Efficient security level in wireless sensor networks (WSNs) using four-factors authentication over the Internet of Things (IoT).

With the increasing demand for the use of technology in all matters of daily life and business, the demand has increased dramatically to transform business electronically especially regards COVID-19. The Internet of Things (IoT) has greatly helped in accomplishing tasks. For example, at a high temperature, it would be possible to switch on the air conditioner using a personal mobile device while the person is in the car. The Internet of Things (IoT) eases lots of tasks. A wireless sensor network is an example of IoT. Wireless sensor network (WSN) is an infrastructure less self-configured that can monitor environmental conditions such as vibration, temperature, wind speed, sound, pressure, and vital signs. Thus, WSNs can occur in many fields. Smart homes give a good example of that. The security concern is important, and it is an essential requirement to ensure secure data. Different attacks and privacy concerns can affect the data. Authentication is the first defence line against threats and attacks. This study proposed a new protocol based on using four factors of authentication to improve the security level in WSN to secure communications. The simulation results prove the strength of the proposed method which reflects the importance of the usage of such protocol in authentication areas.

Outlier detection for keystroke biometric user authentication.

User authentication is a fundamental aspect of information security, requiring robust measures against identity fraud and data breaches. In the domain of keystroke dynamics research, a significant challenge lies in the reliance on imposter datasets, particularly evident in real-world scenarios where obtaining authentic imposter data is exceedingly difficult. This article presents a novel approach to keystroke dynamics-based authentication, utilizing unsupervised outlier detection techniques, notably exemplified by the histogram-based outlier score (HBOS), eliminating the necessity for imposter samples. A comprehensive evaluation, comparing HBOS with 15 alternative outlier detection methods, highlights its superior performance. This departure from traditional dependence on imposter datasets signifies a substantial advancement in keystroke dynamics research. Key innovations include the introduction of an alternative outlier detection paradigm with HBOS, increased practical applicability by reducing reliance on extensive imposter data, resolution of real-world challenges in simulating fraudulent keystrokes, and addressing critical gaps in existing authentication methodologies. Rigorous testing on Carnegie Mellon University's (CMU) keystroke biometrics dataset validates the effectiveness of the proposed approach, yielding an impressive equal error rate (EER) of 5.97%, a notable area under the ROC curve of 97.79%, and a robust accuracy (ACC) of 89.23%. This article represents a significant advancement in keystroke dynamics-based authentication, offering a reliable and efficient solution characterized by substantial improvements in accuracy and practical applicability.

Multi-level authentication for security in cloud using improved quantum key distribution.

Cloud computing is an on-demand virtual-based technology to develop, configure, and modify applications online through the internet. It enables the users to handle various operations such as storage, back-up, and recovery of data, data analysis, delivery of software applications, implementation of new services and applications, hosting websites and blogs, and streaming of audio and video files. Thereby, it provides us many benefits although it is backlashed due to problems related to cloud security like data leakage, data loss, cyber attacks, etc. To address the security concerns, researchers have developed a variety of authentication mechanisms. This means that the authentication procedure used in the suggested method is multi-levelled. As a result, a better QKD method is offered to strengthen cloud security against different types of security risks. Key generation for enhanced QKD is based on the ABE public key cryptography approach. Here, an approach named CPABE is used in improved QKD. The Improved QKD scored the reduced KCA attack ratings of 0.3193, this is superior to CMMLA (0.7915), CPABE (0.8916), AES (0.5277), Blowfish (0.6144), and ECC (0.4287), accordingly. Finally, this multi-level authentication using an improved QKD approach is analysed under various measures and validates the enhancement over the state-of-the-art models.

Fusing 1H NMR and Raman experimental data for the improvement of wine recognition models.

