Developed Detection Method Research Articles

Identifying vulnerabilities of industrial control systems using evolutionary multiobjective optimisation

In this paper, we propose a novel methodology to assist in identifying vulnerabilities in real-world complex heterogeneous industrial control systems (ICS) using two Evolutionary Multiobjective Optimisation (EMO) algorithms, NSGA-II and SPEA2. Our approach is evaluated on a well-known benchmark chemical plant simulator, the Tennessee Eastman (TE) process model. We identified vulnerabilities in individual components of the TE model and then made use of these vulnerabilities to generate combinatorial attacks. The generated attacks were aimed at compromising the safety of the system and inflicting economic loss. Results were compared against random attacks, and the performance of the EMO algorithms was evaluated using hypervolume, spread, and inverted generational distance (IGD) metrics. A defence against these attacks in the form of a novel intrusion detection system was developed, using machine learning algorithms. The designed approach was further tested against the developed detection methods. The obtained results demonstrate that the developed EMO approach is a promising tool in the identification of the vulnerable components of ICS, and weaknesses of any existing detection systems in place to protect the system. The proposed approach can serve as a proactive defense tool for control and security engineers to identify and prioritise vulnerabilities in the system. The approach can be employed to design resilient control strategies and test the effectiveness of security mechanisms, both in the design stage and during the operational phase of the system.

Fe-N-C single-atom nanozyme-linked immunosorbent assay for quantitative detection of aflatoxin B1

Aflatoxin B1 (AFB1) is a prominent food contaminant known for its high toxicity. Rapid, low-cost, and sensitive detection of AFB1 is an urgent demand for food safety risk control. In the traditional enzyme-linked immunosorbent assay (ELISA) method for AFB1 detection, horseradish peroxide (HRP) is high-cost and possesses poor environmental stability. In this study, a direct competitive single-atom nanozymes (SAzymes) -linked immunosorbent assay was developed based on Fe-N-C SAzymes. The proposed methods exhibited excellent sensitivity, rapid detection, fabrication, and low cost. Under the optimal conditions, the limit of detection of the new assay was 3.3 pg mL−1, with a quantitative relationship between 0.0084 and 0.358 ng mL−1. In addition, the developed method presented good recoveries in spiked samples, ranging from 84.50% and 103.75%, with coefficients of variation below 10%. Furthermore, the developed detection method showed good consistency (R2 = 0.9852) with the reference method, liquid chromatography/mass spectrometry (LC-MS/MS). To our knowledge, this is the first report about single-atom nanozymes-based ELISA for AFB1 determination. This work provides a feasible strategy for the rapid and sensitive detection of food contaminants using SAzymes.

Sandwich assay for β-lactoglobulin in infant food formula based on a hierarchically architectured antifouling capture probe and fluorescent recognition probe

Tracing the presence of allergenic β-lactoglobulin (β-Lg) in infant foods is an urgent need, but the interference from the protein-rich matrix often hampered the detection accuracy. Here, we developed a sandwich assay for β-Lg in infant food formula based on a hierarchically architectured antifouling capture probe and fluorescent recognition probe. The antifouling capture probe was constructed from the polydopamine-coated magnetic particles (Fe3O4@PDA), which was modified with repeated glutamic acid-lysine (EK) antifouling peptide and aptamer towards β-Lg. The spatial arrangement of these ligands on the Fe3O4@PDA surface was carefully tailored. Furthermore, a fluorescent recognition probe based on aptamer-modified silica-doped carbon quantum dot was developed to explore a sandwich assay for β-Lg with the capture probe. The sandwich assay was proved to have high potential in detecting β-Lg in commercially available infant food samples. The work provided a new approach to developing detection methods with matrix interference-resistant properties.

Modeling Preharvest Cyclospora cayetanensis Sampling and Testing for Various Water and Produce Sampling Plans

