Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting...Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.展开更多
As mobile networks become high speed and attain an all-IP structure,more services are possible.This brings about many new security requirements that traditional security programs cannot handle.This paper analyzes secu...As mobile networks become high speed and attain an all-IP structure,more services are possible.This brings about many new security requirements that traditional security programs cannot handle.This paper analyzes security threats and the needs of 3G/4G mobile networks,and then proposes a novel protection scheme for them based on their whole structure.In this scheme,a trusted computing environment is constructed on the mobile terminal side by combining software validity verification with access control.At the security management center,security services such as validity verification and integrity check are provided to mobile terminals.In this way,terminals and the network as a whole are secured to a much greater extent.This paper also highlights problems to be addressed in future research and development.展开更多
Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmenta...Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.展开更多
With the continuous expansion of digital infrastructures,malicious behaviors in host systems have become increasingly sophisticated,often spanning multiple processes and employing obfuscation techniques to evade detec...With the continuous expansion of digital infrastructures,malicious behaviors in host systems have become increasingly sophisticated,often spanning multiple processes and employing obfuscation techniques to evade detection.Audit logs,such as Sysmon,offer valuable insights;however,existing approaches typically flatten event sequences or rely on generic graph models,thereby discarding the natural parent-child process hierarchy that is critical for analyzing multiprocess attacks.This paper proposes a structure-aware threat detection framework that transforms audit logs into a unified two-dimensional(2D)spatio-temporal representation,where process hierarchy is modeled as the spatial axis and event chronology as the temporal axis.In addition,entropy-based features are incorporated to robustly capture obfuscated and non-linguistic strings,overcoming the limitations of semantic embeddings.The model’s performance was evaluated on publicly available datasets,achieving competitive results with an accuracy exceeding 95%and an F1-score of at least 0.94.The proposed approach provides a promising and reproducible solution for detecting attacks with unknown indicators of compromise(IoCs)by analyzing the relationships and behaviors of processes recorded in large-scale audit logs.展开更多
The global surge in electric vehicle(EV)adoption is proportionally expanding the EV charging station(EVCS)infrastructure,thereby increasing the attack surface and potential impact of security breaches within this crit...The global surge in electric vehicle(EV)adoption is proportionally expanding the EV charging station(EVCS)infrastructure,thereby increasing the attack surface and potential impact of security breaches within this critical ecosystem.While ISO 15118 standardizes EV-EVCS communication,its underspecified security guidelines and the variability in manufacturers’implementations frequently result in vulnerabilities that can disrupt charging services,compromise user data,or affect power grid stability.This research introduces a systematic black-box fuzzing methodology,accompanied by an open-source tool,to proactively identify and mitigate such security flaws in EVCS firmware operating under ISO 15118.The proposed approach systematically evaluates EVCS behavior by leveraging the state machine defined in the ISO 15118 standard for test case generation and execution,enabling platform-agnostic testing at the application layer.Message sequences,corresponding to valid andmutated traversals of the protocol’s state machine,are generated to uncover logical errors and improper input handling.Themethodology comprises state-aware initial sequence generation,simulated V2G session establishment,targeted message mutation correlated with defined protocol states,and rigorous response analysis to detect anomalies and system crashes.Experimental validation on an open-source EVCS implementation identified five vulnerabilities.These included session integrity weaknesses allowing unauthorized interruptions,billing manipulation through invalid metering data acceptance,and resource exhaustion vulnerabilities from specific parameter malformations leading to denial-of-service.The findings confirm the proposed method’s capability in pinpointing vulnerabilities often overlooked by standard conformance tests,thus offering a robust and practical solution for enhancing the security and resilience of the rapidly growing EV charging infrastructure.展开更多
The accelerated global adoption of electric vehicles(EVs)is driving significant expansion and increasing complexity within the EV charging infrastructure,consequently presenting novel and pressing cybersecurity challe...The accelerated global adoption of electric vehicles(EVs)is driving significant expansion and increasing complexity within the EV charging infrastructure,consequently presenting novel and pressing cybersecurity challenges.While considerable effort has focused on preventative cybersecurity measures,a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents,a gap exacerbated by system heterogeneity,distributed digital evidence,and inconsistent logging practices which hinder effective incident reconstruction and attribution.This paper addresses this critical need by proposing a novel,data-driven forensic framework tailored to the EV charging infrastructure,focusing on the systematic identification,classification,and correlation of diverse digital evidence across its physical,network,and application layers.Our methodology integrates open-source intelligence(OSINT)with advanced system modeling based on a three-layer cyber-physical system architecture to comprehensively map potential evidentiary sources.Key contributions include a comprehensive taxonomy of cybersecurity threats pertinent to EV charging ecosystems,detailed mappings between these threats and the resultant digital evidence to guide targeted investigations,the formulation of adaptable forensic investigation workflows for various incident scenarios,and a critical analysis of significant gaps in digital evidence availability within current EV charging systems,highlighting limitations in forensic readiness.The practical application and utility of this method are demonstrated through illustrative case studies involving both empirically-derived and virtual incident scenarios.The proposed datadriven approach is designed to significantly enhance digital forensic capabilities,support more effective incident response,strengthen compliance with emerging cybersecurity regulations,and ultimately contribute to bolstering the overall security,resilience,and trustworthiness of this increasingly vital critical infrastructure.展开更多
