithm called GPUDA to address the challenges of efficient data collection from IoT devices via UAVs for real-world scenarios where the number of UAVs owned by an individual or organization is unlikely to be excessive, improving the best-known ratio of 4. GPUDA is a geometric partition-based method that incorporates data rounding techniques. The experimental results demonstrate that the proposed algorithm requires 35.01% to 58.55% fewer deployed UAVs compared to existing algorithms on average.

Nowadays, point-of-sale (POS) terminals are no longer limited to wired connections, and many of them rely on Wi-Fi for data transmission. While Wi-Fi provides the convenience of wireless connectivity, it also introduces significant security risks. Previous research has explored Wi-Fi-based eavesdropping methods. However, these methods often rely on limited environmental robustness of Channel State Information (CSI) and require invasive Wi-Fi hardware, making them impractical in real-world scenarios. In this work, we present SThief, a practical Wi-Fi-based eavesdropping attack that leverages beamforming feedback information (BFI) exchanged between POS terminal and access points (APs) to keystroke inference on POS keypads. By capitalizing on the clear-text transmission characteristics of BFI, this attack demonstrates a more flexible and practical nature, surpassing traditional CSI-based methods. BFI is transmitted in the uplink, carrying downlink channel information that allows the AP to adjust beamforming angles. We exploit this channel information to keystroke inference. To enhance the BFI series, we use maximal ratio combining (MRC), ensuring efficiency across various scenarios. Additionally, we employ the Connectionist Temporal Classification method for keystroke inference, providing exceptional generalization and scalability. Extensive testing validates \name's effectiveness, achieving an impressive 81% accuracy rate in inferring 6-digit POS passwords within the top-100 attempts.

Unmanned aerial vehicles (UAVs)-enabled aerial communication provides a flexible, reliable, and cost-effective solution for a range of wireless applications. However, due to the high line-of-sight (LoS) probability, aerial communications between UAVs are vulnerable to eavesdropping attacks, particularly when multiple eavesdroppers collude. In this work, we aim to introduce distributed collaborative beamforming (DCB) into UAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two UAV swarms and construct these swarms as two UAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and the maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of UAVs for constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives as a multi-objective optimization problem. Following this, we propose an enhanced multi-objective swarm intelligence algorithm via the characterized properties of the problem. Simulation results show that our algorithm outperforms other state-of-the-art baseline algorithms. Experimental tests demonstrate that our method can be deployed in limited computing power platforms of UAVs and is beneficial for saving computational resources.

Sound eavesdropping using light has been an area of considerable interest and concern, as it can be achieved over long distances. However, previous work has often lacked stealth (e.g., active emission of laser beams) or been limited in the range of realistic applications (e.g., using direct light from a device's indicator LED or a hanging light bulb). In this paper, we present EchoLight, a non-intrusive, passive and long-range sound eavesdropping method that utilizes the extensive reflection of ambient light from vibrating objects to reconstruct sound. We analyze the relationship between reflection light signals and sound signals, particularly in situations where the frequency response of reflective objects and the efficiency of diffuse reflection are suboptimal. Based on this analysis, we have introduced an algorithm based on cGAN to address the issues of nonlinear distortion and spectral absence in the frequency domain of sound. We extensively evaluate EchoLight's performance in a variety of real-world scenarios. It demonstrates the ability to accurately reconstruct audio from a variety of source distances, attack distances, sound levels, light sources, and reflective materials. Our results reveal that the reconstructed audio exhibits a high degree of similarity to the original audio over 40 meters of attack distance.

Recent years have witnessed a surge of headphones (including in-ear headphones) usage in works and communications. Because of its privacy-preserve property, people feel comfortable having confidential communication wearing headphones and pay little attention to speech leakage. In this paper, we present an end-to-end eavesdropping system, mmEar, which shows the feasibility to launch an eavesdropping attack on headphones leveraging a commercial mmWave radar. Different from previous works that realize eavesdropping by sensing speech-induced vibrations with reasonable amplitude, mmEar focuses on capturing the extremely faint vibrations with a low signal-to-noise ratio on the surface of headphones. Toward this end, we propose a faint vibration emphasis (FVE) method that models and amplifies the mmWave responses to speech-induced vibrations on the In-phase and Quadrature (IQ) plane, followed by a deep denoising network to further improve the SNR. To achieve practical eavesdropping on various headphones and setups, we propose a cGAN model with a pretrain-finetune scheme, boosting the generalization ability and robustness of the attack by generating high-quality synthesis data. We evaluate mmEar with extensive experiments on different headphones and earphones and find that most of them can be compromised by the proposed attack for speech recovery.