As of August 2023, the two U.S. Food and Drug Administration (FDA) official detection methods for C. cayetanensis are outlined in the FDA Bacteriological Analytical Manual (BAM) Chapters 19b (produce testing) and 19c (agricultural water testing). These newly developed detection methods have been shown to not always detect contamination when present at low levels. Yet, industry and regulators may choose to use these methods as part of their monitoring and verification activities while detection methods continue to be improved. This study uses simulation to better understand the performance of these methods for various produce and water sampling plans. To do so, we used published FDA test validation data to fit a logistic regression model that predicts the methods’ detection rate given the number of oocysts present in a 10-L agricultural water or 25 g produce sample. By doing so, we were able to determine contamination thresholds at which different numbers of samples (n = 1, 2, 4, 8, 16, and 32) would be adequate for detecting contamination. Furthermore, to evaluate sampling plans in use cases, a simulation was developed to represent C. cayetanensis contamination in agricultural water and on cilantro throughout a 45-day growth cycle. The model included uncertainty around the contamination sources, including scenarios of unintentionally contaminated irrigation water or in-field contamination. The results demonstrate that in cases where irrigation water was the contamination source, frequent water testing proved to be more powerful than produce testing. In scenarios where contamination occurred in-field, conducting frequent produce testing or testing produce toward the end of the season more reliably detected contamination. This study models the power of C. cayetanensis detection methods to understand the sampling plan performance and how these methods can be better used to monitor this emerging food safety hazard.

Formation of plasmonic silver nanoparticles by glucosamine reduction: Application to a colorimetric sensor for determination of glucosamine in its pharmaceutical preparations

The purpose of this study is to develop a novel method for synthesizing silver nanoparticles using glucosamine as reducing agent and to utilize the developed method for colorimetric detection and quantitative determination of the non-chromophoric drug, glucosamine. Silver nanoparticles are prepared by reducing 0.02 mol/L silver nitrate by glucosamine in 0.075 mol/L ammonia and stabilizing the nanoparticles with 0.1% polyvinylpyrrolidone and the mixture is heated at 90 °C for 5 min. The prepared silver nanoparticles dispersed in water exhibit a bright yellow color due to a localized surface plasmon resonance band at 412 nm. The principle of glucosamine sensing is based on measuring the intensity of the surface plasmon resonance band at 412 nm which is directly proportional to the concentration of glucosamine with a linearity range (1 – 9 μg/mL), limit of detection 0.33 μg/mL and limit of quantitation 1.0 μg/mL. The proposed method was validated according to the ICH guidelines, and it was found to be accurate, precise, selective, and robust. The method was applied for determination of glucosamine in Joflex® capsules using the standard addition approach with mean % recovery ± standard deviation of 100.077 ± 1.786. The method is simple, rapid, and cost-effective and can be used for determination of glucosamine in bulk and in its pharmaceutical preparations.

A high-resolution melting approach for the simultaneous differentiation of five human babesiosis–causing Babesia species

BackgroundSix species of apicomplexan parasites of the genus Babesia, namely B. microti, B. divergens, B. duncani, B. motasi, B. crassa–like and B. venatorum, are considered to be the primary causal agents of human babesiosis in endemic areas. These six species possess variable degrees of virulence for their primary hosts. Therefore, the accurate identification of these species is critical for the adoption of appropriate therapeutic strategies.MethodsWe developed a real-time PCR–high-resolution melting (qPCR-HRM) approach targeting 18S ribosomal RNA gene of five Babesia spp. based on melting temperature (Tm) and genotype confidence percentage values. This approach was then evaluated using 429 blood samples collected from patients with a history of tick bites, 120 DNA samples mixed with plasmids and 80 laboratory-infected animal samples.ResultsThe sensitivity and specificity of the proposed qPCR-HRM method were 95% and 100%, respectively, and the detection limit was 1–100 copies of the plasmid with the cloned target gene. The detection level depended on the species of Babesia analyzed. The primers designed in this study ensured not only the high interspecific specificity of our proposed method but also a high versatility for different isolates from the same species worldwide. Additionally, the Tm obtained from the prepared plasmid standard is theoretically suitable for identifying isolates of all known sequences of the five Babesia species.ConclusionsThe developed detection method provides a useful tool for the epidemiological investigation of human babesiosis and pre-transfusion screening.Graphical

Modeling and De-Noising for Nondestructive Detection of Total Soluble Solid Content of Pomelo by Using Visible/Near Infrared Spectroscopy.