Delaying skin-aging through diet is a hot research topic in recent years,but the anti-aging effects of fish gelatin and related mechanisms are not well understood.In this study,we prepared edible fish gelatin from the...Delaying skin-aging through diet is a hot research topic in recent years,but the anti-aging effects of fish gelatin and related mechanisms are not well understood.In this study,we prepared edible fish gelatin from the swim bladder of Cynoscion acoupa using three different processing methods,namely dried(DCM),soaked(SCM)and instanted(ICM),to investigate its anti-aging effects and mechanisms on D-galactose induced skin aging in mice,as well as its effects on the gut microbiota.The results demonstrated that fish gelatin significantly increased water content,collagen,hyaluronic acid(HA)and hydroxyproline(Hyp)content,and skin integrity of mice skin,as well as enhanced the antioxidative ability and anti-inflammatory capacity of the skin.In terms of protein and mRNA expression levels in skin tissue,CMs treated with different treatments can up-regulate the expression of epidermal growth factor receptor(EGFR)and tissue inhibitor of metal protease 1(TIMP1),down-regulating the expression of matrix metalloproteinase 1(MMP1)and matrix metalloproteinase 3(MMP3),and increase the expression of collagen type III alpha 1 chain(COL3A1)and collagen type I alpha 2 chain(COL1A2).CMs attenuated the D-galactose-mediated inhibition collagen expression by stimulating the transforming growth factor beta(TGF-β)/Smad signaling pathway,thereby maintaining collagen matrix homeostasis.In addition,we revealed that CMs reversed gut microbiota by increase the abundance of intestinal flora.In conclusion,we demonstrated that CMs,especially for ICM,as an effective dietary supplement,have potential anti-aging and skin health benefits.展开更多
Ice krill is the keystone species in the neritic ecosystem in the Southern Ocean, where it replaces the more oceanic Antarctic krill. It is essential to understand the variation of target strength （TS in dB re l m^2...Ice krill is the keystone species in the neritic ecosystem in the Southern Ocean, where it replaces the more oceanic Antarctic krill. It is essential to understand the variation of target strength （TS in dB re l m^2） with the different body size to accurately estimate ice krill stocks. However, there is comparatively little knowledge of the acoustic backscatter of ice krill. The TS of individual, formalin-preserved, tethered ice krill was measured in a freshwater test tank at 38, 120, and 200 kHz with a calibrated split-beam echo sounder system. Mean TS was obtained from 21 individual ice krill with a broad range of body lengths （L： 13-36 iron）. The length （L, mm） to wet weight （W; mg） relationship for ice krill was 11/=0.001 21g^103~L35s （R2=0.96）. The mean TS-to-length relationship were TS38kHz=-177.4＋57log10（L）, （R^2=0.86）; TS120kHz= -129.9＋31.561ogf0（L）, （R2=0.87）; and TS200kHz=-117.6＋24.661ogre（L）, （R2=0.84）. Empirical estimates of the relationship between the TS and body length of ice krill were established at 38, 120, and 200 kHz and compared with predictions obtained from both the linear regression model of Greene et al. （1991） and the Stochastic Distorted Wave Born Approximation （SDWBA） model. This result might be applied to improve acoustic detection and density estimation of ice krill in the Southern Ocean. Further comparative studies are needed with in situ target strength including various body lengths of ice krill.展开更多
Certificateless public key cryptography （CL-PKC） avoids the inherent escrow of identity-based cryptography and does not require certificates to guarantee the authenticity of public keys. Based on CL-PKC, we present ...Certificateless public key cryptography （CL-PKC） avoids the inherent escrow of identity-based cryptography and does not require certificates to guarantee the authenticity of public keys. Based on CL-PKC, we present an efficient constant-round group key exchange protocol, which is provably secure under the intractability of computation Diffie-Hellman problem. Our protocol is a contributory key exchange with perfect forward secrecy and has only two communication rounds. So it is more efficient than other protocols. Moreover, our protocol provides a method to design efficient constant-round group key exchange protocols and most secret sharing schemes could be adopted to construct our protocol.展开更多
AIM To determine the predictive role of body mass index(BMI) and waist circumference(WC) for diabetes and prediabetes risk in future in total sample as well as in men and women separately. METHODS In a population base...AIM To determine the predictive role of body mass index(BMI) and waist circumference(WC) for diabetes and prediabetes risk in future in total sample as well as in men and women separately. METHODS In a population based cohort study, 1765 with mean ± SD age: 42.32 ± 6.18 healthy participants were followed up from 2003 till 2013(n = 960). Anthropometric and biochemical measures of participants were evaluated regularly during the follow up period. BMI and WC measures at baseline and diabetes and prediabetes status of participants at 2013 were determined. Multivariable logistic regression analysis was used for determining the risk of diabetes and prediabetes considering important potential confounding variables. Receiver operatingcharacteristic curve analysis was conducted to determine the best cut of values of BMI and WC for diabetes and prediabetes. RESULTS At 2013, among participants who had complete data, 45 and 307 people were diabetic and prediabetic, respectively. In final fully adjusted model, BMI value was a significant predictor of diabetes(RR = 1.39, 95%CI: 1.06-1.82 and AUC = 0.68, 95%CI: 0.59-0.75; P < 0.001) however not a significant risk factor for prediabetes. Also, WC was a significant predictor for diabetes(RR = 1.2, 95%CI: 1.05-1.38 and AUC = 0.67, 95%CI: 0.6-0.75) but not significant risk factor for prediabetes. Similar results were observed in both genders.CONCLUSION General and abdominal obesity are significant risk factors for diabetes in future.展开更多
