As the backbone of global communication infrastructure, optical fibers carry vast amounts of sensitive data, making them prime targets for eavesdropping attacks that can compromise data confidentiality and national security. However, optical fibers are highly vulnerable to eavesdropping attacks-such as fiber bending or evanescent coupling-which can eavesdrop on data without interrupting the service. Accurate detection of eavesdropping remains a critical challenge, particularly under complex environmental disturbances. Environmental factors such as fluctuation and disturbances can induce signal distortions and polarization variations that mask or mimic eavesdropping characteristics, thereby posing a significant challenge to the training and generalization of machine learning-based classifiers. In this Letter, we propose a sensing framework based on a hybrid gated recurrent unit-convolutional neural network model that jointly analyzes polarization parameters and error vector magnitude. The proposed method enables accurate and robust eavesdropping detection and temporal localization under complex physical environments. Experiments conducted across seven practical scenarios demonstrate that the proposed scheme achieves a detection accuracy of 97.2%. Furthermore, we developed real-time and offline optical fiber monitoring platforms integrating mainstream detection techniques to facilitate comprehensive evaluation and deployment.
The overall function of an ecosystem is determined by the richness of its biodiversity and the complex interactions formed between the different species that inhabit it. Understanding how global change factors (including climate change), and their combinations, are affecting the intricate species-to-species relationships formed within different ecosystems and agro-ecosystems is becoming therefore increasingly important to our future. Here, we discuss how improved plant-to-plant and plant-to-microbiome signaling, achieved via research, intervention, and altered practices, can be used to form resilient plant communities that will help us shape our environment and successfully address some of our current and future anthropogenically generated critical challenges.
Root-knot nematodes navigate the underground chemical landscape to find their hosts. Building on Wu et al.'s discovery that plant metabolites shape microbial cues guiding nematode behavior, this commentary explores how rhizosphere chemical communication integrates plant, microbial, and parasite interactions within a shared 'information network'.
Terahertz communications offer unprecedented data rates for next-generation wireless networks but suffer from blockage susceptibility that restricts coverage and introducse physical-layer security vulnerabilities. Non-line-of-sight relay schemes using metallic wavy surfaces (MWS) address coverage limitations but require concealment beneath indoor materials for practical deployment. This work investigates THz channel characteristics and security vulnerabilities when MWS surfaces are covered with wallpaper, curtains, and wall plaster across 113-170 GHz. Results reveal that covering materials redistribute rather than eliminate eavesdropping threats, with persistent feasible interception scenarios remaining undetectable through conventional backscattering monitoring. These findings underscore the need for enhanced mechanisms designed for covered reflecting elements.
The broadcast nature of wireless channels introduces significant security vulnerabilities in information transmission, particularly when the eavesdropper is close to the legitimate destination. In such scenarios, the eavesdropping channel often exhibits high spatial correlation with, or even superior quality to, the legitimate channel. This makes it challenging for traditional power optimization methods to effectively suppress the eavesdropping rate. To address this challenge, this paper proposes an optimization method for the secrecy capacity of unmanned aerial vehicle (UAV) relaying based on the dynamic adjustment of the power allocation factor. By injecting artificial noise (AN) during signal forwarding and combining it with real-time channel state information, the power allocation factor can be dynamically adjusted to achieve precise jamming of the eavesdropping link. We consider a four-node communication model consisting of a source, a UAV, a legitimate destination, and a passive eavesdropper, and formulate a joint optimization problem to maximize the secrecy rate. Due to the non-convexity of the original problem, we introduce relaxation variables and apply successive convex approximation (SCA) to reformulate it into an equivalent convex optimization problem. An analytical solution for the power allocation factor is derived using the water-filling (WF) algorithm. Furthermore, an alternating iterative optimization algorithm with AN assistance is proposed to achieve global optimization of the system parameters. Simulation results demonstrate that, compared to traditional power optimization schemes, the proposed algorithm substantially suppresses the eavesdropping channel capacity while enhancing transmission efficiency, thereby significantly improving both secrecy performance and overall communication reliability.