The present study proposes the development of new wine recognition models based on Artificial Intelligence (AI) applied to the mid-level data fusion of 1H NMR and Raman data. In this regard, a supervised machine learning method, namely Support Vector Machines (SVMs), was applied for classifying wine samples with respect to the cultivar, vintage, and geographical origin. Because the association between the two data sources generated an input space with a high dimensionality, a feature selection algorithm was employed to identify the most relevant discriminant markers for each wine classification criterion, before SVM modeling. The proposed data processing strategy allowed the classification of the wine sample set with accuracies up to 100% in both cross-validation and on an independent test set and highlighted the efficiency of 1H NMR and Raman data fusion as opposed to the use of a single-source data for differentiating wine concerning the cultivar and vintage.

Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review.

Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Because of the activities of adulterators in recent times, consumers are aware of what they eat. In the past, several methods were employed for the authentication of Halal meat, but numerous drawbacks are attached to this method such as lack of flexibility, limited application, time,consumption and low level of accuracy and sensitivity. Machine Learning (ML) is the concept of learning through the development and application of algorithms from given data and making predictions or decisions without being explicitly programmed. The techniques compared with traditional methods in Halal meat authentication are fast, flexible, scaled, automated, less expensive, high accuracy and sensitivity. Some of the ML approaches used in Halal meat authentication have proven a high percentage of accuracy in meat authenticity while other approaches show no evidence of Halal meat authentication for now. The paper critically highlighted some of the principles, challenges, successes, and prospects of ML approaches in the authentication of Halal meat.

Two-Layered Multi-Factor Authentication Using Decentralized Blockchain in an IoT Environment.

Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie-Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency.

NIR Sensing Technologies for the Detection of Fraud in Nuts and Nut Products: A Review.

Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts.

Regression Models for In Vivo Discrimination of the Iberian Pig Feeding Regime after Near Infrared Spectroscopy Analysis of Faeces.

The Iberian pig is a native breed of the Iberian Peninsula, which holds an international reputation due to the superior quality and the added value of its products. Different rearing practices and feeding regimes are regulated, resulting in different labelling schemes. However, there is no official analytical methodology that is standardised for certification purposes in the sector. Near Infrared Spectroscopy (NIRS) is a technology that provides information about the physicochemical composition of a sample, with several advantages that have enabled its implementation in different fields. Although it has already been successfully used for the analysis of Iberian pig's final products, samples evaluated with NIRS technology are characterised by a postmortem collection. The goal of this study was to evaluate the potential of NIRS analysis of faeces for in vivo discrimination of the Iberian pig feeding regime, using the spectral information per se for the development of modified partial least squares regressions. Faecal samples were used due to their easy collection, especially in extensive systems where pig handling is difficult. A total of 166 individual samples were collected from 12 farms, where the three different feeding regimes available in the sector were ensured. Although slight differences were detected depending on the chemometric approach, the best models obtained a classification success and a prediction accuracy of over 94% for feeding regime discrimination. The results are considered very satisfactory and suggest NIRS analysis of faeces as a promising approach for the in vivo discrimination of the Iberian pigs' diet, and its implementation during field inspections, a significative achievement for the sector.

The tin content of lead inclusions in ancient tin-bronze artifacts: a time-dependent process?

In antiquity, Pb was a common element added in the production of large bronze artifacts, especially large statues, to impart fluidity to the casting process. As Pb does not form a solid solution with pure Cu or with the Sn-Cu alloy phases, it is normally observed in the metal matrix as globular droplets embedded within or in interstitial positions among the crystals of Sn-bronze (normally the α phase) as the last crystallizing phase during the cooling process of the Cu-Sn-Pb ternary melt. The disequilibrium Sn content of the Pb droplets has recently been suggested as a viable parameter to detect modern materials [Shilstein, Berner, Feldman, Shalev & Rosenberg (2019). STAR Sci. Tech. Archaeol. Res. 5, 29-35]. The application assumes a time-dependent process, with a timescale of hundreds of years, estimated on the basis of the diffusion coefficient of Sn in Pb over a length of a few micrometres [Oberschmidt, Kim & Gupta (1982). J. Appl. Phys. 53, 5672-5677]. Therefore, Pb inclusions in recent Sn-bronze artifacts are actually a metastable solid solution of Pb-Sn containing ∼3% atomic Sn. In contrast, in ancient artifacts, unmixing processes and diffusion of Sn from the micro- and nano-inclusions of Pb to the matrix occur, resulting in the Pb inclusions containing a substantially lower or negligible amount of Sn. The Sn content in the Pb inclusions relies on accurate measurement of the lattice parameter of the phase in the Pb-Sn solid solution, since for low Sn values it closely follows Vegard's law. Here, several new measurements on modern and ancient samples are presented and discussed in order to verify the applicability of the method to the detection of modern artwork pretending to be ancient.