The flavor of Pomelo is highly variable and difficult to determine without peeling the fruit. The quality of pomelo flavor is due largely to the total soluble solid content (TSSC) in the fruit and there is a commercial need for a quick but nondestructive TSSC detection method for the industrial grading of pomelo. Due to the large size and thick mesocarp of pomelo, determining the internal quality of a pomelo fruit in a nondestructive manner is difficult, and the detection accuracy is further complicated by the noise typically generated by the common methods for the internal quality detection of other fruits. Thus, the aim of this study was to determine the optimal method to accurately detect pomelo TSSC and find a de-noising model which reduces the influence of noise on the optimal method's results. After developing a full-transmission visible/near infrared (VIS/NIR) spectroscopy sampling method, the confirming experimental results showed that the optimal pomelo TSSC detection model was Savitzky Golay + standard normal variate + competitive adaptive reweighted sampling + partial least squares regression. The R2 and RMSE of the calibration set for pomelo TSSC detection were 0.8097 and 0.8508, respectively, and the R2 and RMSE of the validation set for pomelo TSSC detection were 0.8053 and 0.8888, respectively. Both reference and dark de-noising are important for pomelo internal quality detection and should be calibrated frequently to compensate for time drift. This study found that large sensor response translation noise can be reduced with an artificial horizontal shift. Data supplementation is efficient for improving the adaption of the detection model for batch differences in pomelo samples. Using this optimized de-noising model to compensate for time drift, sensor response translation, and batch differences, the developed detection method is capable of satisfying the requirements of the industry (TSSC detection R2 was equal or larger than 0.9, RMSE was less than 1). These results indicate that full-transmission VIS/NIR spectroscopy can be exploited to realize the nondestructive detection of pomelo TSSC on an industrial scale, and that the methodologies used in this study can be immediately implemented in real-world production.

Hybrid coyote predator with DL network for brain disorder detection using EEG signals

ABSTRACT Brain abnormality is one of the dominant health issues in recent years, and electroencephalogram (EEG) is a mostly employed method for identifying abnormalities in the brain. Brain abnormalities can be cured only when it is detected early stage. Even though there are various techniques for the detection of brain abnormalities, the health sector concentrates more on the advanced modern method to diagnose the abnormality very precisely. Advanced DL methods have attracted physicians very much due to the fascination with developing detection methods with minimal errors. Hence, this paper deeply concentrated on developing a well-structured technique to predict brain abnormalities using the hybrid coyote predator optimisation-dependent DCNN (hybrid coyote predator-DCNN). The results of this study demonstrate the value of hybrid coyote predator optimisation, which inherits qualities like a long jump and an intelligence ability to adapt to its surroundings and aids in fine-tuning the modal parameters of the DCNN. Additionally, to improve the detection model’s precision, the frequency bands from which the features are taken are separated to identify brain changes. Utilising the CAP dataset and the performance metrics, the analysis is carried out. Regarding the TP and k-fold, the hybrid coyote predator-DCNN model attained accuracy of 98.446% and 97.384%, respectively.

Synthesis of hollow Co-N/C nanocage with superior oxidase-like activity for colorimetric sensing analysis and catalytic mechanism

It is of great significance to exploit simple and efficient methods for detecting hazardous substances in the fields related to food safety and environmental protection. Herein, for the first time, a newly-designed hollow Co-N/C nanocage with oxidase-like activity was synthesized via a feasible method. Benefiting from the construction of hollow structure and the introduction of carbon layer, the specific surface area of enzyme mimic was improved, so exposing more active sites. Importantly, hollow Co-N/C nanocage displayed an excellent performance in interfacial charge transfer, which provided convenience for the occurrence of chromatic reaction between enzyme mimic and TMB. As predicted, hollow Co-N/C nanocage can effectively oxidize colorless TMB to blue oxTMB in the absence of H2O2, and the analyses about catalytic mechanism suggested that both the redox couples of Co3+/Co2+ and O2•− were involved in the oxidation of TMB. Furthermore, a chromogenic sensing method for the detection of Na2SO3 and Cr6+ was exploited, and a broad linear range and a low LOD can be achieved. More interestingly, developed detection method still possessed good recovery even in real samples. In a word, current work will share novel insights into the design of enzyme mimics and the analyses on chromogenic mechanism.

A Schiff base fluorescence switch-on probe derived from vitamin B6 cofactor for simultaneous detection of bovine serum albumin and ovalbumin

The abnormal amount of albumin found in serum and urine samples is an important biomarker for a variety of illnesses. It is essential for clinical diagnosis, medicinal research, and preventative medicine to correctly assess the albumin concentration in actual biological samples. Herein, vitamin B6 cofactors derived Schiff bases obtained by condensing pyridoxal 5′-phosphate (PLP) with 2-aminophenol (L), pyridoxal with 2-aminophenol (L1) and PLP with aniline (L2) were employed for the detection of albumins. Schiff base L is non-fluorescent due to conformational flexibility and free intramolecular rotation, but becomes fluorescent upon protein–ligand complex formation with bovine serum albumin (BSA) and ovalbumin (OVA). Other Schiff bases L1 and L2 failed to response the presence of BSA and OVA. The intensity of fluorescence of L increases linearly with the increasing concentration of BSA and OVA, and the detection limits for BSA and OVA were estimated to be 0.18 µM and 0.13 µM, respectively. The molecular docking and molecular dynamics simulations were done to investigate the binding affinity and modes between the albumins (BSA and OVA) and L. The developed detection method was applied for the detection of BSA and OVA in real biological samples of milk, serum, egg white and urine, with satisfactory recovery.

Microbial Synthesis of Heme b: Biosynthetic Pathways, Current Strategies, Detection, and Future Prospects.

Heme b, which is characterized by a ferrous ion and a porphyrin macrocycle, acts as a prosthetic group for many enzymes and contributes to various physiological processes. Consequently, it has wide applications in medicine, food, chemical production, and other burgeoning fields. Due to the shortcomings of chemical syntheses and bio-extraction techniques, alternative biotechnological methods have drawn increasing attention. In this review, we provide the first systematic summary of the progress in the microbial synthesis of heme b. Three different pathways are described in detail, and the metabolic engineering strategies for the biosynthesis of heme b via the protoporphyrin-dependent and coproporphyrin-dependent pathways are highlighted. The UV spectrophotometric detection of heme b is gradually being replaced by newly developed detection methods, such as HPLC and biosensors, and for the first time, this review summarizes the methods used in recent years. Finally, we discuss the future prospects, with an emphasis on the potential strategies for improving the biosynthesis of heme b and understanding the regulatory mechanisms for building efficient microbial cell factories.

Development of novel isothermal-based DNA amplification assay for detection of pig tissues in adulterated meat.

For the first time, we describe an innovative polymerase spiral reaction (PSR) assay for the rapid, simple, and accurate detection of pig tissues or pork in adulterated meat including heat-treated and processed ones. The PSR assay specifically targeting the mitochondrial cytochrome b (cyt-b) gene of the pig was successfully optimized permitting assay results in 65min time. The developed detection method was 100% specific amplifying only the cyt-b gene and displaying negative results with all the tested non-pork meats. The sensitivity of the developed PSR (760fg porcine DNA) was tenfold better than the end-point PCR and able to detect heat-treated (121°C) and adulterated (0.5% pork in beef) meat and processed pork products such as sausages, salami, meatball, soup, curry, etc. The developed PSR-based method can be used for point-of-care detection with minimum instrumentation and technical expertise to guarantee instant clearance of exported and imported meat products. This is the first time that PSR has been adapted for food authenticity purposes.

Ultrasensitive visual detection of the food-borne pathogen via MOF encapsulated enzyme

Various methods have been made to achieve sensitive detection (10 CFU/mL) of Escherichia coli O157:H7 (E. coli) in real samples, however, they are complex, time-consuming, or instrument-dependent. Enzyme-catalyzed reactions are one of the most efficient methods to amplify signals for sensitive detection. ZIF-8 owning stability, porosity, and high specific area are suitable for embedding enzymes which can effectively protect enzyme activity and thus improve detection sensitivity. Herein, a simple visual assay of E. coli with the limits of detection of 1 CFU/mL was developed based on this stable enzyme-catalyzed amplified system. A microbial safety test of milk, orange juice, seawater, cosmetic, and hydrolyzed yeast protein, was successfully performed with the limits of detection of 10 CFU/mL by the naked eye. And this bioassay possessed high selectivity and stability making the developed detection method practically promising.

Development of sandwich ELISA and lateral flow assay for the detection of Bungarus multicinctus venom

