CMAES-WFD:Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy

Website fingerprinting,also known asWF,is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination,even when using the Tor anonymity network.While advanced attacks based on deep neural network(DNN)can performfeature engineering and attain accuracy rates of over 98%,research has demonstrated thatDNNis vulnerable to adversarial samples.As a result,many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success.However,these methods suffer from high bandwidth overhead or require access to the target model,which is unrealistic.This paper proposes CMAES-WFD,a black-box WF defense based on adversarial samples.The process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy(CMAES)optimization algorithm.Perturbations are injected into the local parts of the original traffic to control bandwidth overhead.According to the experiment results,CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting(DF)and VarCnn to below 8.3%and the bandwidth overhead to a maximum of only 14.6%and 20.5%,respectively.Specially,for Automated Website Fingerprinting(AWF)with simple structure,CMAES-WFD reduced the classification accuracy to only 6.7%and the bandwidth overhead to less than 7.4%.Moreover,it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.

Improving Optimal Fingerprinting Methods Requires a Viewpoint beyond Statistical Science

While being successful in the detection and attribution of climate change,the optimal fingerprinting method(OFM)may have some limitations from a physics-and-dynamics-based viewpoint.Here,an analysis is made on the linearity,noninteraction,and stationary-variability assumptions adopted by OFM.It is suggested that furthering OFM needs a viewpoint beyond statistical science,and the method should be combined with theoretical tools in the dynamics and physics of the Earth system,so as to be applied for the detection and attribution of nonlinear climate change including tipping elements within the Earth system.

Certis: Cloud Asset Management & Threat Evaluation Using Behavioral Fingerprinting at Application Layer

This paper introduces Certis, a powerful framework that addresses the challenges of cloud asset tracking, management, and threat detection in modern cybersecurity landscapes. It enhances asset identification and anomaly detection through SSL certificate parsing, cloud service provider integration, and advanced fingerprinting techniques like JARM at the application layer. Current work will focus on cross-layer malicious behavior identification to further enhance its capabilities, including minimizing false positives through AI-based learning techniques. Certis promises to offer a powerful solution for organizations seeking proactive cybersecurity defenses in the face of evolving threats.

Geographical origin identification of winter jujube(Ziziphus jujuba Dongzao')by using multi-element fingerprinting with chemometrics

Winter jujube(Ziziphus jujuba'Dongzao')is greatly appreciated by consumers for its excellent quality,but brand infringement frequently occurs in the market.Here,we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer(ICP-MS).As a result,16 elements(Mg,K,Mn,Cu,Zn,Mo,Ba,Be,As,Se,Cd,Sb,Ce,Er,Tl,and Pb)exhibited significant differences in samples from different producing areas.Supervised linear discriminant analysis(LDA)and orthogonal projection to latent structures discriminant analysis(OPLS-DA)showed better performance in identifying the origin of samples than unsupervised principal component analysis(PCA).LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64%in the testing set,respectively.By using the multilayer perceptron(MLP)and C5.0,the prediction accuracy of the models could reach 96.36 and 91.06%,respectively.Based on the above four chemometric methods,Cd,Tl,Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube.Overall,this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics,and may also provide reference for establishing the origin traceability system of other fruits.

A UWB/IMU-Assisted Fingerprinting Localization Framework with Low Human Efforts

With the rapid development of smart phone,the location-based services(LBS)have received great attention in the past decades.Owing to the widespread use of WiFi and Bluetooth devices,Received Signal Strength Indication(RSSI)fingerprintbased localization method has obtained much development in both academia and industries.In this work,we introduce an efficient way to reduce the labor-intensive site survey process,which uses an UWB/IMU-assisted fingerprint construction(UAFC)and localization framework based on the principle of Automatic radio map generation scheme(ARMGS)is proposed to replace the traditional manual measurement.To be specific,UWB devices are employed to estimate the coordinates when the collector is moved in a reference point(RP).An anchor self-localization method is investigated to further reduce manual measurement work in a wide and complex environment,which is also a grueling,time-consuming process that is lead to artificial errors.Moreover,the measurements of IMU are incorporated into the UWB localization algorithm and improve the label accuracy in fingerprint.In addition,the weighted k-nearest neighbor(WKNN)algorithm is applied to online localization phase.Finally,filed experiments are carried out and the results confirm the effectiveness of the proposed approach.

