EsaNet: Environment Semantics Enabled Physical Layer Authentication

Wireless networks are vulnerable to physical layer spoofing attacks due to the wireless broadcast nature, thus, integrating communications and security (ICAS) is urgently needed for 6G endogenous security. In this letter, we propose an environment semantics enabled physical layer authentication network based on deep learning, namely EsaNet, to authenticate the spoofing from the underlying wireless protocol. Specifically, the frequency independent wireless channel fingerprint (FiFP) is extracted from the channel state information (CSI) of a massive multi-input multi-output (MIMO) system based on environment semantics knowledge. Then, we transform the received signal into a two-dimensional red green blue (RGB) image and apply the you only look once (YOLO), a single-stage object detection network, to quickly capture the FiFP. Next, a lightweight classification network is designed to distinguish the legitimate from the illegitimate users. Finally, the experimental results show that the proposed EsaNet can effectively detect physical layer spoofing attacks and is robust in time-varying wireless environments

Generative Pretraining in Multimodality

We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context. This omnivore model can take in any single-modality or multimodal data input indiscriminately (e.g., interleaved image, text and video) through a one-model-for-all autoregressive training process. First, visual signals are encoded into embeddings, and together with text tokens form an interleaved input sequence. Emu is then end-to-end trained with a unified objective of classifying the next text token or regressing the next visual embedding in the multimodal sequence. This versatile multimodality empowers the exploration of diverse pretraining data sources at scale, such as videos with interleaved frames and text, webpages with interleaved images and text, as well as web-scale image-text pairs and video-text pairs. Emu can serve as a generalist multimodal interface for both image-to-text and text-to-image tasks, and supports in-context image and text generation. Across a broad range of zero-shot/few-shot tasks including image captioning, visual question answering, video question answering and text-to-image generation, Emu demonstrates superb performance compared to state-of-the-art large multimodal models. Extended capabilities such as multimodal assistants via instruction tuning are also demonstrated with impressive performance.Comment: Code and Demo: https://github.com/baaivision/Em

Trust Dynamics in WSNs: An Evolutionary Game-Theoretic Approach

A sensor node (SN) in Wireless Sensor Networks (WSNs) can decide whether to collaborate with others based on a trust management system (TMS) by making a trust decision. In this paper, we study the trust decision and its dynamics that play a key role to stabilize the whole network using evolutionary game theory. When SNs are making their decisions to select action Trust or Mistrust, a WSNs trust game is created to reflect their utilities. An incentive mechanism bound with one SN’s trust degree is incorporated into this trust game and effectively promotes SNs to select action Trust. The replicator dynamics of SNs’ trust evolution, illustrating the evolutionary process of SNs selecting their actions, are given. We then propose and prove the theorems indicating that evolutionarily stable strategies can be attained under different parameter values, which supply theoretical foundations to devise a TMS for WSNs. Moreover, we can find out the conditions that will lead SNs to choose action Trust as their final behavior. In this manner, we can assure WSNs’ security and stability by introducing a trust mechanism to satisfy these conditions. Experimental results have confirmed the proposed theorems and the effects of the incentive mechanism

Reliability Evaluation for Clustered WSNs under Malware Propagation

We consider a clustered wireless sensor network (WSN) under epidemic-malware propagation conditions and solve the problem of how to evaluate its reliability so as to ensure efficient, continuous, and dependable transmission of sensed data from sensor nodes to the sink. Facing the contradiction between malware intention and continuous-time Markov chain (CTMC) randomness, we introduce a strategic game that can predict malware infection in order to model a successful infection as a CTMC state transition. Next, we devise a novel measure to compute the Mean Time to Failure (MTTF) of a sensor node, which represents the reliability of a sensor node continuously performing tasks such as sensing, transmitting, and fusing data. Since clustered WSNs can be regarded as parallel-serial-parallel systems, the reliability of a clustered WSN can be evaluated via classical reliability theory. Numerical results show the influence of parameters such as the true positive rate and the false positive rate on a sensor node’s MTTF. Furthermore, we validate the method of reliability evaluation for a clustered WSN according to the number of sensor nodes in a cluster, the number of clusters in a route, and the number of routes in the WSN

Quantal Response Equilibrium-Based Strategies for Intrusion Detection in WSNs

This paper is to solve the problem stating that applying Intrusion Detection System (IDS) to guarantee security of Wireless Sensor Networks (WSNs) is computationally costly for sensor nodes due to their limited resources. For this aim, we obtain optimal strategies to save IDS agents’ power, through Quantal Response Equilibrium (QRE) that is more realistic than Nash Equilibrium. A stage Intrusion Detection Game (IDG) is formulated to describe interactions between the Attacker and IDS agents. The preference structures of different strategy profiles are analyzed. Upon these structures, the payoff matrix is obtained. As the Attacker and IDS agents interact continually, the stage IDG is extended to a repeated IDG and its payoffs are correspondingly defined. The optimal strategies based on QRE are then obtained. These optimal strategies considering bounded rationality make IDS agents not always be in Defend. Sensor nodes’ power consumed in performing intrusion analyses can thus be saved. Experiment results show that the probabilities of the actions adopted by the Attacker can be predicted and thus the IDS can respond correspondingly to protect WSNs

