The advanced persistent threat (APT) attack is the most frequent and seriously damaging one that can be launched against the target system. This type of malicious activity is deliberate and focused, with specific objectives in mind. The rise of this attack technique poses a significant challenge to the information security systems of various businesses, governments, and organizations. The techniques of utilizing deep learning (DL) or machine learning (ML) methods to evaluate abnormal behaviours and network traffic signs for preventing and detecting APT attacks are becoming popular in recent times. Though, the APT attack detection technique utilizes behaviour evaluation and analysis methods for confronting various problems owing to the absence of distinctive data of attack drives. So, the experimental outcomes display better recognition, it does not bring higher effectiveness in practice. Therefore, this study develops a novel Defence against Advanced Persistent Threat Attack Detection Feature Selection with Deep Learning (DAPTAD-FSDL) Method. The key objective of the DAPTAD-FSDL approach lies in the robust defence against the detection of APT attacks. In the presented DAPTAD-FSDL model, Z-score normalization is performed to change the raw data into a compatible format. Then, the presented DAPTAD-FSDL technique accomplishes the feature selection process using the grey wolf optimizer (GWO) algorithm. Besides, the attention with CNN-BiLSTM model is employed for the defence against the detection of APT attacks. Finally, a multi-objective improved golden jackal optimization algorithm (MIGJOA) is exploited for the optimal hyperparameter adjustment of the A-CNN-BiLSTM model. The experimental analysis of the DAPTAD-FSDL algorithm is tested employing the APT NSL-KDD dataset. The simulation outcomes described the supremacy of the DAPTAD-FSDL technique under different measures.

In the global wave of digitalization, the digital preservation of Chinese Ming Dynasty landscape paintings has achieved remarkable results. According to statistics, major museums in China have scanned more than 60% of Ming Dynasty landscape paintings with high precision, and the digital preservation work covers a wide range. However, the accompanying cybersecurity challenges cannot be ignored either. In recent years, cyber attacks on digital platforms of cultural heritage have occurred frequently. Worldwide, the number of such attacks has exceeded 3 million every year, of which attacks on works of art account for as high as 15%. Faced with this grim situation, China has taken a series of measures to strengthen network security protection. By upgrading encryption technology, optimizing network security protection systems, and conducting regular network security training and drills, China has made remarkable progress in ensuring the security of digital works of art. According to the latest data, since the implementation of enhanced protection measures, the number of cyber attacks on the digital platform of landscape painting in Ming Dynasty in China has decreased by 35%, effectively protecting precious cultural heritage from cyber threats.

The oceans, as the forthcoming financial frontier, have precipitated disputes at the nexus of resource protection and abuse, a domain where customs and social norms converge. The present research, supported by research reviews, inquiries, and personal assessments, determines the primary policy, socio-economic, ecological, and cultural motorists of conflicts stemming from the interplay of recreational fishing a significant shipping activity in Mediterranean maritime safeguarded areas and other coastal consumers. Intelligent media is a new media form with the comprehensive application of mobile Internet technology, Internet of Things technology, big data technology, and artificial intelligence technology to realize high human-machine interaction and collaboration. The functions of information production, information experience, information push, information storage, and information utilization are realized through intelligent media and intelligent platforms. The application of smart technology builds a new form of communication, making the transmission of Intangible Cultural Heritage (ICH) more efficient and accurate. It can promote ICH's more effective dissemination and higher quality utilization through intelligent media technology by establishing a larger cultural communication platform, stimulating wider cultural consumption, and transforming cultural resources. ICH Fisheries Management (ICHFM) is an effective carrier of national spirit and emotion, representing the nation's past, present, and future, and is the core of social and cultural development. The protection and inheritance of ICH are related to the continuation and development of Chinese culture, and it is an essential cultural project that is urgently needed. As one of the products of human civilization, the ICHFM is facing a complex situation of coexisting challenges and opportunities in contemporary times, which needs to respond to the significant changes in living environment and values, and at the same time, it meets with the awakening of human awareness of cultural diversity and the improvement of cultural heritage protection ability. Therefore, researchers and decision-makers should have a positive and advanced attitude towards technology innovation under the background of intangible protection and inheritance of all kinds of innovation practice, especially intangible knowledge popularization, culture, and social education transmission behavior, which should be encouraged and emerging technology, communication channels, consumption forms, explore the intangible spread in the contemporary digital potential value, focus on intangible digital transmission present "video and experience" "elements of open source and digital potential" "digital survival and virtual community inheritance" trend. The findings indicate that the persistent contradictions resulting from the conflict originate from divergent notions of history, resource adoption, and conservation.