The significance of metabolic fingerprinting in carcinogenesis

Carcinogenesis describes the process through which normal cells transform into malignant cells(cancer).There were an estimated 18.1 million new cases of cancer(all cancers combined excluding non-melanoma skin cancer)worldwide in 2020:8.8 million(48%)in females and 9.3 million(52%)in males,giving a male:female ratio of 10:9.5.It may be initiated by the action of biological,physical,or chemical agents that cause a non-lethal,permanent,DNA error on the cell with a consequence of altered cell metabolism.This altered cell metabolism include the Warburg effect,altered lipid and amino acid metabolism and production of various metabolites.It also results in unique metabolic dependencies that,in some cases,can be targeted with precision medicine and nutrition,including drugs that selectively target metabolic enzymes.Metabolic fingerprinting has been applied to the study of carcinogenesis and is particularly helpful in early diagnosis,staging and choice of treatment,thus improving health outcomes.This technology could therefore be harnessed effectively while combining with other omics technologies.

Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study

Herbal medicines are popular natural medicines that have been used for decades.The use of alternative medicines continues to expand rapidly across the world.The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications,as their therapeutic potential varies between different geographic origins,plant species,and varieties.Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach.Their quality should be considered based on a complete metabolic profile,as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds.A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization.In this study,a rapid and effective quality assessment system for geographical traceability,species,and variety-specific authenticity of the widely used natural medicines turmeric,Ocimum,and Withania somnifera was investigated using Fourier transform near-infrared(FT-NIR)spectroscopy-based metabolic fingerprinting.Four different geographical origins of turmeric,five different Ocimum species,and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches.Extremely good discrimination(R^(2)>0.98,Q^(2)>0.97,and accuracy=1.0)with sensitivity and specificity of 100%was achieved using this metabolic fingerprinting strategy.Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.

Radio Frequency Fingerprinting Identification Using Semi-Supervised Learning with Meta Labels

Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF identification by leveraging the hardware-level features.However,traditional supervised learning methods require huge labeled training samples.Therefore,how to establish a highperformance supervised learning model with few labels under practical application is still challenging.To address this issue,we in this paper propose a novel RFF semi-supervised learning(RFFSSL)model which can obtain a better performance with few meta labels.Specifically,the proposed RFFSSL model is constituted by a teacher-student network,in which the student network learns from the pseudo label predicted by the teacher.Then,the output of the student model will be exploited to improve the performance of teacher among the labeled data.Furthermore,a comprehensive evaluation on the accuracy is conducted.We derive about 50 GB real long-term evolution(LTE)mobile phone’s raw signal datasets,which is used to evaluate various models.Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97%experimental testing accuracy over a noisy environment only with 10%labeled samples when training samples equal to 2700.

CNN-Based RF Fingerprinting Method for Securing Passive Keyless Entry and Start System

The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety.One key system that needs protection is the passive key entry system(PKES).To prevent attacks aimed at defeating the PKES,we propose a novel radio frequency(RF)fingerprinting method.Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal.This feature is then analyzed using a convolutional neural network(CNN)for device identification.In evaluation,we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model.Our experimental results revealed that the Gammatone Frequency Cepstral Coefficient(GFCC)was the most compelling feature compared to Mel-Frequency Cepstral Coefficient(MFCC),Inverse Mel-Frequency Cepstral Coefficient(IMFCC),Linear-Frequency Cepstral Coefficient(LFCC),and Bark-Frequency Cepstral Coefficient(BFCC).Additionally,we experimented with evaluating the effectiveness of our method in comparison to existing approaches that are similar to ours.