Because the small CACHE size of computers, the scanning speed of DFA based multi-pattern string-matching algorithms slows down rapidly especially when the number of patterns is very large. For solving such problems, w...Because the small CACHE size of computers, the scanning speed of DFA based multi-pattern string-matching algorithms slows down rapidly especially when the number of patterns is very large. For solving such problems, we cut down the scanning time of those algorithms (i.e. DFA based) by rearranging the states table and shrinking the DFA alphabet size. Both the methods can decrease the probability of large-scale random memory accessing and increase the probability of continuously memory accessing. Then the hitting rate of the CACHE is increased and the searching time of on the DFA is reduced. Shrinking the alphabet size of the DFA also reduces the storage complication. The AC++algorithm, by optimizing the Aho-Corasick (i.e. AC) algorithm using such methods, proves the theoretical analysis. And the experimentation results show that the scanning time of AC++and the storage occupied is better than that of AC in most cases and the result is much attractive when the number of patterns is very large. Because DFA is a widely used base algorithm in may string matching algorithms, such as DAWG, SBOM etc., the optimizing method discussed is significant in practice.展开更多
The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such...The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information transmission.Due to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security threats.This paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario.Each UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative domain.After successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for storage.There are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,respectively.The security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution attack.It also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)assumption.The performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.展开更多
Radio frequency identification(RFID)has been widespread used in massive items tagged domains.However,tag collision increases both time and energy consumption of RFID network.Tag collision can seriously affect the succ...Radio frequency identification(RFID)has been widespread used in massive items tagged domains.However,tag collision increases both time and energy consumption of RFID network.Tag collision can seriously affect the success of tag identification.An efficient anti-collision protocol is very crucially in RFID system.In this paper,an improved binary search anti-collision protocol namely BRTP is proposed to cope with the tag collision concern,which introduces a Bi-response mechanism.In Bi-response mechanism,two groups of tags allowed to reply to the reader in the same slot.According to Bi-response mechanism,the BRTP strengthens the tag identification of RFID network by reducing the total number of queries and exchanged messages between the reader and tags.Both theoretical analysis and numerical results verify the effectiveness of the proposed BRTP in various performance metrics including the number of total slots,system efficiency,communication complexity and total identification time.The BRTP is suitable to be applied in passive RFID systems.展开更多
In Wireless Sensor Network(WSN),because battery and energy supply are constraints,sleep scheduling is always needed to save energy while maintaining connectivity for packet delivery.Traditional schemes have to ensure ...In Wireless Sensor Network(WSN),because battery and energy supply are constraints,sleep scheduling is always needed to save energy while maintaining connectivity for packet delivery.Traditional schemes have to ensure high duty cycling to ensure enough percentage of active nodes and then derogate the energy efficiency.This paper proposes an RFID based non-preemptive random sleep scheduling scheme with stable low duty cycle.It employs delay tolerant network routing protocol to tackle the frequent disconnections.A low-power RFID based non-preemptive wakeup signal is used to confirm the availability of next-hop before sending packet.It eliminates energy consumption of repeated retransmission of the delayed packets.Moreover,the received wakeup signal is postponed to take effect until the sleep period is finished,and the waken node then responds to the sending node to start the packet delivery.The scheme can keep stable duty cycle and then ensure energy saving effect compared with other sleeping scheduling methods.展开更多
A collective user web behavior simulation is an import means for generating a large-scale user network behavior in a network testbed or cyber range.Existing studies almost focus on individual web behavior analysis and...A collective user web behavior simulation is an import means for generating a large-scale user network behavior in a network testbed or cyber range.Existing studies almost focus on individual web behavior analysis and prediction,which cannot simulate human dynamics that widely exist in large-scale users’behaviors.To address these issues,we propose a novel collective user web behavior simulation method,in which an algorithm for constructing a connected virtual social network is proposed,and then a collective user web behavior simulation algorithm is designed on the virtual social network.In the simulation method,a new epidemic information dissemination algorithm based on the SIR model is proposed to drive the user web behavior with Breadth—First Search algorithm on the connected virtual social network.We specially build an experiment environment with 12 servers by using Docker container technology and then perform a wide range of experiments with different user scales to evaluate the method.The experimental results demonstrate that not only the degrees of the social network but also the time intervals of the collective users’web behavior can be well fitted to a power-law distribution and show that our simulation method can well simulate a collective user web behavior.展开更多