This paper investigates secure and low-latency communications in UAV-mounted simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted urban vehicular networks, where severe blockage, high vehicle mobility, eavesdropping threats, and delay-sensitive traffic services coexist. In the considered system, the UAV is used not only as an aerial carrier for the STAR-RIS but also as a mobile intelligent control node that can dynamically adjust its horizontal aerial position according to vehicle distribution, blockage conditions, and eavesdropping threats. First, a UAV-STAR-RIS-assisted vehicular communication system model is developed by jointly considering urban blockage, vehicle mobility, passive eavesdropping attacks, queueing dynamics, and UAV flight constraints. Then, a high-dimensional, non-convex, and strongly coupled dynamic optimization problem is formulated to maximize the long-term average secure and low-latency utility through the joint optimization of the UAV trajectory, the STAR-RIS transmission-reflection partition ratio, the phase-shift matrices, and the transmit power allocation. Furthermore, the problem is modeled as a Markov decision process with continuous state and action spaces, and a hierarchical constrained soft actor-critic (HC-SAC)-based joint control algorithm is proposed to enable adaptive UAV movement, STAR-RIS configuration, and power control in complex dynamic environments. Simulation results demonstrate that the proposed method outperforms DDPG and several structural benchmark schemes. In the representative evaluation, the proposed HC-SAC achieves an average delay of 10.85 slots and a secrecy outage probability of 0.7160, compared with 11.72 slots and 0.8501 for PPO, and 11.94 slots and 0.8599 for DDPG. Although PPO provides the highest average secrecy rate and successful service ratio, the proposed method still maintains a competitive secure communication capability and service reliability. A normalized composite utility analysis further shows that HC-SAC attains the highest utility value of 0.9254, indicating a more favorable security-latency trade-off in complex urban vehicular scenarios.
The rapid evolution of eavesdropping technologies has encouraged regular updates and improvement of encryption systems. Developing a detector-dependent optical encryption scheme to tightly connect the decryption and imaging processes offers great potential to prevent eavesdropping. By designing an optically programmable dual-band photodetector, a color image encryption scheme where the photodetector functions as both a detector and a critical decryption key is demonstrated here. The distinctive optically programmable property of the photodetector enables the manipulation of its long-wavelength sensitivity via short-wavelength photonic stimulation, leading to different imaging outputs between single-pixel imaging and point-scan imaging, which therefore demonstrates a capability to decrypt information hidden in color images. This detector-dependent decryption method can effectively prevent potential information leaks when other detectors are used as eavesdropping devices. Our encryption paradigm opens new avenues for color image encryption using photodetectors, enhancing encryption security by introducing a device-based dimension.
In widely deployed Internet of Things (IoT) scenarios, physical-layer key generation (PLKG) serves as a useful complement to conventional cryptographic methods, yet it often suffers from a fundamentally low key generation rate, which becomes particularly severe in quasi-static environments. This low rate is mainly attributed to three key issues: (1) slow channel variations, which provide insufficient randomness and thus limit the key generation rate; (2) correlation between the legitimate channel and the eavesdropping channel, which reduces the uniqueness of the extracted key and further degrades the achievable rate; and (3) insufficient degrees of freedom in the key source, which constrain the key space. To address these challenges, this paper introduces the Dynamic Agile Reconfigurable Intelligent Surface Antenna into physical-layer key generation. By deploying metasurface antennas at both ends and independently applying random phase modulation, the scheme injects two-sided randomness, thereby mitigating the adverse effects of quasi-static channels and legitimate eavesdropper channel correlation. Moreover, by leveraging the dynamic, agile, and reconfigurable characteristics of the metasurface antennas in the key generation process, the proposed approach can further enhance the key generation rate while simultaneously resolving all three issues above. The proposed scheme is developed under a general setting where correlation exists between the legitimate and eavesdropping channels. A closed-form expression for the key capacity is rigorously derived, accompanied by detailed theoretical analysis and simulations. The results demonstrate the superiority of the proposed approach when applied to physical-layer key generation.