Anomaly-based network intrusion detection systems (A-NIDS) use unsupervised models to detect unforeseen attacks. However, existing A-NIDS solutions suffer from low throughput, lack of interpretability, and high maintenance costs. Recent in-network intelligence (INI) exploits programmable switches to offer line-rate deployment of NIDS. Nevertheless, current in-network NIDS are either model-specific or only apply to supervised models. In this paper, we propose Genos, a general in-network framework for unsupervised A-NIDS by rule extraction, which consists of a Model Compiler, a Model Interpreter, and a Model Debugger. Specifically, observing benign data are multimodal and usually located in multiple subspaces in the feature space, we utilize a divide-and-conquer approach for model-agnostic rule extraction. In the Model Compiler, we first propose a tree-based clustering algorithm to partition the feature space into subspaces, then design a decision boundary estimation mechanism to approximate the source model in each subspace. The Model Interpreter interprets predictions by important attributes to aid network operators in understanding the predictions. The Model Debugger conducts incremental updating to rectify errors by only fine-tuning rules on affected subspaces, thus reducing maintenance costs. We implement a prototype using physical hardware, and experiments demonstrate its superior performance of 100 Gbps throughput, great interpretability, and trivial updating overhead.

Network intrusion detection (NID) aims to identify unusual network traffic patterns (distribution shifts) that require NID systems to evolve continuously. While prior art emphasizes fully supervised annotated data-intensive continual learning methods for NID, semi-supervised continual learning (SSCL) methods require only limited annotated data. However, the inherent class imbalance (CI) in network traffic can significantly impact the performance of SSCL approaches. Previous approaches to tackle CI issues require storing a subset of labeled training samples from all past tasks in the memory for an extended duration, potentially raising privacy concerns. The proposed SPIDER (Semisupervised Privacy-preserving Intrusion Detection with Drift-aware Continual Learning) is a novel method that combines gradient projection memory with SSCL to handle CI effectively without storing labeled samples from all of the previous tasks. We assess SPIDER's performance against baselines on six intrusion detection benchmarks formed over a short period and the Anoshift benchmark spanning ten years, which includes natural distribution shifts. Additionally, we validate our approach on standard continual learning image classification benchmarks known for frequent distribution shifts compared to NID benchmarks. SPIDER achieves comparable performance to baseline (fully supervised, semisupervised) methods, utilizes a maximum of 20% annotated data while reducing the total training time by 2X.

The rapid expansion of the Internet of Things (IoT) has raised increasing concern about targeted cyber attacks. Previous research primarily focused on static Intrusion Detection Systems (IDSs), which employ offline training to safeguard IoT systems. However, such static IDSs struggle with real-world scenarios where IoT system behaviors and attack strategies can undergo rapid evolution, necessitating dynamic and adaptable IDSs. In response to this challenge, we propose AOC-IDS, a novel online IDS that features an autonomous anomaly detection module (ADM) and a labor-free online framework for continual adaptation. In order to enhance data comprehension, the ADM employs an Autoencoder (AE) with a tailored Cluster Repelling Contrastive (CRC) loss function to generate distinctive representation from limited or incrementally incoming data in the online setting. Moreover, to reduce the burden of manual labeling, our online framework leverages pseudo-labels automatically generated from the decision-making process in the ADM to facilitate periodic updates of the ADM. The elimination of human intervention for labeling and decision-making boosts the system's compatibility and adaptability in the online setting to remain synchronized with dynamic environments. Experimental validation using the NSL-KDD and UNSW-NB15 datasets demonstrates the superior performance and adaptability of AOC-IDS, surpassing the state-of-the-art solutions.

Existing Deep Learning (DL) based Intrusion Detection System (IDS) is able to characterize sequence semantics of traffic and discover malicious behaviors. Yet DL models are often nonlinear and highly non-convex functions that are difficult for in-network deployment. In this paper, we present RIDS, a hardware-friendly Recurrent Neural Network (RNN) model that is co-designed with programmable switches. As its core, RIDS is powered by two tightly-coupled components: (i) rLearner, the RNN learning module with in-network deployability as the first-class requirement; and (ii) rEnforcer, the concrete dataplane design to realize rLearner -generated models inside the network dataplane. We implement a prototype of RIDS and evaluate it on our physical testbed. The experiments show that RIDS could satisfy both detection performance and high-speed bandwidth adaptation simultaneously, when none of the other compared approaches could do so. Inspiringly, RIDS realizes remarkable intrusion/malware detection effect (e.g., ∼99% F1 score) and model deployment (e.g., 100 Gbps per port), while only imposing nanoseconds of latency.