Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Milk Spoilage: Methods and Practices of Detecting Milk Quality

Milk spoilage is an indefinite term and difficult to measure with accuracy. This uncertainty can cause suffering for both milk manufacturers and consumers. Consumers who have been misled by ambiguous expiration dates on milk cartons waste resources by disposing of unspoiled milk or experience discomfort from drinking spoiled milk. Consumers are often unwilling to purchase products close to their inaccurate expiration dates. This consumer behavior has a negative financial impact on milk producers. Inaccurate milk spoilage detection methods also force milk producers to use overly conservative expiration dates in an effort to avoid the legal and economic consequences of consumers experiencing illness from drinking spoiled milk. Over the last decade, new methods have been researched with the purpose of developing more accurate and efficient means of detecting milk spoilage. These methods include indicators based on pH bacteria counts and gas-sensor arrays. This article explores various methods of spoilage detection designed to prevent such consequences. The respective level of effectiveness of each method is discussed, as well as several further approaches to contain freshness regardless of detection.

Privacy Preservation in IoT Devices by Detecting Obfuscated Malware Using Wide Residual Network

The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks.Further,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT systems.The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis dataset.Moreover,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and recall.The study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT environments.Furthermore,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested methodology.This research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT devices.However,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world datasets.Utilizing WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT ecosystems.To summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning methods.The research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance analysis.Future research could focus on enhancing the recommended approach by adding more datasets and leveraging real-time monitoring capabilities to strengthen IoT devices’defenses against new cybersecurity threats.

YOLO-VSI: An Improved YOLOv8 Model for Detecting Railway Turnouts Defects in Complex Environments

Railway turnouts often develop defects such as chipping,cracks,and wear during use.If not detected and addressed promptly,these defects can pose significant risks to train operation safety and passenger security.Despite advances in defect detection technologies,research specifically targeting railway turnout defects remains limited.To address this gap,we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex environments.To enhance detection accuracy,we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU(YOLO-VSI).The model employs a state-space model(SSM)to enhance the C2f module in the YOLOv8 backbone,proposed the C2f-VSS module to better capture long-range dependencies and contextual features,thus improving feature extraction in complex environments.In the network’s neck layer,we integrate SPDConv and Omni-Kernel Network(OKM)modules to improve the original PAFPN(Path Aggregation Feature Pyramid Network)structure,and proposed the Small Object Upgrade Pyramid(SOUP)structure to enhance small object detection capabilities.Additionally,the Inner-CIoU loss function with a scale factor is applied to further enhance the model’s detection capabilities.Compared to the baseline model,YOLO-VSI demonstrates a 3.5%improvement in average precision on our railway turnout dataset,showcasing increased accuracy and robustness.Experiments on the public NEU-DET dataset reveal a 2.3%increase in average precision over the baseline,indicating that YOLO-VSI has good generalization capabilities.

A Method for Detecting and Recognizing Yi Character Based on Deep Learning

Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detection and recognition.In the detection stage,an improved Differentiable Binarization Network(DBNet)framework is introduced to detect Yi characters,in which the Omni-dimensional Dynamic Convolution(ODConv)is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features,thereby improving the accuracy of Yi character detection.Then,the feature pyramid network fusion module is used to further extract Yi character image features,improving target recognition at different scales.Further,the previously generated feature map is passed through a head network to produce two maps:a probability map and an adaptive threshold map of the same size as the original map.These maps are then subjected to a differentiable binarization process,resulting in an approximate binarization map.This map helps to identify the boundaries of the text boxes.Finally,the text detection box is generated after the post-processing stage.In the recognition stage,an improved lightweight MobileNetV3 framework is used to recognize the detect character regions,where the original Squeeze-and-Excitation(SE)block is replaced by the efficient Shuffle Attention(SA)that integrates spatial and channel attention,improving the accuracy of Yi characters recognition.Meanwhile,the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model,so that the model can better understand the contextual information and improve the accuracy of text recognition.The experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters,with detection and recognition accuracy rates of 97.5%and 96.8%,respectively.And also,we have compared the detection and recognition algorithms proposed in this paper with other typical algorithms.In these comparisons,the proposed model achieves better detection and recognition results with a certain reliability.