Next-generation optical wireless communication requires photodetectors that offer both high spectral selectivity and strong security against interception. However, conventional broadband devices remain vulnerable to spectral crosstalk and eavesdropping. Here we show a digitally encoded dual-narrowband organic photodetector that intrinsically integrates optical filtering with algorithm-assisted encryption to enable secure, high-fidelity optical wireless communication. Operating without an external power supply, the self-powered device employs a Fabry-Pérot cavity with a carefully designed organic spacer Liq to achieve selective detection at wavelengths of 485 nm and 910 nm, along with an ultrafast response time of 440 ns. By combining chaotic encryption with hardware-level wavelength selectivity, our hardware-software co-design system achieves an ultra-low bit-error rate of 9.17 × 10-5 at 1.25 Mbps while demonstrating strong resilience to eavesdropping and external interference. Furthermore, precise cavity engineering allows the dual-narrowband response to be extended into the short-wave infrared region (>1230 nm), offering a scalable route toward multi-wavelength secure transmission, high-resolution spectroscopy, and intelligent photonic networks.
"Eavesdropping" on heterospecific alarm calls is common, but we are only beginning to understand the cognitive mechanisms involved. A key question is whether eavesdropping results from innate perceptual sensitivities to acoustic features that are common to the calls of diverse species, or if it is a learned behavior. Here, we used playback experiments to investigate whether wild and captive meerkats (Suricata suricatta), and wild yellow mongooses (Cynictis penicillata) respond in a functionally appropriate way, ie successfully "eavesdrop", to heterospecific alarm calls. We tested calls from both sympatric and allopatric species to reveal whether responses were a result of learned associations between call types and their referents, or of innate perceptual sensitivities to their acoustic features. By playing back calls which signal either "aerial" or "general" predation threats, we also explored whether subjects could differentiate between the underlying function of heterospecific alarm calls. Our results show that wild meerkats and wild yellow mongooses, but not captive meerkats, were more likely to respond to the alarm calls of sympatric species. Additionally, wild, but not captive, meerkats were also more likely to flee as a response to aerial as opposed to general alarm calls. This indicates that learning likely affects the response of mongooses to heterospecific alarm calls. However, other factors are also likely to have an impact on how individuals in different populations of the same species respond to heterospecific alarm calls, such as innate perceptual sensitivities, external threats, the need to respond, and the soundscape a species is exposed to.
Split-learning-based Virtual Physically Unclonable Functions (VPUFs) in Internet of Things (IoT) networks remain vulnerable to eavesdropping and replay attacks due to insufficient security mechanisms that balance robustness with computational efficiency. This paper proposes a novel digital watermarking approach to improve the security of Split-Learning-based VPUFs. The suggested framework utilizes deep learning-based approaches to generate a watermark to be embedded in the latent representation of the VPUF response to provide additional security against eavesdropping and replay attacks without incurring significant hardware or computational overhead. Watermark embedding is done by simulating Rayleigh fading through Jake's Model to get the secret channel information, which is input to an autoencoder to create a strong latent representation. The formed latent watermark is embedded into the latent response of the VPUF. Experimental testing demonstrates that fidelity remains high under test conditions, reliability, and unforgability, confirming that the watermarking process does not compromise the VPUF's performance. Further, the proposal supports dual-factor authentication through simultaneous verification of the extracted watermark and the retrieved latent response. This research not only enhances the strength and security of the baseline VPUF mechanism but also provides a cost-effective, scalable solution specifically designed for resource-constrained IoT networks.
Secure key distribution is fundamental to encrypted communications but remains challenging in indoor wireless scenarios due to high computational overhead and specialized hardware requirements. Here, a Meta Key Distribution (MKD) system based on programmable metasurface to securely distribute cryptographic keys and enable protocol-independent encrypted communication is proposed. The metasurface embeds synchronized entropy into wireless channel by dynamically modulating the spatiotemporal properties of electromagnetic wave, thus allowing legitimate users to independently extract identical cryptographic keys. A prototype is implemented and experimentally validated in an indoor environment. The results show that the proposed MKD system achieves a key generation rate of 400 bit/s and a bit error rate below 3%, demonstrating reliable key generation performance and strong resistance to passive eavesdropping. In addition, the system can be readily integrated with standard wireless protocols such as WiFi and Bluetooth without requiring significant modifications to existing communication hardware. This metasurface-assisted approach provides a lightweight, compatible, and secure key distribution solution, suitable for emerging applications in smart homes, the Internet of Things, and healthcare environments.