A Personalized Digital Code from Unique Genome Fingerprinting Pattern for Use in Identification and Application on Blockchain

With over 10 million points of genetic variation from person to person, every individual’s genome is unique and provides a highly reliable form of identification. This is because the genetic code is specific to each individual and does not change over time. Genetic information has been used to identify individuals in a variety of contexts, such as criminal investigations, paternity tests, and medical research. In this study, each individual’s genetic makeup has been formatted to create a secure, unique code that incorporates various elements, such as species, gender, and the genetic identification code itself. The combinations of markers required for this code have been derived from common single nucleotide polymorphisms (SNPs), points of variation found in the human genome. The final output is in the form of a 24 numerical code with each number having three possible combinations. The custom code can then be utilized to create various modes of identification on the decentralized blockchain network as well as personalized services and products that offer users a novel way to uniquely identify themselves in ways that were not possible before.

Plastid Inheritance in Sweet Potato as Revealed by DNA Restriction Fingerprinting

The inheritance of chloroplast DNA (cpDNA) in sweet potato (Ipomoea batatas Lain.) was analyzed using DNA restriction fingerprinting. The cpDNA fingerprints of hybrids from reciprocal crosses between Xushu18 and AB78-1 were found to be identical to those of their female parents, which reveals that cpDNA of sweet potato is maternally inherited in this intervarietal crossing. This maternal cpDNA transmission pattern does not accord with the putative one based on former cytological studies. The plastid inheritance in Convolvulaceae has been briefly reviewed in this study, and the utility of DNA restriction fingerprinting analysis in the study of plastid inheritance is also discussed.

A Molecular Approach to Identification of the Chinese Drug“pu Gong Ying”(Herba Taraxaci)and Six Adulterants byDNA Fingerprinting Using Random Primed PolymeraseChain Resaction(PCR)

DNA fingerprinting among members of the Chinese drug Pu Gong Ying(Taraxacum mongolicum Hand,-Mazz.)and six adulterants of Tu Gong Ying were demonstrated with random-primed polymerase chain reaction(PCR)including arbitrarily primed polymerase chain reaction(AP-PCR)and random amplified polymorphic DNA(RAPD).Distinctive,reproducible genomic fingerprints from DNA from 7 species belonged to Compositae were generated with two long(20 and 24 mer)and one short(10 mer)randomly chosen primers.The Pu Gong Ying can be differentiated from six species of Tu Gong Ying according to the banding pattems of their amplified DNA on agarose gels.The results showed that AP-PCR and RAPD methods can be used for identifying Chinese drugs.Moreover,the Similarity Indexes of the genomic DNA fingerprints showed that Pu Gong Ying and its adulterants are unrelated.Therefore,AP-PCR and RAPD methods can be used for identifying Chinese drugs.

WEIGHTED ALGORITHM FOR FINGERPRINTING RELATIONAL DATABASES

The necessity and the feasibility of introducing attribute weight into digital fingerprinting system are given. The weighted algorithm for fingerprinting relational databases of traitor tracing is proposed. Higher weights are assigned to more significant attributes, so important attributes are more frequently fingerprinted than other ones. Finally, the robustness of the proposed algorithm, such as performance against collusion attacks, is analyzed. Experimental results prove the superiority of the algorithm.