Analysis and authentication of avocado oil by low-field benchtop NMR spectroscopy and chemometrics.

Avocado oil is a nutritious, edible oil produced from avocado fruit. It has high commercial value and is increasing in popularity, thus powerful analytical methods are needed to ensure its quality and authenticity. Recent advancements in low-field (LF) NMR spectroscopy allow for collection of high-quality data despite the use of low magnetic fields produced by non-superconductive magnets. Combined with chemometrics, LF NMR opens new opportunities in food analysis using targeted and untargeted approaches. Here, it was used to determine poly-, mono-, and saturated fatty acids in avocado oil. Although direct signal integration of LF NMR spectra was able to determine certain classes of fatty acids, it had several challenges arising from signal overlapping. Thus, we used partial least square regression and developed models with good prediction performance for fatty acid composition, with residual prediction deviation ranging 3.46-5.53 and root mean squared error of prediction CV ranging 0.46-2.48. In addition, LF NMR, combined with unsupervised and supervised methods, enabled the differentiation of avocado oil from other oils, namely, olive oil, soybean oil, canola oil, high oleic (OL) safflower oil, and high OL sunflower oil. This study showed that LF NMR can be used as an efficient alternative for the compositional analysis and authentication of avocado oil. PRACTICAL APPLICATION: Here, we describe the application of LF-NMR for fatty acid analysis and avocado oil authentication. LF-NMR can be an efficient tool for targeted and untargeted analysis, thus becoming an attractive option for companies, regulatory agencies, and quality control laboratories. This tool is especially important for organizations and entities seeking economic, user-friendly, and sustainable analysis solutions.

Effectiveness of handheld near infrared spectrometer for traceability of Angus steaks.

The feasibility of Near Infrared Spectroscopy was assessed for aging traceability of steaks of Angus beef (Biceps femoris) individually vacuum-packaged, as well as for the prediction of the refrigeration storage time (0, 7, and 14 days). For this purpose, a total of 288 steaks homogeneously distributed among the sampling times were used. The model developed by Partial Least Squares-Discriminant Analysis offered high discrimination ability between aged beef vs. non-aged. The accuracy after external validation exceeded 90%. Regarding the predictive capacity of the storage time, it was greater on the set of aged samples, in which the accuracy achieved values higher than 96%, while the accuracy decreased to 75% for the non-aged samples. Results obtained support the ability of NIRS technology to be considered in any digital transformation strategy for traceability across the meat supply chain.

Recent advances in the application of direct analysis in real time-mass spectrometry (DART-MS) in food analysis.

Direct analysis in real time-mass spectrometry (DART-MS) has evolved as an effective analytical technique for the rapid and accurate analysis of food samples. The current advancements of DART-MS in food analysis are described in this paper. We discussed the DART principles, which include devices, ionization mechanisms, and parameter settings. Numerous applications of DART-MS in the fields of food and food products analysis published during 2018-2023 were reviewed, including contamination detection, food authentication and traceability, and specific analyte analysis in the food matrix. Furthermore, the challenges and limitations of DART-MS, such as matrix effect, isobaric component analysis, cost considerations and accessibility, and compound selectivity and identification, were discussed as well.

An integrative review of analytical techniques used in food authentication: A detailed description for milk and dairy products.

