Malware is a significant threat that has grown with the spread of technology. This makes detecting malware a critical issue. Static and dynamic methods are widely used in the detection of malware. However, traditional static and dynamic malware detection methods may fall short in advanced malware detection. Data obtained through memory analysis can provide important insights into the behavior and patterns of malware. This is because malwares leave various traces on memories. For this reason, the memory analysis method is one of the issues that should be studied in malware detection. In this study, the use of memory data in malware detection is suggested. Malware detection was carried out by using various deep learning and machine learning approaches in a big data environment with memory data. This study was carried out with Pyspark on Apache Spark big data platform in Google Colaboratory. Experiments were performed on the balanced CIC-MalMem-2022 dataset. Binary classification was made using Random Forest, Decision Tree, Gradient Boosted Tree, Logistic Regression, Naive Bayes, Linear Vector Support Machine, Multilayer Perceptron, Deep Feed Forward Neural Network, and Long Short-Term Memory algorithms. The performances of the algorithms used have been compared. The results were evaluated using the Accuracy, F1-score, Precision, Recall, and AUC performance metrics. As a result, the most successful malware detection was obtained with the Logistic Regression algorithm, with an accuracy level of 99.97% in malware detection by memory analysis. Gradient Boosted Tree follows the Logistic Regression algorithm with 99.94% accuracy. The Naive Bayes algorithm showed the lowest performance in malware analysis with memory data, with an accuracy of 98.41%. In addition, many of the algorithms used have achieved very successful results. According to the results obtained, the data obtained from memory analysis is very useful in detecting malware. In addition, deep learning and machine learning approaches were trained with memory datasets and achieved very successful results in malware detection.

The rapid expansion of the Industrial Internet of Things (IIoT) has revolutionized industrial automation and introduced significant cybersecurity challenges, particularly for supervisory control and data acquisition (SCADA) systems. Traditional intrusion detection systems (IDSs) often struggle to effectively identify and mitigate complex cyberthreats, such as denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks. This study proposes an advanced IDS framework integrating machine learning, deep learning, and hybrid models to enhance cybersecurity in IIoT environments. Using the WUSTL-IIoT-2021 dataset, multiple classification models—including decision tree, random forest, multilayer perceptron (MLP), convolutional neural networks (CNNs), and hybrid deep learning architectures—were systematically evaluated based on key performance metrics, including accuracy, precision, recall, and F1 score. This research introduces several key innovations. First, it presents a comparative analysis of machine learning, deep learning, and hybrid models within a unified experimental framework, offering a comprehensive evaluation of various approaches. Second, while existing studies frequently favor hybrid models, findings from this study reveal that the standalone MLP model outperforms other architectures, achieving the highest detection accuracy of 99.99%. This outcome highlights the critical role of dataset-specific feature distributions in determining model effectiveness and calls for a more nuanced approach when selecting detection models for IIoT cybersecurity applications. Additionally, the study explores a broad range of hyperparameter configurations, optimizing model effectiveness for IIoT-specific intrusion detection. These contributions provide valuable insights for developing more efficient and adaptable IDS solutions in IIoT networks.

Wireless Sensor Networks—WSNs, an important part of IoT—consist of sensor nodes with limited processing, memory capacities, and energy. Wireless Sensor Networks face many dangers as they are often distributed into untrusted regions. The accuracy of the data obtained in a WSN, where security threats cannot be prevented, is also questioned. In WSNs, the authentication of the resources and the data can be verified with the authentication mechanism. Authentication in WSNs allows the node to verify whether data have been sent from authorized sources and protects the original data from changes. However, there are some deficiencies in terms of security in existing authentication protocols such as ID spoofing attacks. In addition, blockchain, one of the emerging technologies, gives significant successful results in security applications. Cryptographically secured, immutable, non-repudiable, irrevocable, auditable, and verifiable can be given as security-related characteristics of the blockchain. This study aims to use these features of the blockchain in WSNs. In this study, a new blockchain-based authentication protocol was developed for WSNs. Based on the study’s system model, sensor nodes, cluster nodes, base station, and blockchain networks were created using a private blockchain, and users. A detailed security analysis was carried out for the study. At the same time, efficiency analysis was performed by implementing the proposed model on the WiSeN sensor node.

Artificial Intelligence (AI) analytics, tools, and coding are increasingly used to improve quality in every part of the industry. However, such tools find their place in bugs, vulnerabilities, security points, duplications, and many more. Researchers around the world have conducted many academic studies with AI until today. The increasing development and use in the field in the last decade led to the need to research the publication amount, application areas, and achievements of countries in technology. This article used systematic research and literature review to determine the structured approach to the concepts of AI, machine learning, deep learning, and neural networks that can be searched in the TR Index. Although the results do not have an essential place in the TR Index, they can be evaluated as an accelerated increase of around 70% in the last three years. In the field distribution, engineering ranked first with 673 units. In addition, a decrease in the indexation rate of the subject is also observed. The results of this study may lead to the examination of the indexing of many publications made in our country in the TR Index.