Using the hydroacoustic method with a 200 kHz scientific echo sounding system, the diel vertical migration （DVM） of the sound-scatteringlayer （SSL） in the Yellow Sea Bottom Cold Water （YSBCW） of the southeastern...Using the hydroacoustic method with a 200 kHz scientific echo sounding system, the diel vertical migration （DVM） of the sound-scatteringlayer （SSL） in the Yellow Sea Bottom Cold Water （YSBCW） of the southeastern Yellow Sea was studied in April （spring） and August （summer） of 2010 and 2011. For each survey, 13-27 hours of acoustic data were continuously collected at a stationary station. The acoustic volume scattering strength （Sv） data were analyzed with temperature profile data. In the spring of both 2010 and 2011, the SSL clearly showed the vertical migration throughout the entire water column, moving from the surface layer at night to near the bottom during the day. Conductivity, temperature, and depth data indicated that the entire water column was well mixed with low temperature of about 8℃. However, the SSL showed different patterns in the summers of 2010 and 2011. In the summer of 2010 （≈28℃ at the surface）, the SSL migrated to near the bottom during the day, but there were two SSLs above and below the thermocline at depth of 10-30 m at night. In the summer of 2011 （≈20℃ at the surface）, the SSL extended throughout the entire water column at night, possibly owing to an abrupt change in sea weather conditions caused by the passage of a Typhoon Muifa over the study area. It was cancluded that the DVM patterns in summer in the YSBCW area may be greatly influenced by a strengthened or weakened thermocline.展开更多
BACKGROUND The risk of developing prediabetes based on the metabolic/obesity phenotypes has been poorly investigated. AIM To examine the association of baseline metabolic/obesity phenotypes and their changes over time...BACKGROUND The risk of developing prediabetes based on the metabolic/obesity phenotypes has been poorly investigated. AIM To examine the association of baseline metabolic/obesity phenotypes and their changes over time with the risk of prediabetes development. METHODS In a population-based cohort study, 1741 adults (aged > 19 years) with normal blood glucose were followed for 14 years. Anthropometric and biochemical measures were evaluated regularly during the follow-up period. According to body mass index and metabolic health status, participants were categorized into four groups: Metabolically healthy normal weight (MHNW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUNW) and metabolically unhealthy obese (MUO). Multivariable Cox regression analysis was used to measure the risk of prediabetes according to the baseline metabolic/obesity phenotype and their changes during the follow-up. RESULTS In the whole population with a mean (95CCI for mean) follow up duration of 12.7 years (12.6-12.9), all three MUNW, MHO, MUO groups were at higher risk for developing prediabetes compared to the MHNW group (P = 0.022). The MUNW group had the highest risk for developing prediabetes (hazard ratio (HR): 3.84, 95%CI: 1.20, 12.27). In stratified analysis by sex, no significant association was found in men, while women in the MUNW group were at the greatest risk for prediabetes (HR: 6.74, 95%CI: 1.53, 29.66). Transforming from each phenotype to MHNW or MHO was not related to the risk of prediabetes development, whereas transforming from each phenotype to MUO was associated with an increased risk of prediabetes (HR > 1;P < 0.05). CONCLUSION Our findings indicate that MHO is not a high risk, unless it transforms into MUO over time. However, people in the MUNW group have the greatest risk for developing prediabetes, and therefore, they should be screened and treated.展开更多
With the enormous developmen t of imformation and communicat ion technology,more concerns are focused to achieve secure and reliable smart grids as the social infrastrueture,especially in the explosion era of mobile d...With the enormous developmen t of imformation and communicat ion technology,more concerns are focused to achieve secure and reliable smart grids as the social infrastrueture,especially in the explosion era of mobile devices.In this paper,we propose an efficient scheme to satis勿the outdoor electrical demand of mobile customers.Our scheme protects the privacy and integrity of users'electricity consumption data.Technically,we encrypt users'electricity consumption data by a chosen-plai nt ext-at tack(CPA)secure public key encryption(PKE)scheme and aggregate ciphertexts by the aggregator(an untrusted third party).In the scheme,internal and external adversaries cannot obtain the electricity consumption data.Additionally,we require users to provide authentication and commitment of consumption data that can track who modifies the data,which protects the integrity of users1 electricity consumption data.展开更多
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.
基金funded by the National High-Technology Research and Development Program of China"(863"Program)under Grant No.2009AA01Z427
文摘As mobile networks become high speed and attain an all-IP structure,more services are possible.This brings about many new security requirements that traditional security programs cannot handle.This paper analyzes security threats and the needs of 3G/4G mobile networks,and then proposes a novel protection scheme for them based on their whole structure.In this scheme,a trusted computing environment is constructed on the mobile terminal side by combining software validity verification with access control.At the security management center,security services such as validity verification and integrity check are provided to mobile terminals.In this way,terminals and the network as a whole are secured to a much greater extent.This paper also highlights problems to be addressed in future research and development.