SSR fingerprinting of 203 sweetpotato (Ipomoea batatas (L.) Lam.) varieties

Simple sequence repeat （SSR） markers have been shown to be a powerful tool for varieties identification in plants. How- ever, SSR fingerprinting of sweetpotato varieties has been a little reported. In this study, a total of 1 294 SSIR primer pairs, including 1 215 genomic-SSR and 79 expressed sequence tag （EST）-SSR primer pairs, were screened with sweetpotato varieties Zhengshu 20 and Luoxushu 8 and their 2 F1 individuals randomly sampled, and 273 and 38 of them generated polymorphic bands, respectively. Four genomic-SSR and 3 EST-SSR primer pairs, which showed good polymorphism, were selected to amplify 203 sweetpotato varieties and gave a total of 172 bands, 85 （49.42%） of which were polymorphic. All of the 203 sweetpotato varieties showed unique fingerprint patterns, indicating the utility of SSR markers in variety iden- tification of this crop. Polymorphism information content （PIC） ranged from 0.5824 to 0.9322 with an average of 0.8176. SSR-based genetic distances varied from 0.0118 to 0.6353 with an average of 0.3100 among these varieties. Thus, these sweetpotato varieties exhibited high levels of genetic similarity and had distinct fingerprint profiles. The SSR fingerprints of the 203 sweetpotato varieties have been successfully constructed. The highly polymorphic SSR primer pairs developed in this study have the potential to be used as core primer pairs for variety identification, genetic diversity assessment and linkage map construction in sweetpotato and other plants.

Fingerprinting 146 Chinese chestnut(Castanea mollissima Blume)accessions and selecting a core collection using SSR markers

Chinese chestnut is an important nut tree around the world.Although the types of Chinese chestnut resources are abundant,resource utilization and protection of chestnut accessions are still very limited.Here,we fingerprinted and determined the genetic relationships and core collections of Chinese chestnuts using 18 fluorescently labeled SSR markers generated from 146 chestnut accessions.Our analyses showed that these markers from the tested accessions are highly polymorphic,with an average allele number(N_(a))and polymorphic information content(PIC)of 8.100 and 0.622 per locus,respectively.Using these strongly distinguishing markers,we successfully constructed unique fingerprints for 146 chestnut accessions and selected seven of the SSR markers as core markers to rapidly distinguish different accessions.Our exploration of the genetic relationships among the five cultivar groups indicated that Chinese chestnut accessions are divided into three regional type groups:group I(North China(NC)and Northwest China(NWC)cultivar groups),group II(middle and lower reaches of the Yangtze River(MLY)cultivar group)and group III(Southeast China(SEC)and Southwest China(SWC)cultivar groups).Finally,we selected 45 core collection members which represent the most genetic diversity of Chinese chestnut accessions.This study provides valuable information for identifying chestnut accessions and understanding the phylogenetic relationships among cultivar groups,which can serve as the basis for efficient breeding in the future.

AFLP Fingerprinting and Genetic Diversity of Main Sweetpotato Varieties in China

AFLP fingerprinting of the 98 main sweetpotato varieties planted in China has been constructed. Using 17 AFLP primer combinations which were selected from 1 208 primer combinations and generated the most amounts of polymorphic bands, AFLP analysis of the 98 main sweetpotato varieties gave a total of 410 clear polymorphic bands with an average of 24.12 polymorphic bands per primer combination. Each one of the 98 sweetpotato varieties could be clearly distinguished by EcoR I-cta/Mse I-ggc primer combination which generated the most polymorphic bands. AFLP-based genetic distance ranged from 0.0546 to 0.5709 with an average of 0.3799. The dendrogram based on AFLP markers indicated that sweetpotato varieties coming from the same regions or having same parents were clustered in the same groups. Analysis of molecular variance （AMOVA） revealed greater variations within regions （94.08%） than among regions （5.92%）. Thus, the genetic variations mainly existed within regions, while the variations among regions were very low in the tested sweetpotato varieties. Significant genetic variations existed between ＂Northern＂ and ＂Southern＂ sweetpotato varieties when Yangtze River was used as the dividing line.

Identification of Porphyra lines using computerized DNA fingerprinting

RAPD (Randomly Amplified Polymorphic DNA) analysis was performed with filaments of 15 Porphyra lines representing four important groups (P. yezoensis, P. haitanensis, P. katadai var. Hemiphylla and P. digospermatangia). Eight stable and repeatable RAPD bands amplified with two primers, OPN-02 and OPJ-18, were selected for the construction of DNA fingerprinting. The RAPD results were scored based on the presence or absence of each of the 8 bands and then converted to computer language expressed with two digitals, 1 and 0, which represented the presence (numbered as 1) or absence (numbered as 0) of each band, respectively. Based on these results, a model DNA fingerprint and a computerized DNA fingerprint were constructed. In the constructed DNA fingerprint, each Porphyra line has its unique fingerprinting pattern and can be easily distinguished from each other. Later, a software, named as PhGI, was designed based on this DNA fingerprinting. It can be used in practical Porphyra line identification.