The issue of Cybersecurity (CS) danger is a persistent and escalating burden that has garnered worldwide attention in recent years. This might be attributed to technological advances that have transformed the internet into a breeding ground for cybercriminals. The rising frequency of cybercrimes emphasizes the need for heightened CS Awareness (CSA) and education among individuals and institutions. This study investigates the level of CSA among undergraduates (UG). Surveys were sent to 250 UG from four chosen postsecondary institutions in Peru. The samples were selected using a basic random sampling procedure, and the gathered data was examined using percentages, frequencies, and statistical techniques such as regression and Analysis of Variance (ANOVA). They lacked awareness of effective measures to safeguard against cyber threats and assaults. A robust statistical correlation was found between the respondents' level of expertise in CS and their attitudes towards CSA. A substantial association was seen between their understanding of CS and their interest in CSA. The barriers that hinder CSA include a shortage of cyber talents, time limitations, the absence of CS in non-computing programs, inadequate knowledge of essential computing regions, an absence of instructors with practical experience in CS, insufficient supportive facilities, and indifference. It is imperative to incorporate CS into educational programs to provide pupils with the necessary skills to navigate and safeguard against cyber threats, such as crime and espionage, in the current digital landscape, it is imperative to incorporate CS into educational programs to provide pupils with the necessary skills to navigate and safeguard against cyber threats, such as crime and espionage.

Technology is important in all aspects of our lives, particularly in the banking sector. Advanced technologies resulted in a fundamental shift in the way banks operate. Malicious use of technology leads to security breaches, customer distrust, financial instability, and other forms of cybercrime. This paper focuses on the most important threats and challenges to be considered while interacting with banks and financial institutions. Significant number of literatures will be reviewed to identify the most common threats and attacks on banks and financial institutions sector, as well as appropriate mitigation techniques and countermeasures. This paper focuses on bank and financial services institution infrastructure vulnerabilities that are escalating attacks on the sector and affecting the banking system, individuals, and organizations. The security study in these 35 projects demonstrates how cyber intrusions continue to attack the financial sector due to the weakness of security defense and data containment. It turns out that malware attacks are the most common threats to banks and financial institutions. Sharing and exchanging information between banks and financial institutions, as well as best practices taken by bank cards issuers and users are the most effective mitigation techniques. Some previous studies proposed a successful model for mitigating the impact of threats on banks and financial institutions, as well as reducing risks that cannot be mitigated using current mitigation techniques. According to banks and financial services statistics and surveys, existing controls and laws do not provide appropriate security meet the banks and financial services institutions systems' demands and requirements.

Monitoring systems have become more and more crucial in our lives in recent years. Consequently, we propose in this work an automatic weather monitoring system that is capable of obtaining dynamic, real-time climatic data for a given area. The Internet of Things (IoT), a sophisticated and effective method of connecting objects to the Internet, serves as the scientific foundation for this system. Many areas of practice and science rely on accurate climate measurements. Location and time of day influence weather factors, especially in tropical areas where there are no seasons. Furthermore, as precision agriculture or smart agriculture advances, it will be crucial to improve the measurement of systems that are widely distributed over growing areas. For these reasons, the design, building, and fabrication of a mobile air conditioner with Wi-Fi connectivity and solar energy power is presented in this work. This station measures both relative humidity and temperature. Additionally, workstations can be configured and managed remotely. The program's objective is to promote the creation of freely accessible, open-source hardware. A meteorological base station framework based on the ESP-32 Internet of Things device development board is proposed in this study. The system has a feature that records the ambient temperature and humidity. The ESP-32 web server then makes the data available to the user. A web page is also part of the system to make controlling adjacent instruments easier. The system gets its power from solar electricity.

In Smart City (SC), things can connect with humans intelligently through the Internet of Things (IoT). IoT and ecological sensors will enable the detection of pollutants, contributing to developing environmentally friendly SC. Green Internet Architecture (GIA) in SC ensures the long-term viability of friendly spaces by enabling autonomous and intelligent sustainability practices through collaboration. Governments and organizations worldwide are promoting the significance of reducing energy usage and carbon emissions while highlighting the relevance of using GIA in SC. Several SC layouts connected to the IoT have previously been proposed in the literature. However, this study introduces the "Green Internet" notion of providing a friendly setting that focuses on conserving energy in SC. This research presents a suggested design for a GIA in SC. The main objective is to minimize energy usage at every level and ensure the implementation of IoT in a friendly manner. The suggested GIA is founded on a cloud-based system that diminishes hardware usage with Blockchain technology.