基金Postgraduate Innovation Top notch Talent Training Project of Hunan Province,Grant/Award Number:CX20220045Scientific Research Project of National University of Defense Technology,Grant/Award Number:22-ZZCX-07+2 种基金New Era Education Quality Project of Anhui Province,Grant/Award Number:2023cxcysj194National Natural Science Foundation of China,Grant/Award Numbers:62201597,62205372,1210456foundation of Hefei Comprehensive National Science Center,Grant/Award Number:KY23C502。
文摘Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.
基金supported by the Nuclear Safety Research Program through Korea Foundation of Nuclear Safety(KoFONS)using the financial resource granted by the Nuclear Safety and Security Commission(NSSC)of the Republic of Korea(Grant number:2106061,50%)supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2025-25394739,Development of Security Enhancement Technology for Industrial Control Systems Based on S/HBOM Supply Chain Protection,50%).
文摘With the continuous expansion of digital infrastructures,malicious behaviors in host systems have become increasingly sophisticated,often spanning multiple processes and employing obfuscation techniques to evade detection.Audit logs,such as Sysmon,offer valuable insights;however,existing approaches typically flatten event sequences or rely on generic graph models,thereby discarding the natural parent-child process hierarchy that is critical for analyzing multiprocess attacks.This paper proposes a structure-aware threat detection framework that transforms audit logs into a unified two-dimensional(2D)spatio-temporal representation,where process hierarchy is modeled as the spatial axis and event chronology as the temporal axis.In addition,entropy-based features are incorporated to robustly capture obfuscated and non-linguistic strings,overcoming the limitations of semantic embeddings.The model’s performance was evaluated on publicly available datasets,achieving competitive results with an accuracy exceeding 95%and an F1-score of at least 0.94.The proposed approach provides a promising and reproducible solution for detecting attacks with unknown indicators of compromise(IoCs)by analyzing the relationships and behaviors of processes recorded in large-scale audit logs.
基金support of the Korea Internet&Security Agency(KISA)—Information Security Specialized University Support Project(50%)supported by a grant from the Korea Electric Power Corporation(R24XO01-4,50%)for basic research and development projects starting in 2024.
文摘The global surge in electric vehicle(EV)adoption is proportionally expanding the EV charging station(EVCS)infrastructure,thereby increasing the attack surface and potential impact of security breaches within this critical ecosystem.While ISO 15118 standardizes EV-EVCS communication,its underspecified security guidelines and the variability in manufacturers’implementations frequently result in vulnerabilities that can disrupt charging services,compromise user data,or affect power grid stability.This research introduces a systematic black-box fuzzing methodology,accompanied by an open-source tool,to proactively identify and mitigate such security flaws in EVCS firmware operating under ISO 15118.The proposed approach systematically evaluates EVCS behavior by leveraging the state machine defined in the ISO 15118 standard for test case generation and execution,enabling platform-agnostic testing at the application layer.Message sequences,corresponding to valid andmutated traversals of the protocol’s state machine,are generated to uncover logical errors and improper input handling.Themethodology comprises state-aware initial sequence generation,simulated V2G session establishment,targeted message mutation correlated with defined protocol states,and rigorous response analysis to detect anomalies and system crashes.Experimental validation on an open-source EVCS implementation identified five vulnerabilities.These included session integrity weaknesses allowing unauthorized interruptions,billing manipulation through invalid metering data acceptance,and resource exhaustion vulnerabilities from specific parameter malformations leading to denial-of-service.The findings confirm the proposed method’s capability in pinpointing vulnerabilities often overlooked by standard conformance tests,thus offering a robust and practical solution for enhancing the security and resilience of the rapidly growing EV charging infrastructure.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2023-00242528,50%)supported by a grant from the Korea Electric Power Corporation(R24XO01-4,50%)for basic research and development projects starting in 2024.
文摘The accelerated global adoption of electric vehicles(EVs)is driving significant expansion and increasing complexity within the EV charging infrastructure,consequently presenting novel and pressing cybersecurity challenges.While considerable effort has focused on preventative cybersecurity measures,a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents,a gap exacerbated by system heterogeneity,distributed digital evidence,and inconsistent logging practices which hinder effective incident reconstruction and attribution.This paper addresses this critical need by proposing a novel,data-driven forensic framework tailored to the EV charging infrastructure,focusing on the systematic identification,classification,and correlation of diverse digital evidence across its physical,network,and application layers.Our methodology integrates open-source intelligence(OSINT)with advanced system modeling based on a three-layer cyber-physical system architecture to comprehensively map potential evidentiary sources.Key contributions include a comprehensive taxonomy of cybersecurity threats pertinent to EV charging ecosystems,detailed mappings between these threats and the resultant digital evidence to guide targeted investigations,the formulation of adaptable forensic investigation workflows for various incident scenarios,and a critical analysis of significant gaps in digital evidence availability within current EV charging systems,highlighting limitations in forensic readiness.The practical application and utility of this method are demonstrated through illustrative case studies involving both empirically-derived and virtual incident scenarios.The proposed datadriven approach is designed to significantly enhance digital forensic capabilities,support more effective incident response,strengthen compliance with emerging cybersecurity regulations,and ultimately contribute to bolstering the overall security,resilience,and trustworthiness of this increasingly vital critical infrastructure.
