Because of the existence of Covid-19 and its variants, health monitoring systems have become mandatory, particularly for critical patients such as neonates. However, the massive volume of real-time data generated by monitoring devices necessitates the use of efficient methods and approaches to respond promptly. A fog-based architecture for IoT healthcare systems tends to provide better services, but it also produces some issues that must be addressed. We present a bidirectional approach to improving real-time data transmission for health monitors by minimizing network latency and usage in this paper. To that end, a simplified approach for large-scale IoT health monitoring systems is devised, which provides a solution for IoT device selection of optimal fog nodes to reduce both communication and processing delays. Additionally, an improved dynamic approach for load balancing and task assignment is also suggested. Embedding the best practices from the IoT, Fog, and Cloud planes, our aim in this work is to offer software architecture for IoT-based healthcare systems to fulfill non-functional needs. 4 + 1 views are used to illustrate the proposed architecture.

Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures. This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems. The objectives include Investigating the performance of various classifiers, such as Gaussian Naïve Bayes, K Nearest Neighbors (KNN), Stochastic Gradient Descent Classifier (SGDC), and Decision Tree, in detecting Windows malware. Evaluating the accuracy, efficiency, and suitability of each classifier for real-world malware detection scenarios. Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers. Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence. The study employs a structured methodology consisting of several phases: exploratory data analysis, data preprocessing, model training, and evaluation. Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements. Data preprocessing includes cleaning, feature encoding, dimensionality reduction, and optimization to prepare the data for training. Model training utilizes various supervised classifiers, and their performance is evaluated using metrics such as accuracy, precision, recall, and F1 score. The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection. Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware. Additionally, insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses. Overall, this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications.