Casting represents a production process where a liquid material is poured into a mold with a hollow cavity, usually of the intended shape, following which its solidification is allowed. Numerous defect types are available, including blow holes, pin holes, burrs, mold material defects, shrinkage defects, metallurgical defects, casting metal defects, etc. All industries have quality control departments to eliminate the occurrence of this defective product. But the main problem is that this inspection process is done manually. This is a very time consuming process and due to human sensitivity this is not 100% accurate. In this study, we will verify whether the "manual inspection" bottleneck can be eliminated by automating the inspection process with transfer learning in the manufacturing process of casting products. In this study, we will verify whether the "manual inspection" bottleneck can be eliminated by automating the inspection process with transfer learning in the manufacturing process of casting products. In this study, the casting images were divided into two separate classes, and the classification process was carried out by applying deep learning architectures. The benefits of this proposed approach are discussed and proposed as a more efficient way to control the quality of final products under Industry 4.0.

Electric Vehicle Charging Stations (EVCSs) are critical components of modern transportation infrastructure. However, smart grid integrations create new security vulnerabilities. Cyber attacks can damage EVCSs, cause financial losses, and compromise user security. Traditional security measures are not enough. By analyzing the large data sets produced by EVCSs, machine learning (ML) can detect anomalies and provide predictive maintenance. The increasing importance of EVCSs in smart grid infrastructure necessitates taking advanced security measures. Machine learning-based intrusion detection systems represent a promising solution to the dynamic and complex cyber threats facing these critical infrastructures. Through continuous learning and adaptation, ML can provide a robust defense mechanism that ensures the security and reliability of EVCSs in the face of evolving cyber threats.

This article discusses the innovative application of artificial intelligence (AI) to develop a predictive model that aims to evaluate the condition of the machine by focusing on the probability of failure. The research uses a synthetic dataset prepared to simulate real-world situations where machines are equipped with sensors that monitor various health indicators and record the occurrence of faults. This data set consists of 10,000 inputs, each containing five numerical measurements: air temperature, process temperature, rotation speed, torque, and machine wear, in addition to the type of product produced, for a total of six input variables. The output of the model is the fault state of the machine, displayed as true or false. A hybrid artificial neural network integrating a GRU (Gated Recurrent Unit)-based model with the Transformer Encoder block was used for prediction. This combination highlights the superior predictive capabilities of the model. This approach represents a shift from traditional maintenance programs, which are often time-based and often result in unnecessary resource use, to a more efficient, condition-based maintenance strategy. This new strategy aims to ensure that maintenance activities are carried out only when necessary, thus optimizing resource use and minimizing downtime.

Industrial control systems (ICSs) are critical components automating the processes and operations of electromechanical systems. These systems are vulnerable to cyberattacks and can be the targets of malicious activities. With increased internet connectivity and integration with the Internet of Things (IoT), ICSs become more vulnerable to cyberattacks, which can have serious consequences, such as service interruption, financial losses, and security hazards. Threat actors target these systems with sophisticated attacks that can cause devastating damage. Cybersecurity vulnerabilities in ICSs have recently led to increasing cyberattacks and malware exploits. Hence, this paper proposes to develop a security solution with dynamic and adaptive deceptive patching strategies based on studies on the use of deceptive patches against attackers in industrial control systems. Within the present study’s scope, brief information on the adversarial training method and window size manipulation will be presented. It will emphasize how these methods can be integrated into industrial control systems and how they can increase cybersecurity by combining them with deceptive patch solutions. The discussed techniques represent an approach to improving the network and system security by making it more challenging for attackers to predict their targets and attack methods. The acquired results demonstrate that the suggested hybrid method improves the application of deception to software patching prediction, reflecting enhanced patch security.

With the increasing prevalence of cyber threats, robust network security has become imperative. Traditional centralized machine learning models for network intrusion detection systems (NIDS) face significant challenges related to data confidentiality, scalability, and centralization risks. Federated Learning (FL) offers a decentralized approach that allows multiple clients to train a shared model while keeping their data local collaboratively. This technique is particularly relevant to attack prediction in cybersecurity, where organizations may be hesitant to share sensitive data due to privacy and security concerns. This paper investigates the application of FL on the UNSW-NB15 dataset to predict network attacks. The study demonstrates the potential of FL to achieve high accuracy and minimal false negatives in attack detection while preserving data confidentiality. The results visualized in the confusion matrix highlight the effectiveness of FL in distinguishing between normal and malicious network traffic, making it a promising approach for real-world cybersecurity applications. By leveraging FL, organizations can improve their network security infrastructure while reducing the risks associated with centralized data processing.

This study can be considered a vital development in the field of cyber security. Today, the ever-changing and evolving structure of cyber threats constantly challenges defense mechanisms and requires the development of innovative solutions. In this context, the application of the Naive Bayes approach enriched with genetic algorithm offers a significant contribution to existing methodologies in this field. In particular, the use of genetic algorithm in cyber-attack detection optimizes classification processes by determining the most appropriate features from data sets and thus provides a more effective detection mechanism. The integration of the Naive Bayes classifier makes it possible to detect cyber-attacks precisely and quickly based on these selected features. Empirical studies and evaluations have shown that this approach provides superior sensitivity rates and lower false positive rates than traditional techniques, demonstrating its potential to overcome the limitations of existing methods in the field of cybersecurity. These findings can be considered an important step in making cybersecurity strategies more efficient and adaptable, especially considering the constantly evolving and unpredictable nature of cyber threats. The results of this study highlight the importance of developing innovative and effective solutions in the field of cybersecurity and provide a basis for further research in this field.