The use of suitable analytical techniques for the detection of adulteration, falsification, deliberate substitution, and mislabeling of foods has great importance in the industrial, scientific, legislative, and public health contexts. This way, this work reports an integrative review with a current analytical approach for food authentication, indicating the main analytical techniques to identify adulteration and perform the traceability of chemical components in processed and non-processed foods, evaluating the authenticity and geographic origin. This work presents results from a systematic search in Science Direct® and Scopus® databases using the keywords "authentication" AND "food", "authentication," AND "beverage", from published papers from 2013 to, 2024. All research and reviews published were employed in the bibliometric analysis, evaluating the advantages and disadvantages of analytical techniques, indicating the perspectives for direct, quick, and simple analysis, guaranteeing the application of quality standards, and ensuring food safety for consumers. Furthermore, this work reports the analysis of natural foods to evaluate the origin (traceability), and industrialized foods to detect adulterations and fraud. A focus on research to detect adulteration in milk and dairy products is presented due to the importance of these products in the nutrition of the world population. All analytical tools discussed have advantages and drawbacks, including sample preparation steps, the need for reference materials, and mathematical treatments. So, the main advances in modern analytical techniques for the identification and quantification of food adulterations, mainly milk and dairy products, were discussed, indicating trends and perspectives on food authentication.

The development of honey recognition models with broad applicability based on the association of isotope and elemental content with ANNs.

Honey adulteration represents a worldwide problem, driven by the illicit economic gain that producers, traders, or merchants pursue. Among the falsification methods that can unfairly influence the price is the incorrect declaration of the botanical origin and harvesting year. Therefore, the present study aimed to test the potential given by the application of Artificial Neural Networks (ANNs) for developing prediction models able to assess honey botanical origin and harvesting year based on isotope and elemental fingerprints. For each classification criterion, significant focus was dedicated to the data preprocessing phase to enhance the models' prediction capability. The obtained classification performances (accuracy scores >86% during the test phase) have highlighted the efficiency of ANNs for honey authentication as well as the feasibility of applying the developed classifiers for a large-scale application, supported by their ability to recognize the correct origin despite considerable variability in botanical source, geographical origin, and harvesting period.

Quantum Authentication Evolution: Novel Approaches for Securing Quantum Key Distribution.

This study introduces a novel approach to bolstering quantum key distribution (QKD) security by implementing swift classical channel authentication within the SARG04 and BB84 protocols. We propose mono-authentication, a pioneering paradigm employing quantum-resistant signature algorithms-specifically, CRYSTALS-DILITHIUM and RAINBOW-to authenticate solely at the conclusion of communication. Our numerical analysis comprehensively examines the performance of these algorithms across various block sizes (128, 192, and 256 bits) in both block-based and continuous photon transmission scenarios. Through 100 iterations of simulations, we meticulously assess the impact of noise levels on authentication efficacy. Our results notably highlight CRYSTALS-DILITHIUM's consistent outperformance of RAINBOW, with signature overheads of approximately 0.5% for the QKD-BB84 protocol and 0.4% for the QKD-SARG04 one, when the quantum bit error rate (QBER) is augmented up to 8%. Moreover, our study unveils a correlation between higher security levels and increased authentication times, with CRYSTALS-DILITHIUM maintaining superior efficiency across all key rates up to 10,000 kb/s. These findings underscore the substantial cost and complexity reduction achieved by mono-authentication, particularly in noisy environments, paving the way for more resilient and efficient quantum communication systems.

Fingerprint authentication based on deep convolutional descent inversion tomography.

Fingerprint authentication is widely used in various areas. While existing methods effectively extract and match fingerprint features, they encounter difficulties in detecting wet fingers and identifying false minutiae. In this paper, a fast fingerprint inversion and authentication method based on Lamb waves is developed by integrating deep learning and multi-scale fusion. This method speeds up the inversion performance through deep fast inversion tomography (DeepFIT) and uses Mask R-CNN to improve authentication accuracy. DeepFIT utilizes fully connected and convolutional operations to approach the descent gradient, enhancing the efficiency of ultrasonic array reconstruction. This suppresses artifacts and accelerates sub-millimeter-level fingerprint minutia inversion. By identifying the overall morphological relationships of various minutia in fingerprints, meaningful minutia representing individual identities are extracted by the Mask R-CNN method. It segments and matches multi-scale fingerprint features, improving the reliability of authentication results. Results indicate that the proposed method has high accuracy, robustness, and speed, optimizing the entire fingerprint authentication process.

NMR-based approach to detect white wine vinegar fraud.

Low-Field Nuclear Magnetic Resonance (LF-NMR) can be a valid tool in food fingerprint analyses to detect commercial frauds. Thus, the work aims at exploring the potential of LF-NMR, coupled with chemometrics, in discriminating authentic white wine vinegars from products adulterated with alcohol vinegars (i.e., 5-25% v/v adulteration levels). The monodimensional spectra and transverse relaxation times (T2) of 88 samples, including 32 authentic vinegars and 56 adulterated samples, were collected. Three different spectral regions were investigated (i.e., 3.75-0.90, 3.75-2.00, and 1.50-0.90 ppm) and, for each, fifteen variables were selected from the pretreated monodimensional spectra. Linear Discriminant Analysis (LDA) on monodimensional spectra in the range 3.75-0.90 ppm gave 100% correct classification of authentic and adulterated vinegars in prediction, whereas LDA models developed with acetic acid or water T2 failed. In conclusion, LF-NMR spectra can be effectively used to detect, in a rapid and non-destructive way, white wine vinegar adulteration with alcohol vinegar.

Validation of a simplified small-scale DNA extraction protocol from wine by quantitative real-time PCR.

In the present study, we compared a simplified small-scale purification protocol to obtain DNA admixtures out of wine, with our large-scale published method. The extraction methods must provide DNA free of PCR inhibitors, that can interfere with DNA amplification. To evaluate the efficiency of grapevine's nuclear DNA extraction from wine, the new protocol was also compared in terms of purity and yield to the DNA obtained out of grapevine's (Vitis vinifera) leaf tissue, using a commercial kit. Two single-copy nuclear genes, nine-cis-epoxy carotenoid dioxygenase 2 (NCED2), and prefoldin subunit 5-like (PS5) were amplified in DNA extracted from wine and grapevine by real-time TaqMan PCR to determine the presence of inhibitors in relation to the diversity of starting biological matrix. This study showed that the small-scale, simpler method for extracting DNA from wine produced effective results in terms of inhibitor presence and purity. Furthermore, even though the initial biological matrix was more complicated, the grapevine nuclear DNA that was removed from wine was qualitatively equivalent to the DNA that was isolated from the leaves. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-024-03992-x.

Two-Dimensional High-Performance Thin-Layer Chromatography with Bioautography for Distinguishing Angelicae Dahuricae Radix Varieties: Chemical Fingerprinting and Antioxidant Profiling.

Angelicae Dahuricae Radix (ADR) holds a prominent place in traditional medicine for its remarkable antioxidative, anti-allergic, and antiproliferative capabilities. Recognized within the Korean Pharmacopoeia (KP 12th), Angelica dahurica (Hoffm.) Benth. and Hook.f. ex Franch. and Sav. (AD) and Angelica dahurica var. formosana (H. Boissieu) Yen (ADF) serve as the botanical origins for ADR. Differentiating these two varieties is crucial for the formulation and quality control of botanical drugs, as they are categorized under the same medicinal label. This research utilized two-dimensional high-performance thin-layer chromatography (2D-HPTLC) to effectively distinguish AD from ADF. Additionally, a quantitative analysis reveals significant differences in the concentrations of key active constituents such as oxypeucedanin, imperatorin, and isoimperatorin, with AD showing higher total coumarin levels. We further enhanced our investigative depth by incorporating a DPPH bioautography, which confirmed known antioxidant coumarins and unearthed previously undetected antioxidant profiles, including byakangelicin, byakangelicol, falcarindiol in both AD and ADF, and notably, 2-linoleoyl glycerol detected only in AD as an antioxidant spot. This comprehensive approach affords a valuable tool set for botanical drug development, emphasizing the critical need for accurate source plant identification and differentiation in ensuring the efficacy and safety of herbal medicine products.