Snakebite envenoming adversely affects human health and life worldwide. Presently, no suitable diagnostic tools for snakebite envenoming are available in China. Therefore, we sought to develop reliable diagnostic tests for snakebite management. We conducted affinity purification experiments to prepare species-specific antivenom antibody (SSAb). In brief, affinity chromatography with an antibody purification column (Protein A) was conducted to purify immunoglobulin G from Bungarus multicinctus (BM) venom hyperimmunized rabbit serum. The cross-reactive antibodies were removed from commercial BM antivenin by immune adsorption on the affinity chromatography columns of the other three venoms, Bungarus Fasciatus (FS), Naja atra (NA), and O. hannah (OH), generating SSAb. The results of western blot analysis and enzyme-linked immunosorbent assay (ELISA) showed the high specificity of the prepared SSAb. The obtained antibodies were then applied to ELISA and lateral flow assay (LFA) to detect BM venom. The resulting ELISA and LFA could specifically and rapidly detect BM venom in various samples with the limits of quantification as 0.1 and 1 ng/ml, respectively. This method could effectively detect snake venom in experimentally envenomed rats (simulating human envenomation), which could distinguish positive and negative samples within 10-15 min. This method also showed promise in serving as a highly useful tool for a rapid clinical distinguishing of BM bites and rational use of antivenom in emergency centers. The study also revealed cross-reactivity between BM and heterogenous venoms, suggesting that they shared common epitopes, which is of great significance for developing detection methods for venoms of the snakes belonging to the same family.

Bias Mitigation for Capturing Potentially Illegal Hate Speech

Hate speech is a persistent issue in social media. Researchers have analyzed and developed detection methods for hate speech on the basis of example data, even though the phenomenon is only rather vaguely defined. This paper provides an approach to identify hate speech in terms of German laws, which are used as a basis for annotation guidelines applied to real world data. We annotate six labels in a corpus of 1,385 German short text messages: four subcategories of illegal hate speech, offensive language and a neutral class. We consider hate speech expressions as illegal if the linguistic content could be interpreted in a given context possibly violating a specific law. This interpretation and a check by lawyers would be the next step which is not yet included in our annotation. In this paper, I report on strategies to avoid certain biases in data for illegal hate speech. These strategies may serve as a model for building a larger dataset. In experiments, I investigate the capability of a Transformer-based neural network model to learn our classification. The results show that this multiclass classification is still difficult to learn, probably due to the small size of the dataset. I suggest that it is crucial to be aware of data biases and to apply bias mitigation techniques when training hate speech detection systems on such data. Data and scripts of the experiments are made publicly available.

Minimizing the environmental impact of unused pharmaceuticals: Review focused on prevention

Pharmaceuticals are essential for human health, but they become an environmental concern when entering the environment which occurs when residues are excreted after consumption or when unused pharmaceuticals are discarded improperly. Although there are no developed detection methods for all pharmaceuticals that reach the ecosystem, certain groups have been proven to cause adverse effects on ecosystems, including increased mortality in aquatic species and changes in physiology, behavior, or reproduction. Particular attention is devoted to these groups of pharmaceuticals and their environmental impact. In this review, the authors suggest measures for the reduction of unused pharmaceuticals in the environment, with a strong emphasis on prevention. Various policy interventions are recommended across the lifecycle including source-directed, user-orientated, and waste management measures, to prevent the creation of household pharmaceutical waste and to ensure environmentally friendly ways of pharmaceutical household waste disposal. Preventive measures include rational pharmaceutical consumption, prescribing greener drugs, or designing pharmaceuticals that are benign and easily biodegradable, improved disease prevention, personalized medicine, enhanced dimensioning of pack sizes, and marketplaces for redistribution of unused pharmaceuticals. The next step is to prevent unavoidable waste to reach the environment, so proper collection and disposal of unused pharmaceuticals is of utmost importance. Finally, educating health professionals and the public and partnership between environmental and healthcare scientists are of vital significance in all stages of the pharmaceuticals’ lifecycle. Minimalization of the level of pharmaceuticals in the environment will benefit human life.

Electrophoretic detection of black market myostatin propeptide.

Myostatin propeptide is prohibited according to chapter S4 of the "WADA 2022 List of Prohibited Substances and Methods." So far, no approved myostatin-propeptide pharmaceuticals are available. Nevertheless, myostatin-propeptides can be bought on the black market for "research purposes." A study on black market myostatin propeptide products is presented as well as electrophoretic detection methods for serum and urine. Out of the 12 tested products, only nine actually contained the protein. Separation by SDS-PAGE revealed that the nine products were relatively impure and that the main compound had a much higher mass (approximately 54-55 kDa) than expected (approximately 33 kDa). Further analyses by mass spectrometry showed that the elevated molecular mass was due to the presence of a full length GST-tag on the propeptide. The developed detection method for serum is based on immunoprecipitation (IP) followed by SDS-PAGE and Western blotting. In total, three anti-myostatin propeptide antibodies were tested. All of them were well suited for either IP or immunoblotting. The final protocol applies a biotinylated polyclonal antibody, streptavidin-coated magnetic beads, and a monoclonal detection antibody. For a sample volume of 500 μL serum, the detection limit of the method is approximately 2.5 ng/mL. The urine method applies a commercial ELISA for IP and performs with a limit of detection (LOD) of approximately 0.4 ng/mL. Furthermore, practically all currently available myostatin propeptide standards were also investigated. Due to the significant molecular mass difference of the black market products, an unambiguous differentiation from endogenous myostatin propeptide is possible.