基金supported by the China Agriculture Research System of MOF and MARA(CARS-21)Key-Area Research and Development Program of Guangdong Province(2022B0202050001).
文摘Delaying skin-aging through diet is a hot research topic in recent years,but the anti-aging effects of fish gelatin and related mechanisms are not well understood.In this study,we prepared edible fish gelatin from the swim bladder of Cynoscion acoupa using three different processing methods,namely dried(DCM),soaked(SCM)and instanted(ICM),to investigate its anti-aging effects and mechanisms on D-galactose induced skin aging in mice,as well as its effects on the gut microbiota.The results demonstrated that fish gelatin significantly increased water content,collagen,hyaluronic acid(HA)and hydroxyproline(Hyp)content,and skin integrity of mice skin,as well as enhanced the antioxidative ability and anti-inflammatory capacity of the skin.In terms of protein and mRNA expression levels in skin tissue,CMs treated with different treatments can up-regulate the expression of epidermal growth factor receptor(EGFR)and tissue inhibitor of metal protease 1(TIMP1),down-regulating the expression of matrix metalloproteinase 1(MMP1)and matrix metalloproteinase 3(MMP3),and increase the expression of collagen type III alpha 1 chain(COL3A1)and collagen type I alpha 2 chain(COL1A2).CMs attenuated the D-galactose-mediated inhibition collagen expression by stimulating the transforming growth factor beta(TGF-β)/Smad signaling pathway,thereby maintaining collagen matrix homeostasis.In addition,we revealed that CMs reversed gut microbiota by increase the abundance of intestinal flora.In conclusion,we demonstrated that CMs,especially for ICM,as an effective dietary supplement,have potential anti-aging and skin health benefits.
基金Supported by the Korea Polar Research Institute(No.PP14020)the Korea Institute of Ocean Science and Technology(No.PN65250)
文摘Ice krill is the keystone species in the neritic ecosystem in the Southern Ocean, where it replaces the more oceanic Antarctic krill. It is essential to understand the variation of target strength （TS in dB re l m^2） with the different body size to accurately estimate ice krill stocks. However, there is comparatively little knowledge of the acoustic backscatter of ice krill. The TS of individual, formalin-preserved, tethered ice krill was measured in a freshwater test tank at 38, 120, and 200 kHz with a calibrated split-beam echo sounder system. Mean TS was obtained from 21 individual ice krill with a broad range of body lengths （L： 13-36 iron）. The length （L, mm） to wet weight （W; mg） relationship for ice krill was 11/=0.001 21g^103~L35s （R2=0.96）. The mean TS-to-length relationship were TS38kHz=-177.4＋57log10（L）, （R^2=0.86）; TS120kHz= -129.9＋31.561ogf0（L）, （R2=0.87）; and TS200kHz=-117.6＋24.661ogre（L）, （R2=0.84）. Empirical estimates of the relationship between the TS and body length of ice krill were established at 38, 120, and 200 kHz and compared with predictions obtained from both the linear regression model of Greene et al. （1991） and the Stochastic Distorted Wave Born Approximation （SDWBA） model. This result might be applied to improve acoustic detection and density estimation of ice krill in the Southern Ocean. Further comparative studies are needed with in situ target strength including various body lengths of ice krill.
基金Supported by the National Natural Science Foundation of China (90204012, 60573035, 60573036) and the University IT Research Center Project of Korea
文摘Certificateless public key cryptography （CL-PKC） avoids the inherent escrow of identity-based cryptography and does not require certificates to guarantee the authenticity of public keys. Based on CL-PKC, we present an efficient constant-round group key exchange protocol, which is provably secure under the intractability of computation Diffie-Hellman problem. Our protocol is a contributory key exchange with perfect forward secrecy and has only two communication rounds. So it is more efficient than other protocols. Moreover, our protocol provides a method to design efficient constant-round group key exchange protocols and most secret sharing schemes could be adopted to construct our protocol.