The speed and load capacity of trains are improving at an accelerating rate, which raises the standards for railway services' security criteria. The track's surface may progressively reveal varying degrees of defects because of the impacts of moisture, temperature, load, and other factors; if the flaws never addressed in a prompt period, the degree of defects would expand, significantly raising the probability of train service. The development of automatic RSDD (Rail Surface Defect Detection) has substantial practical and scientific implications. In this study, a multi-crack detection technique depends on Variational Autoencoder Diffusion Model (VAEDM), The introduction of VAEDM allows for the non-destructive detection of fastener and RSD. In order to produce hidden elements from images that have various cracks, VAEDM combines the multi-layer and a rail surface image encoder was introduced. Zero Shot- Divergence Asynchronous Reinforcement Learning (ZS-DARL), Policy-Gradient is used to establish the relationship between defects and non-defects. DARL transformer encoder is introduced for taking long-range dependences for FE (Feature Extraction) from detected objects images with various patterns and dimensions. The simulation outcomes on the open Railway Track Fault Detection (RTFD) and Rail Surface Defect Datasets (RSDDs) with rail surface defects are collected from rail tracks surface defect detection. Results are measured using the metrics like recall, precision, F-measure, and accuracy.

Applying approaches such as crowding distance between samples that belong to the Non/Dominated Sorting version of the Genetic Algorithm type II can gradually improve the feature subset by selecting the most relevant based on their distance values. The number of subsets has been decreased for the applied dataset from (41) features to (29) features with accuracy maintained after applying machine learning techniques. K-NN, SVM, and DT provided an improvement in predicted accuracy from (80.81, 76.6, and 86.7) to (81.9, 81.6, and 87.6) with features minimizations based on the proposed model. In addition, a study was done on the applied dataset for level and basic types of IoT regarding its affection on prediction accuracy with specifying a value of these features. This study was also done after applying the crowding distance with info gain for the first 10 features of 29 in total plus a comparison with four feature selection methods. The packet as a level type as well as the basic type was the most dominant feature of all other IoT features on the prediction accuracy. The accuracy of overall utilized Machine learning was maintained or even increased regarding these types only, especially for SVM. Connection level features and traffic types (such as the same service or same host) were less effective features on the overall machine learning prediction accuracy. This study was done for the NSL-KDD dataset to record reasonable numbers with more variety to protect machine learning methods from frequent records affection. In addition to accuracy, other evaluated parameters were done such as Precision, Recall, ROC, and PRC area to demonstrate the privileges of features minimization. This study was compared to relative studies based on feature specification or not with accuracy evaluations based on state-of-the-art methods. Packet level had a 3.8% effective ratio on the SVM method, while Basic type had a 4.5% effective ratio on the same overall SVM accuracy. Comparisons have been provided to demonstrate the validity of these results with previously utilized methods.

The influence of fast food is very large on people's health, it can cause dangers such as weight gain and obesity, poor nutrition, digestive disorders, increased blood pressure and cholesterol, increased risk of serious diseases such as cancer, diabetes and heart disease. Doctors who diagnose heart disease cannot be seen directly, with the help of an electrocardiogram, an examination using an electrocardiogram to detect abnormalities in the heart such as heart rhythm disturbances and pericarditis. The electrocardiogram tool detects how long the electrical waves last through measuring intervals and the amount of electrical activity in the heart muscle. The electrocardiogram tool consists of 12 leads. This research aims to diagnose heart abnormalities using the backpropagation method approach and to determine the level of accuracy of diagnosing heart abnormalities in patients. An artificial neural network is a network of interconnected nodes, where each connection has a connection weight distributed across all nodes. Meanwhile, the backpropagation learning algorithm is a guided learning algorithm whose input and output are known in advance. In addition to building artificial neural network models for predictive capabilities. Backpropagation is a method of working backwards, which can reduce the number of errors. The model will be evaluated using 10% separate data as validation data. The evaluation model created uses RMSE, confusion matrix, and ROV curve to find out how well the model performs in making predictions. The results of this research show an RMSE (Root Mean Square Error) value of 0.33, a matrix confusion value of 88.89%, and an ROC curve value with an ROC curve area = 0.88. The results of data analysis using artificial neural networks with the backpropagation method approach provide maximum results. The results of data analysis using artificial neural networks with a backpropagation method approach provide maximum results for diagnosing patient heart abnormalities.