Fluorescent Sensing of Ciprofloxacin and Chloramphenicol in Milk Samples via Inner Filter Effect and Photoinduced Electron Transfer Based on Nanosized Rod-Shaped Eu-MOF.

Rapid, facile, and accurate detection of antibiotic residues is vital for practical applications. Herein, we designed a sensitive, visual, and rapid analytical method for sensitive detection of ciprofloxacin and chloramphenicol based on a nanosized rod-shaped Europium metal organic framework (Eu-MOF). The fluorescent Eu-MOF was firstly synthesized by a simple synthetic route at room temperature, which displays a red emission. The mechanisms of detecting ciprofloxacin and chloramphenicol were confirmed to be the inner filter effect (IFE) and photoinduced electron transfer (PET). Under the optimized experimental conditions, the detection limits of the developed method for ciprofloxacin and chloramphenicol detection were 0.0136 and 3.16 μM, respectively. Moreover, the sensor was effectively applied for quantitative determination of ciprofloxacin and chloramphenicol milk samples with satisfactory recoveries of 94.5–102% and 97–110%, respectively. This work developed a new method for rapid detection of ciprofloxacin and chloramphenicol residues. In addition, the established method has potential practical application value for on-site safety regulation on antibiotic residues in animal-derived food.

Detection of Pertechnetate Using Square Wave Anodic Stripping Voltammetry on Carbon Electrodes

Technetium-99 (Tc-99) is a primary fission product of U-235, U-233, and Pu-239 and is a major constituent of radioactive waste. Tc-99 is commonly found as pertechnetate (TcO4 -), an anion that does not readily adsorb to natural sediment and is extremely mobile in environmental water systems. With concerns that radioactive waste may be leaking from sub-surface storage tanks and potentially reaching groundwater, developing detection methods for TcO4 - is important. Square wave anodic stripping voltammetry (SWASV) is a simple electrochemical technique that has yet to be investigated for pertechnetate due to the high potential required to form solid TcO2. In this work, we use SWASV with a variety of carbon electrodes to determine the optimal conditions for the detection of TcO4 - in a variety of solutions including acidic, basic and neutral pH. Carbon electrodes such as glassy carbon, boron-doped diamond, and nitrogen-doped tetrahedral amorphous carbon were examined. Carbon electrodes are ideal for the detection of pertechnetate due to their robust nature, low background current, and wide windows when compared to metal electrodes. Control studies included optimization of the deposition potential and time. In addition, calibration curves were created for each solution’s optimized parameters. Several analytical figures of merit such as the sensitivity, limit of detection, and limit of quantification were determined. This work expands upon the use of carbon electrodes for trace detection of environmentally relevant analytes.

Spatio-temporal data-driven detection of false data injection attacks in power distribution systems

The utilization of distributed generation units (DG) in power distribution systems has increased the complexity of system monitoring and operation. Numerous information and communication technologies have been adopted recently to overcome the challenges and complexities associated with the integration of DG units in distribution systems. However, these technologies have created wide opportunities for energy theft and other types of cyber-physical threats. False data injection attacks (FDIA) illustrate a challenging threat for distribution systems for these are very tough to detect in reality. In this manuscript, we propose a spatio-temporal learning algorithm that is able to acquire the normal dynamics of distribution systems to overcome possible FDIA. First, we use a long short-term memory autoencoder (LSTM-AE) in acquiring the usual dynamics. After that, we employ the unsupervised trained model in detecting the numerous potentials of FDIAs in distribution systems by assessing the residual error of every measurement sample. This developed method is purely data-driven. This enables it to be robust to the distribution systems’ nonlinearities and uncertainties which overcomes the weaknesses of the proposed detection algorithms in the literature. The efficacy of the developed detection method is assessed via different test case scenarios with numerous basic and stealth FDIAs.