文摘AIM To determine the predictive role of body mass index(BMI) and waist circumference(WC) for diabetes and prediabetes risk in future in total sample as well as in men and women separately. METHODS In a population based cohort study, 1765 with mean ± SD age: 42.32 ± 6.18 healthy participants were followed up from 2003 till 2013(n = 960). Anthropometric and biochemical measures of participants were evaluated regularly during the follow up period. BMI and WC measures at baseline and diabetes and prediabetes status of participants at 2013 were determined. Multivariable logistic regression analysis was used for determining the risk of diabetes and prediabetes considering important potential confounding variables. Receiver operatingcharacteristic curve analysis was conducted to determine the best cut of values of BMI and WC for diabetes and prediabetes. RESULTS At 2013, among participants who had complete data, 45 and 307 people were diabetic and prediabetic, respectively. In final fully adjusted model, BMI value was a significant predictor of diabetes(RR = 1.39, 95%CI: 1.06-1.82 and AUC = 0.68, 95%CI: 0.59-0.75; P < 0.001) however not a significant risk factor for prediabetes. Also, WC was a significant predictor for diabetes(RR = 1.2, 95%CI: 1.05-1.38 and AUC = 0.67, 95%CI: 0.6-0.75) but not significant risk factor for prediabetes. Similar results were observed in both genders.CONCLUSION General and abdominal obesity are significant risk factors for diabetes in future.
文摘Because the small CACHE size of computers, the scanning speed of DFA based multi-pattern string-matching algorithms slows down rapidly especially when the number of patterns is very large. For solving such problems, we cut down the scanning time of those algorithms (i.e. DFA based) by rearranging the states table and shrinking the DFA alphabet size. Both the methods can decrease the probability of large-scale random memory accessing and increase the probability of continuously memory accessing. Then the hitting rate of the CACHE is increased and the searching time of on the DFA is reduced. Shrinking the alphabet size of the DFA also reduces the storage complication. The AC++algorithm, by optimizing the Aho-Corasick (i.e. AC) algorithm using such methods, proves the theoretical analysis. And the experimentation results show that the scanning time of AC++and the storage occupied is better than that of AC in most cases and the result is much attractive when the number of patterns is very large. Because DFA is a widely used base algorithm in may string matching algorithms, such as DAWG, SBOM etc., the optimizing method discussed is significant in practice.
基金supported in part by the National Key R&D Program of China under Project 2020YFB1006004the Guangxi Natural Science Foundation under Grants 2019GXNSFFA245015 and 2019GXNSFGA245004+2 种基金the National Natural Science Foundation of China under Projects 62162017,61862012,61962012,and 62172119the Major Key Project of PCL under Grants PCL2021A09,PCL2021A02 and PCL2022A03the Innovation Project of Guangxi Graduate Education YCSW2021175.
文摘The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information transmission.Due to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security threats.This paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario.Each UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative domain.After successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for storage.There are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,respectively.The security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution attack.It also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)assumption.The performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.
基金This work was partially supported by the Key-Area Research and Development Program of Guangdong Province(2019B010136001,20190166)the Basic and Applied Basic Research Major Program for Guangdong Province(2019B030302002)the Science and Technology Planning Project of Guangdong Province LZC0023 and LZC0024.
文摘Radio frequency identification(RFID)has been widespread used in massive items tagged domains.However,tag collision increases both time and energy consumption of RFID network.Tag collision can seriously affect the success of tag identification.An efficient anti-collision protocol is very crucially in RFID system.In this paper,an improved binary search anti-collision protocol namely BRTP is proposed to cope with the tag collision concern,which introduces a Bi-response mechanism.In Bi-response mechanism,two groups of tags allowed to reply to the reader in the same slot.According to Bi-response mechanism,the BRTP strengthens the tag identification of RFID network by reducing the total number of queries and exchanged messages between the reader and tags.Both theoretical analysis and numerical results verify the effectiveness of the proposed BRTP in various performance metrics including the number of total slots,system efficiency,communication complexity and total identification time.The BRTP is suitable to be applied in passive RFID systems.
基金This work is supported in part by the National Natural Science Foundation of China(61871140,61572153,U1636215,61572492,61672020)the National Key research and Development Plan(Grant No.2018YFB0803504).
文摘In Wireless Sensor Network(WSN),because battery and energy supply are constraints,sleep scheduling is always needed to save energy while maintaining connectivity for packet delivery.Traditional schemes have to ensure high duty cycling to ensure enough percentage of active nodes and then derogate the energy efficiency.This paper proposes an RFID based non-preemptive random sleep scheduling scheme with stable low duty cycle.It employs delay tolerant network routing protocol to tackle the frequent disconnections.A low-power RFID based non-preemptive wakeup signal is used to confirm the availability of next-hop before sending packet.It eliminates energy consumption of repeated retransmission of the delayed packets.Moreover,the received wakeup signal is postponed to take effect until the sleep period is finished,and the waken node then responds to the sending node to start the packet delivery.The scheme can keep stable duty cycle and then ensure energy saving effect compared with other sleeping scheduling methods.
基金National Key Research and Development Plan under Grant 2017YFB0801804,Key Research and Development Plan of Shandong Province under Grant 2017CXGC0706Peng Cheng Laboratory Project of Guangdong Province PCL2018KP004+1 种基金frontier science and technology innovation of China under Grant 2016QY05X1002-2national regional innovation center scientific and technological special projects Grant 2017QYCX14,University Coconstruction Project in Weihai City.
