This study explores the relatively under-researched area of comparing data privacy regulations and best practices across different countries, with a focus on the gaming industry. It provides an overview of general data privacy principles and existing global regulations, analyzing how gaming operators leverage personal data for competitive advantage. Specifically, the research examines the data privacy approaches and regulatory requirements in Singapore, Macau, and Japan, highlighting the cultural and historical contexts influencing these regulations. Through a comparative analysis, the article discusses the compliance needs for gaming operators in these jurisdictions.

In the era of big data, the balance between network security and personal privacy has become a key issue. This paper analyzes the new connotation and threat of network security, and discusses the ethical conflict between network security and personal privacy. Then it puts forward four principles to determine the ethical boundary. Finally, the guarantee mechanism is constructed from the aspects of perfecting laws and regulations, applying technological innovation, enhancing the awareness of ethical education, and self-discipline supervision and management in the industry. Taking the new power tram enterprises as an example, it is emphasized that enterprises should pay attention to balance and establish a standardized mechanism to eliminate the risk of conflict and realize the harmonious development of digital society.

Zero Day security threats are diverse and manifest in many forms. Despite the growing number of zero day attacks, very little information about the kind of threat and how to defend against the threats is known by information security professionals. Signature based techniques and statistical based techniques have been seen to be less effective in handling Zero-day security threats (ZDST) since they require a new threat signature and threat profile to be learnt each time, meaning new signatures and profiles cannot be detected and behavior-based approaches have always resulted in many false positives in handling of zero-day security threats. The ZDST may result in disruptions of service, loss of data, loss of data integrity, corruption of data, systems malfunction, miscommunication, or other undesired effects on information systems. This research proposes an ontology-based approach for management of ZDST and evaluates its performance for use in detection and prevention of ZDST within the information security domain.

A vulnerability repair solution for distributed wireless sensor networks that is based on the Mobile-N technique is presented in this paper. A vulnerability detection method is utilized to ascertain the position of edge nodes that are susceptible to attack, and a strategy for optimizing energy consumption and performance is implemented. This method determines the most suitable repair node by doing an exhaustive investigation on mobility distance, redundancy, and the amount of energy that is still available. The Mobile-N technique has been shown to be effective in several areas, including the correction of network defects, the completion of repairs while simultaneously reducing energy consumption, and the optimization of network stability. The Mobile-N technique outperformed other algorithms in every trial, with an average effective repair area ratio and failed node repair rate of 94.9%. Consequently, this demonstrates that the technique has resulted in significant enhancements to the repair energy efficiency and the node repair rate.

Stacking of multiple Machine Learning (ML) classifiers have gained popularity in addressing anomalous data classification along with Deep Learning (DL) algorithms. This study compares traditional ML classifiers, multi-layer stacking ML classifiers, and DL classifiers using an open-source malware dataset-containing equal numbers of benign and malware samples. The results on the realistic dataset indicate that the DL classifier, utilizing a Bidirectional Long Short-Term Memory (BiLSTM) model, outperformed the stacked classifiers with Logistic Regression (LR) and Support Vector Machine (SVM) as Meta learners by 36.78% and 39.69%, respectively, in terms of classification accuracy and performance. The research work was extended to study the impact of Generative Adversarial Network (GAN) based synthetic dataset of relatively smaller size on deep learning models. It was observed that the Deep Learning Multi-Layer Perceptron (DLMLP) Model had relatively superior performance as compared to complex deep learning models like Long Short-Term Memory LSTM and BiLSTM

Due to the emigration of world business to the internet, credit ‎cards have become a tool for ‎payments for both online and outline purchases. However, fraudsters try ‎to attack those systems ‎using various techniques, and credit card fraud has become dangerous. To ‎secure credit cards, ‎different methods are proposed in the academic paper based on artificial ‎intelligence. The proposed ‎solution in this paper aims at combining the robustness of three methods: ‎the differential evolution ‎algorithm (DE) for selecting the best hyperparameters, a resampling ‎technique for handling ‎imbalanced data issues, and the XGBoost technique for classification. Finally, ‎the fraudulent ‎transactions are classified using the optimized XGBoost algorithm. The proposed ‎solution is ‎evaluated using two real-world datasets: the European dataset and the UCI dataset. The ‎evaluation ‎in terms of accuracy, sensitivity, specificity, precision, and F-measure shows the ability and ‎the ‎superiority of the proposed approach in comparison with the state-of-the-art machine learning ‎‎models.‎

With the rapid development of the Internet, it is more and more difficult to get interested information from the mass of information. In order to alleviate the problem of data sparsity, this paper proposes an intelligent recommender of mobile wireless communication information based on speech recognition technology under strong multipath interference with the help of speech recognition technology. According to different types of mobile wireless communication information, the recommended path is selected, and the information is denoised to reduce the interference value of mobile wireless communication information intelligent recommendation and ensure the recommendation effect. Finally, the experimental results show that the proposed intelligent recommendation method of mobile wireless communication information based on speech recognition technology under strong multipath interference has high prediction accuracy and resource diversity, strong excavation ability, and good recommendation effect.

The energy storage or management system is becoming more important as electrical energy demand globally grows. In the first step of the energy management plan, energy forecasting is an essential element. Software-defined network (SDN) seeks to change network architecture and processes to be agile and effectively enhance the functionality of fundamental system components such as switches and routers. Predicting electric energy is crucial to support demand-side management in the building business. The total power load of the individual building services systems management is becoming a challenging process to be predicted for buildings with electricity metering systems. Conventional model energy prediction focuses on predictive accuracy, and energy consumption must be predicted according to different conditions to build an efficient system. Novel deep learning-based smart energy metering prediction (DL-SEMP) technique is considered to overthrow such accuracy lack in prediction. Blockchain-based SDN has emerged a new architect for obtaining a distributed network in blockchain technology.

This paper proposes a privacy preservation-based data security and secure data protocol for distributed Data Storage and Communication (DSaC). An effective performance analysis is done between privacy preservation-based data security approach for authenticated encrypted access and secure data protocol for DSaC in cloud and the approaches are compared with the traditional schemes like Ins-PAbAC, Homomorphic Proxy Re-encryption (HPRE), LAM-CIoT, SA-EDS, Advanced Encryption Standard (AES), and Data Encryption Standard (DES). The proposed privacy preservation approach achieved a minimum time of 12614ms, maximum detection rate of 0.985, maximum privacy factor of 0.645, maximum TPR of 0.785, and maximum TNR of 0.777 for Switzerland dataset and the ACS-based secure data protocol achieved a minimum time of 5527ms, maximum detection rate of 0.953, minimum E2E delay of 85, maximum TPR of 0.817, and maximum TNR of 0.754 for Cleveland dataset.