Quantum key distribution (QKD) establishes a shared secret between remote parties and is proven unbreakable in theory. Unfortunately, practical implementations of QKD exhibit device imperfections that lead to security vulnerabilities. Most of these vulnerabilities have been verified in a white-box testing scenario, when one has access to the system hardware for analysis. Here we implement automated penetration testing of a QKD system in a black-box setting, using only its public communication lines and a limited operator's manual. Our implementation parses information transmitted over the classical communication line and toggles an optical delay in the quantum communication line. This enables it to tamper with the timing settings of detector gates in the QKD system during its calibration procedure and to passively eavesdrop on 98.97% of the sifted key. The entire testing process is fully automated and takes only minutes to initiate eavesdropping. Our work paves the way for automated penetration testing of QKD installations as a method of security verification.
Interspecies communication networks about threats can shape animal communities, as many species produce alarm calls while eavesdropping on those of others, resulting in information flow crossing ecological niches and taxonomic boundaries1,2,3. Some species contribute disproportionately to these networks by being particularly vigilant, accurate in predator detection, and consistent in alarm call production. These key informants influence how others perceive danger and produce information used across taxonomic groups4,5,6. As well, predator encounters by themselves could also alter the soundscape more broadly by producing brief risk-related cues that reach many listeners7. However, information about how predator-related information spreads across animal communities remains poorly understood.
Quantum information processing enables secure communication, quantum teleportation, and computation. However, current protocols are limited by the narrow electronic bandwidth of standard measurement devices (megahertz to gigahertz), vastly underusing the broad optical bandwidth (10 to 100 terahertz) of readily available quantum light sources. We introduce a general framework for frequency-multiplexing of quantum channels along with methods for efficient processing of quantum information in those channels across the full optical bandwidth. Using a broadband squeezed-light source, spectral manipulation, and parametric homodyne detection, we generate, process, and measure multiple quantum channels in parallel. We demonstrate this through multiplexed protocols of both continuous-variable quantum key distribution (CV-QKD) and quantum teleportation. We experimentally demonstrate a proof-of-principle realization of multiplexed CV-QKD over 23 independent spectral channels with eavesdropping detection in each channel. These techniques pave the way for massively parallel quantum processing, potentially boosting the throughput of quantum protocols by orders of magnitude.
With the increasing demand for high throughput and ultra-dense small cell deployment in the next-generation communication networks, spectrum resources are becoming increasingly strained. At the same time, the security risks posed by eavesdropping remain a significant concern, particularly due to the broadcast-access property of optical fronthaul networks. To address these challenges, we propose a high-security, high-spectrum efficiency radio-over-fiber (RoF) system in this paper, which leverages compressive sensing (CS)-based algorithms and chaotic encryption. An 8 Gbit/s RoF system is experimentally demonstrated, with 10 km optical fiber transmission and 20 GHz radio frequency (RF) transmission. In our experiment, spectrum efficiency is enhanced by compressing transmission data and reducing the quantization bit requirements, while security is maintained with minimal degradation in signal quality. The system could recover the signal correctly after dequantization with 6-bit fronthaul quantization, achieving a structural similarity index (SSIM) of 0.952 for the legitimate receiver (Bob) at a compression ratio of 0.75. In contrast, the SSIM for the unauthorized receiver (Eve) is only 0.073, highlighting the effectiveness of the proposed security approach.