Optimal Report Strategies for WBANs Using a Cloud-Assisted IDS

Applying an Intrusion Detection System (IDS) to Wireless Body Area Networks (WBANs) becomes a costly task for body sensors due to their limited resources. To solve this problem, a cloud-assisted IDS framework is proposed. We adopt a new distributed-centralized mode, where IDS agents residing in body sensors will be triggered to launch. All IDS agents are only responsible for reporting the monitored events, not intrusion decision that is processed in the cloud platform. We then employ the signaling game to construct an IDS Report Game (IDSRG) depicting interactions between a body sensor and its opponent. The pure- and mixed-strategy Bayesian Nash Equilibriums (BNEs) of the stage IDSRG are achieved, respectively. As two players interact continually, we develop the stage IDSRG into a dynamic multistage game in which the belief can be updated dynamically. Upon the current belief, the Perfect Bayesian Equilibrium (PBE) of the dynamic multistage IDSRG is attained, which helps the IDS-sensor select the optimal report strategy. We afterward design a PBE-based algorithm to make the IDS-sensor decide when to report the monitored events. Experiments show the effectiveness of the dynamic multistage IDSRG in predicting the type and optimal strategy of a malicious body sensor

Structural evolution during corn stalk acidic and alkaline hydrogen peroxide pretreatment

Oxidative pretreatment is a promising strategy for biomass utilization due to its excellent performance and economic costs. Insights into lignocellulose recalcitrance are essential to further develop biomass manufacture. To understand structural recalcitrance, we performed systematic characterizations after mild hydrogen peroxide pretreatment in alkali or Lewis acid environments, followed by enzymatic hydrolysis to obtain higher glucose yields than raw materials. Results indicated that hydrogen peroxide pretreatment in saturated calcium hydroxide solution effectively removed lignin through cleavage of the lignin-carbohydrate complex and part C-C linkages in the middle lamella. The ferrous sulfate solution induced hemicellulose solubility, and the partially oxidized lignin was depolymerized under hydrogen peroxide pretreatment. The solvent could then be used for subsequent hydrolysis and cellulose conversion. It was demonstrated that hydrogen peroxide pretreatment is an effective and promising method for refining lignocellulose under an alkaline environment and is suitable for subsequent cellulose conversion in ferrous sulfate solution

Influence of Drought Stress on the Rhizosphere Bacterial Community Structure of Cassava (<i>Manihot esculenta</i> Crantz)

Drought presents a significant abiotic stress that threatens crop productivity worldwide. Rhizosphere bacteria play pivotal roles in modulating plant growth and resilience to environmental stresses. Despite this, the extent to which rhizosphere bacteria are instrumental in plant responses to drought, and whether distinct cassava (Manihot esculenta Crantz) varieties harbor specific rhizosphere bacterial assemblages, remains unclear. In this study, we measured the growth and physiological characteristics, as well as the physical and chemical properties of the rhizosphere soil of drought-tolerant (SC124) and drought-sensitive (SC8) cassava varieties under conditions of both well-watered and drought stress. Employing 16S rDNA high-throughput sequencing, we analyzed the composition and dynamics of the rhizosphere bacterial community. Under drought stress, biomass, plant height, stem diameter, quantum efficiency of photosystem II (Fv/Fm), and soluble sugar of cassava decreased for both SC8 and SC124. The two varieties’ rhizosphere bacterial communities’ overall taxonomic structure was highly similar, but there were slight differences in relative abundance. SC124 mainly relied on Gamma-proteobacteria and Acidobacteriae in response to drought stress, and the abundance of this class was positively correlated with soil acid phosphatase. SC8 mainly relied on Actinobacteria in response to drought stress, and the abundance of this class was positively correlated with soil urease and soil saccharase. Overall, this study confirmed the key role of drought-induced rhizosphere bacteria in improving the adaptation of cassava to drought stress and clarified that this process is significantly related to variety

Lead activates neutrophil degranulation to induce early myocardial injury in mice

Lead (Pb) is a pervasive toxic metal contaminant associated with a high risk of myocardial injury. However, the precise mechanism underlying Pb-induced myocardial injury has yet to be fully elucidated. In this study, a murine model of Pb exposure (0, 1, 5, and 10 mg/kg) was employed to investigate the involvement of neutrophil degranulation in the induction of myocardial injury. Notably, serum levels of cardiac troponin I (cTnI) and creatine kinase-MB (CK-MB) increased significantly in Pb-exposed mice, whereas cTnI levels in cardiomyocytes decreased, suggesting that Pb exposure may cause early myocardial injury. Moreover, Pb exposure was found to promote neutrophil degranulation, as evidenced by elevated myeloperoxidase (MPO) and neutrophil elastase (NE) concentrations in both the serum of Pb-exposed workers and Pb-exposed mice, as well as the extracellular supernatant of neutrophils following exposure. However, we found that serum level of cTnI enhanced by Pb exposure is associated with increased NE levels in the serum, but not with MPO levels. Upon treatment with NE inhibitor (sivelestat), the serum level of cTnI markedly reduced in Pb-exposed mice, we found that early myocardial injury is associated with NE levels in the serum. At the molecular level, western blotting analysis revealed an upregulation of ERK1/2 expression in vitro following Pb exposure, suggesting that the activation of the ERK1/2 signaling pathway may underlie the participation of neutrophil degranulation in Pb-induced myocardial injury. In summary, our findings demonstrate that Pb exposure can initiate early myocardial injury by promoting the neutrophil degranulation process, thereby highlighting the potential role of this process in the pathogenesis of Pb-associated myocardial injury