文摘A collective user web behavior simulation is an import means for generating a large-scale user network behavior in a network testbed or cyber range.Existing studies almost focus on individual web behavior analysis and prediction,which cannot simulate human dynamics that widely exist in large-scale users’behaviors.To address these issues,we propose a novel collective user web behavior simulation method,in which an algorithm for constructing a connected virtual social network is proposed,and then a collective user web behavior simulation algorithm is designed on the virtual social network.In the simulation method,a new epidemic information dissemination algorithm based on the SIR model is proposed to drive the user web behavior with Breadth—First Search algorithm on the connected virtual social network.We specially build an experiment environment with 12 servers by using Docker container technology and then perform a wide range of experiments with different user scales to evaluate the method.The experimental results demonstrate that not only the degrees of the social network but also the time intervals of the collective users’web behavior can be well fitted to a power-law distribution and show that our simulation method can well simulate a collective user web behavior.
基金The China-Korea cooperative project"The study on the impact of the Yellow Sea Cold Water Mass to the ecosystem"under contract No.PE99165promoted by the Korea Institute of Ocean Science and Technology
文摘Using the hydroacoustic method with a 200 kHz scientific echo sounding system, the diel vertical migration （DVM） of the sound-scatteringlayer （SSL） in the Yellow Sea Bottom Cold Water （YSBCW） of the southeastern Yellow Sea was studied in April （spring） and August （summer） of 2010 and 2011. For each survey, 13-27 hours of acoustic data were continuously collected at a stationary station. The acoustic volume scattering strength （Sv） data were analyzed with temperature profile data. In the spring of both 2010 and 2011, the SSL clearly showed the vertical migration throughout the entire water column, moving from the surface layer at night to near the bottom during the day. Conductivity, temperature, and depth data indicated that the entire water column was well mixed with low temperature of about 8℃. However, the SSL showed different patterns in the summers of 2010 and 2011. In the summer of 2010 （≈28℃ at the surface）, the SSL migrated to near the bottom during the day, but there were two SSLs above and below the thermocline at depth of 10-30 m at night. In the summer of 2011 （≈20℃ at the surface）, the SSL extended throughout the entire water column at night, possibly owing to an abrupt change in sea weather conditions caused by the passage of a Typhoon Muifa over the study area. It was cancluded that the DVM patterns in summer in the YSBCW area may be greatly influenced by a strengthened or weakened thermocline.
文摘BACKGROUND The risk of developing prediabetes based on the metabolic/obesity phenotypes has been poorly investigated. AIM To examine the association of baseline metabolic/obesity phenotypes and their changes over time with the risk of prediabetes development. METHODS In a population-based cohort study, 1741 adults (aged > 19 years) with normal blood glucose were followed for 14 years. Anthropometric and biochemical measures were evaluated regularly during the follow-up period. According to body mass index and metabolic health status, participants were categorized into four groups: Metabolically healthy normal weight (MHNW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUNW) and metabolically unhealthy obese (MUO). Multivariable Cox regression analysis was used to measure the risk of prediabetes according to the baseline metabolic/obesity phenotype and their changes during the follow-up. RESULTS In the whole population with a mean (95CCI for mean) follow up duration of 12.7 years (12.6-12.9), all three MUNW, MHO, MUO groups were at higher risk for developing prediabetes compared to the MHNW group (P = 0.022). The MUNW group had the highest risk for developing prediabetes (hazard ratio (HR): 3.84, 95%CI: 1.20, 12.27). In stratified analysis by sex, no significant association was found in men, while women in the MUNW group were at the greatest risk for prediabetes (HR: 6.74, 95%CI: 1.53, 29.66). Transforming from each phenotype to MHNW or MHO was not related to the risk of prediabetes development, whereas transforming from each phenotype to MUO was associated with an increased risk of prediabetes (HR > 1;P < 0.05). CONCLUSION Our findings indicate that MHO is not a high risk, unless it transforms into MUO over time. However, people in the MUNW group have the greatest risk for developing prediabetes, and therefore, they should be screened and treated.
基金the National Natural Science Foundation of China(Nos.61632012,61672239 and 61602180)the Natural Science Foundation of Shanghai(No.16ZR1409200)the China Postdoctoral Science Foundation(No.2017M611502)。
文摘With the enormous developmen t of imformation and communicat ion technology,more concerns are focused to achieve secure and reliable smart grids as the social infrastrueture,especially in the explosion era of mobile devices.In this paper,we propose an efficient scheme to satis勿the outdoor electrical demand of mobile customers.Our scheme protects the privacy and integrity of users'electricity consumption data.Technically,we encrypt users'electricity consumption data by a chosen-plai nt ext-at tack(CPA)secure public key encryption(PKE)scheme and aggregate ciphertexts by the aggregator(an untrusted third party).In the scheme,internal and external adversaries cannot obtain the electricity consumption data.Additionally,we require users to provide authentication and commitment of consumption data that can track who modifies the data,which protects the integrity of users1 electricity consumption data.