Standard-definition White-light,High-definition White-light versus Narrow-band Imaging Endoscopy for Detecting Colorectal Adenomas:A Multicenter Randomized Controlled Trial

Objective This study aimed to compare the performance of standard-definition white-light endoscopy(SD-WL),high-definition white-light endoscopy(HD-WL),and high-definition narrow-band imaging(HD-NBI)in detecting colorectal lesions in the Chinese population.Methods This was a multicenter,single-blind,randomized,controlled trial with a non-inferiority design.Patients undergoing endoscopy for physical examination,screening,and surveillance were enrolled from July 2017 to December 2020.The primary outcome measure was the adenoma detection rate(ADR),defined as the proportion of patients with at least one adenoma detected.The associated factors for detecting adenomas were assessed using univariate and multivariate logistic regression.Results Out of 653 eligible patients enrolled,data from 596 patients were analyzed.The ADRs were 34.5%in the SD-WL group,33.5%in the HD-WL group,and 37.5%in the HD-NBI group(P=0.72).The advanced neoplasm detection rates(ANDRs)in the three arms were 17.1%,15.5%,and 10.4%(P=0.17).No significant differences were found between the SD group and HD group regarding ADR or ANDR(ADR:34.5%vs.35.6%,P=0.79;ANDR:17.1%vs.13.0%,P=0.16,respectively).Similar results were observed between the HD-WL group and HD-NBI group(ADR:33.5%vs.37.7%,P=0.45;ANDR:15.5%vs.10.4%,P=0.18,respectively).In the univariate and multivariate logistic regression analyses,neither HD-WL nor HD-NBI led to a significant difference in overall adenoma detection compared to SD-WL(HD-WL:OR 0.91,P=0.69;HD-NBI:OR 1.15,P=0.80).Conclusion HD-NBI and HD-WL are comparable to SD-WL for overall adenoma detection among Chinese outpatients.It can be concluded that HD-NBI or HD-WL is not superior to SD-WL,but more effective instruction may be needed to guide the selection of different endoscopic methods in the future.Our study’s conclusions may aid in the efficient allocation and utilization of limited colonoscopy resources,especially advanced imaging technologies.

Improved YOLOv8n Model for Detecting Helmets and License Plates on Electric Bicycles

Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.

Fiber Bragg Grating Strain Sensing Detecting Multi-Crack Damages under Vibrating Status

The multi-crack damages modal of simple supported beam has been build, at the vibrating status, the multi-damage detecting method of simple supported beam measured by fiber Bragg grating strain sensing array has been studied. From 0 hz to 200 hz, using exciter vibrating simple supported beam, with different damages, resonant frequency of simple supported beam has changed. So, when the damage appears in simple supported beam, the local rigidity will decrease, the resonant frequency of simple supported beam will be affected, the damage status of simple supported beam have been determined by this. The experimental result indicates that the resonant frequency of simple supported beam has changed when there is no damage, one damage, two damages, three damages on simple supported beam. According to this, the fiber Bragg grating strain sensing array can detect multi-crack damage of simple supported beam under vibrating status.

Structure Damage Identification via Fiber Grating Strain Sensing Array Detecting and Wavelet Analysis

The measuring method of structure damage during vibrating has been developed by applying simple supported beam as object of study, fiber Bragg grating strain sensing array as the measuring method, and wavelet package analysis as signal extracting tools. The damage data of simple supported beam at vibrating state has been collected. The damage characteristic indexes have been extracted based on analyzing and handling the damage data with wavelet analysis. The experiment shows that fiber Bragg grating strain sensing array can sensitively measure the experimental data of simple supported beam at vibrating state. The fiber Bragg grating strain sensing array measuring is a new method in dynamic measurement.

A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.