Nematodes communicate via diverse sex pheromones, including long-range volatile signals, short-range chemical cues, and contact-dependent molecules. While the ascaroside family of small molecules that mediate short-range attraction is well characterized, the identities and roles of volatile sex pheromones (VSPs) that act over longer ranges remain unknown. Using GC-MS analysis of crude VSP extracts, we identified cyclohexyl acetate (CA) as a candidate mimic, sharing retention time and mass spectral features with natural VSPs. Behavioral assays demonstrated that CA acts as a concentration-dependent, male-specific attractant in Caenorhabditis. Pre-exposure to VSPs induced cross-adaptation to CA, suggesting shared sensory processing. Surprisingly, genetic and calcium imaging analyses revealed that CA perception is mediated primarily by AWCon (str-2-expressing) neurons and involves VSP chemoreceptor srd-1-independent pathways, which are distinct from the neural pathways involved in natural VSP perception. These data indicate that CA is unlikely to be a major VSP constituent; rather, it is a structural analog that elicits male-specific attraction via a parallel sensory circuit. The endogenous source of CA in C. remanei remains unresolved; our data do not establish whether females produce CA. Its structural and behavioral mimicry provides new insights into the complexity of chemosensory signaling and the potential for interspecies chemical eavesdropping in nematode ecology.
The proliferation of massive antenna arrays and the consequent intensification of near-field effects with 6G necessitate addressing critical security challenges in near-field communication environments. This paper presents a novel artificial noise-aided spatial and directional modulation (SDMN-AN) framework, specifically tailored for secure near-field communications. The proposed system integrates legitimate receiver indices, modulation symbols, and artificial noise (AN) confined to the null space of legitimate channels, thereby enhancing both spectral efficiency and communication security. Two precoding strategies-maximum-ratio transmission (MRT) and zero-forcing (ZF)-are investigated, offering trade-offs between hardware complexity and detection overhead. Analytical derivations of bit error rate (BER) bounds, corroborated by simulation results, underscore the superiority of the SDMN-AN framework in mitigating eavesdropping threats while significantly improving spectral efficiency, positioning it as a compelling solution for next-generation secure wireless networks.
Species interactions are fundamental to ecological and evolutionary processes, shaping ecosystem dynamics and driving biodiversity. Among those, interactions between flies and amphibians are common in tropical areas, yet most aspects of their ecology and evolution are understudied. Using the PRISMA method, we systematically review the literature to examine the direct and indirect threats imposed by Diptera flies attacking amphibians and the behavioral, physiological, and acoustic defenses they elicit. We delve, for instance, into the eavesdropping behavior of some dipteran species, which use anuran calls as cues for host-seeking, and the potential impacts on frog communication systems. As flies can be disease vectors, we investigate pathogen transmission to amphibians as an indirect cost imposed by flies attacking them and examine the role of species specificity in these dynamics. Finally, we address how human activities are currently impacting these long-established interactions between dipterans and amphibians. We focus on potential disruptions caused by habitat alteration, the presence of invasive species, and climate change. By synthesizing existing knowledge of the threats imposed by flies on amphibians, we shed light on these groups of growing conservation concern given their current escalating extinction rates. Ultimately, our findings provide valuable insights into the intricacies of species interactions and underscore the urgent need for comprehensive studies mitigating the adverse effects of anthropogenic disturbances on these clades.
The vulnerability of intensity modulation direct detection (IM-DD) systems to physical-layer eavesdropping poses a significant threat to data center interconnects (DCI). In this Letter, we propose a dual-dynamic strategy for physical-layer encryption to enhance the security of IM-DD systems. The scheme synergistically combines dynamic key update and dynamic rule-based pulse-amplitude modulation (DR-PAM) symbol scrambling to achieve efficient security protection. The master key is dynamically updated according to a pre-negotiated rule, enabling the generation of a distinct session key for each data frame via a hash function. This approach ensures real-time operation without frequent key negotiation. To our knowledge, the DR-PAM is the first universal symbol scrambling scheme supporting arbitrary PAM orders. Experimental results demonstrate that at 56 Gbit/s and 112 Gbit/s data rates, legitimate receivers achieve performance identical to unencrypted links, while illegal receivers are completely unable to decrypt the data. The proposed encryption scheme has better compatibility with existing transmission systems and introduces no performance penalty, thus delivering a secure, real-time, and practical solution for cost-sensitive, high-speed DCI.