Authentication of Cassia seeds on the basis of two-wavelength HPLC fingerprinting with the use of chemometrics

High performance liquid chromatographic(HPLC) fingerprints of Cassia seed,a traditional Chinese medicine(TCM),were developed by means of the chromatograms at two wavelengths of 238 and 282 nm.Then,the two data sets were combined into one matrix.The application of principal component analysis(PCA) for this data matrix showed that the samples were clustered into four groups in accordance with the plant sources and preparation procedures.Furthermore,partial least squares(PLS),back propagation artificial neural...

An ultra-robust fingerprinting method for quality assessment of traditional Chinese medicine using multiple reaction monitoring mass spectrometry

Chromatographic fingerprinting has been perceived as an essential tool for assessing quality and chemical equivalence of traditional Chinese medicine.However,this pattern-oriented approach still has some weak points in terms of chemical coverage and robustness.In this work,we proposed a multiple reaction monitoring(MRM)-based fingerprinting method in which approximately 100 constituents were simultaneously detected for quality assessment.The derivative MRM approach was employed to rapidly design MRM transitions independent of chemical standards,based on which the large-scale fingerprinting method was efficiently established.This approach was exemplified on QiShenYiQi Pill(QSYQ),a traditional Chinese medicine-derived drug product,and its robustness was systematically evaluated by four indices:clustering analysis by principal component analysis,similarity analysis by the congruence coefficient,the number of separated peaks,and the peak area proportion of separated peaks.Compared with conventional ultraviolet-based fingerprints,the MRM fingerprints provided not only better discriminatory capacity for the tested normal/abnormal QSYQ samples,but also higher robustness under different chromatographic conditions(i.e.,flow rate,apparent pH,column temperature,and column).The result also showed for such large-scale fingerprints including a large number of peaks,the angle cosine measure after min-max normalization was more suitable for setting a decision criterion than the unnormalized algorithm.This proof-of-concept application gives evidence that combining MRM technique with proper similarity analysis metrices can provide a highly sensitive,robust and comprehensive analytical approach for quality assessment of traditional Chinese medicine.

Comparison of DUS testing and SNP fingerprinting for variety identification in cucumber

Variety identification plays an important role in protecting the intellectual property of varieties,ensuring seed quality,and encouraging breeding innovation.Currently,morphological evaluation in the field,such as distinctness,uniformity,and stability(DUS)testing,and DNA fingerprinting in the laboratory using molecular markers are two dominant methods used for variety identification.Few studies have compared the results of these approaches,and the relationship between the two methods is obscure.In this study,134 dominant cucumber varieties were evaluated using 50 DUS testing traits and genotyped by 40 single nucleotide polymorphisms(SNPs).The 40 SNPs were developed in our previous study and arewell suited for variety identification.In the DUS testing,significant positive or negative correlations among 50 DUS traits were observed,and 20 core traits,including 15 fruit traits,were further selected to increase field inspection efficiency.This suggested that fruit shape plays an important role in variety identification.The ratio of fruit length/diameter was themost important trait,explaining 9.2%of the phenotypic variation.In the DNA fingerprinting test,the 40 SNPs were highly polymorphic and could distinguish all of the 134 cucumber varieties,and 14 core SNPs were selected to improve the identification rate.Interestingly,the population structure analysis of 134 cucumber varieties by phenotypic data in the DUS test was in accordance with the genotypic data from the DNA fingerprinting,indicating that all varieties could be divided into the same four subgroups:European type,North China type,South China type,and hybrids of the North China and South China types.Moreover,linear correlativity of distinguishment for each pair of varieties was observed between the DUS test and the DNA fingerprinting.These results indicated that these two methods have good application in future research,especially for the scaled-up analysis of hundreds of varieties.