A Novel YOLOv5s-Based Lightweight Model for Detecting Fish’s Unhealthy States in Aquaculture

Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for preventing the spread of diseases and minimizing economic losses.To address this issue,an improved algorithm based on the You Only Look Once v5s(YOLOv5s)lightweight model has been proposed.This enhanced model incorporates a faster lightweight structure and a new Convolutional Block Attention Module(CBAM)to achieve high recognition accuracy.Furthermore,the model introduces theα-SIoU loss function,which combines theα-Intersection over Union(α-IoU)and Shape Intersection over Union(SIoU)loss functions,thereby improving the accuracy of bounding box regression and object recognition.The average precision of the improved model reaches 94.2%for detecting unhealthy fish,representing increases of 11.3%,9.9%,9.7%,2.5%,and 2.1%compared to YOLOv3-tiny,YOLOv4,YOLOv5s,GhostNet-YOLOv5,and YOLOv7,respectively.Additionally,the improved model positively impacts hardware efficiency,reducing requirements for memory size by 59.0%,67.0%,63.0%,44.7%,and 55.6%in comparison to the five models mentioned above.The experimental results underscore the effectiveness of these approaches in addressing the challenges associated with fish health detection,and highlighting their significant practical implications and broad application prospects.

A proposal for detecting weak electromagnetic waves around 2.6μm wavelength with Sr optical clock

Infrared signal detection is widely used in many fields.Due to the detection principle,however,the accuracy and range of detection are limited.Thanks to the ultra stability of the^(87)Sr optical lattice clock,external infrared electromagnetic wave disturbances can be responded to.Utilizing the ac Stark shift of the clock transition,we propose a new method to detect infrared signals.According to our calculations,the theoretical detection accuracy in the vicinity of its resonance band of 2.6μm can reach the order of 10-14W,while the minimum detectable signal of common detectors is on the order of 10^(-10)W.

Improved Mechanism for Detecting Examinations Impersonations in Public Higher Learning Institutions: Case of the Mwalimu Nyerere Memorial Academy (MNMA)

Currently, most public higher learning institutions in Tanzania rely on traditional in-class examinations, requiring students to register and present identification documents for examinations eligibility verification. This system, however, is prone to impersonations due to security vulnerabilities in current students’ verification system. These vulnerabilities include weak authentication, lack of encryption, and inadequate anti-counterfeiting measures. Additionally, advanced printing technologies and online marketplaces which claim to produce convincing fake identification documents make it easy to create convincing fake identity documents. The Improved Mechanism for Detecting Impersonations (IMDIs) system detects impersonations in in-class exams by integrating QR codes and dynamic question generation based on student profiles. It consists of a mobile verification app, built with Flutter and communicating via RESTful APIs, and a web system, developed with Laravel using HTML, CSS, and JavaScript. The two components communicate through APIs, with MySQL managing the database. The mobile app and web server interact to ensure efficient verification and security during examinations. The implemented IMDIs system was validated by a mobile application which is integrated with a QR codes scanner for capturing codes embedded in student Identity Cards and linking them to a dynamic question generation model. The QG model uses natural language processing (NLP) algorithm and Question Generation (QG) techniques to create dynamic profile questions. Results show that the IMDIs system could generate four challenging profile-based questions within two seconds, allowing the verification of 200 students in 33 minutes by one operator. The IMDIs system also tracks exam-eligible students, aiding in exam attendance and integrates with a Short Message Service (SMS) to report impersonation incidents to a dedicated security officer in real-time. The IMDIs system was tested and found to be 98% secure, 100% convenient, with a 0% false rejection rate and a 2% false acceptance rate, demonstrating its security, reliability, and high performance.

Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

Use of buffy coat thick films in detecting malaria parasites in patients with negative conventional thick films

Objective:To determine the frequency of malaria parasite detection from the buffy coal blood films by using capillary tube in falciparum malaria patients with negative conventional thick films.Methods:Thirty six uncomplicated falciparum malaria patients confirmed by conventional thick and thin films were included in the study.The patients were treated with artemisinin combination therapy at Hospital for Tropical Diseases,Bangkok,Thailand for 28 day.Fingerpricks for conventional blood films were conducted every 6 hours until negative parasitemia,then daily fingerpricks for parasite checks were conducted until the patients were discharged from hospital. Blood samples were also concurrently collected in 3 heparinized capillary tubes at the same time of fingerpricks for conventional blood films when the prior parasitemia was negative on thin films and parasitemia was lower than 50 parasites/200 white blood cells by thick film.The first negative conventional thick films were compared with buffy coat thick films for parasite identification. Results:Out of 36 patients with thick films showing negative for asexual forms of parasites, buffy coat films could detect remaining 10 patients(27.8%) with asexual forms of Plasmodium falciparum.Conclusions:The study shows that buffy coat thick films are useful and can detect malarial parasites in 27.8%of patients whose conventional thick films show negative parasitemia.

Improvement Detecting Method of Optical Axes Parallelism of Shipboard Photoelectrical Theodolite Based on Image Processing

An improvement detecting method was proposed according to the disadvantages of testing method of optical axes parallelism of shipboard photoelectrical theodolite (short for theodolite) based on image processing. Pointolite replaced 0.2'' collimator to reduce the errors of crosshair images processing and improve the quality of image. What’s more, the high quality images could help to optimize the image processing method and the testing accuracy. The errors between the trial results interpreted by software and the results tested in dock were less than 10'', which indicated the improve method had some actual application values.

Intelligent Biometric Information Management

We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.

Infrastructure of Synchrotronic Biosensor Based on Semiconductor Device Fabrication for Tracking, Monitoring, Imaging, Measuring, Diagnosing and Detecting Cancer Cells

Copper Zinc Antimony Sulfide(CZAS)is derived from Copper Antimony Sulfide(CAS),a famatinite class of compound.In the current paper,the first step for using Copper,Zinc,Antimony and Sulfide as materials in manufacturing synchrotronic biosensor-namely increasing the sensitivity of biosensor through creating Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor and using it instead of Copper Tin Sulfide,CTS(Cu2SnS3)for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells,is evaluated.Further,optimization of tris(2,2'-bipyridyl)ruthenium(II)(Ru(bpy)32+)concentrations and Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor as two main and effective materials in the intensity of synchrotron for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells are considered so that the highest sensitivity obtains.In this regard,various concentrations of two materials were prepared and photon emission was investigated in the absence of cancer cells.On the other hand,ccancer diagnosis requires the analysis of images and attributes as well as collecting many clinical and mammography variables.In diagnosis of cancer,it is important to determine whether a tumor is benign or malignant.The information about cancer risk prediction along with the type of tumor are crucial for patients and effective medical decision making.An ideal diagnostic system could effectively distinguish between benign and malignant cells;however,such a system has not been created yet.In this study,a model is developed to improve the prediction probability of cancer.It is necessary to have such a prediction model as the survival probability of cancer is high when patients are diagnosed at early stages.

Design and Implementation of a Multi-Sensor Based Object Detecting and Removing Autonomous Robot Exploration System

Developing autonomous mobile robot system has been a hot topic in AI area. With recent advances in technology, autonomous robots are attracting more and more attention worldwide, and there are a lot of ongoing research and development activities in both industry and academia. In complex ground environment, obstacles positions are uncertain. Path finding for robots in such environment is very hot issues currently. In this paper, we present the design and implementation of a multi-sensor based object detecting and moving autonomous robot exploration system, 4RE, with the VEX robotics design system. With the goals of object detecting and removing in complex ground environment with different obstacles, a novel object detecting and removing algorithms is proposed and implemented. Experimental results indicate that our robot system with our object detecting and removing algorithm can effectively detect the obstacles on the path and remove them in complex ground environment and avoid collision with the obstacles.

Blockchain‑oriented approach for detecting cyber‑attack transactions

With the high-speed development of decentralized applications,account-based blockchain platforms have become a hotbed of various financial scams and hacks due to their anonymity and high financial value.Financial security has become a top priority with the sustainable development of blockchain-based platforms because of an increasing number of cyber attacks,which have resulted in a huge loss of crypto assets in recent years.Therefore,it is imperative to study the real-time detection of cyber attacks to facilitate effective supervision and regulation.To this end,this paper proposes the weighted and extended isolation forest algorithms and designs a novel framework for the real-time detection of cyber-attack transactions by thoroughly studying and summarizing real-world examples.Furthermore,this study develops a new detection approach for locating the compromised address of a cyber attack to resolve the data scarcity of hack addresses and reduce time consumption.Moreover,three experiments are carried out not only to apply on different types of cyber attacks but also to compare the proposed approach with the widely used existing methods.The results demonstrate the high efficiency and generality of the proposed approach.Finally,the lower time consumption and robustness of our method were validated through additional experiments.In conclusion,the proposed blockchain-oriented approach in this study can handle real-time detection of cyber attacks and has significant scope for applications.