Phishing leverages people’s tendency to share personal information online. Phishing attacks often begin with an email and can be used for a variety of purposes. The cybercriminal will employ social engineering techniques to get the target to click on the link in the phishing email, which will take them to the infected website. These attacks become more complex as hackers personalize their fraud and provide convincing messages. Phishing with a malicious URL is an advanced kind of cybercrime. It might be challenging even for cautious users to spot phishing URLs. The researchers displayed different techniques to address this challenge. Machine learning models improve detection by using URLs, web page content and external features. This article presents the findings of an experimental study that attempted to enhance the performance of machine learning models to obtain improved accuracy for the two phishing datasets that are used the most commonly. Three distinct types of tuning factors are utilized, including data balancing, hyper-parameter optimization and feature selection. The experiment utilizes the eight most prevalent machine learning methods and two distinct datasets obtained from online sources, such as the UCI repository and the Mendeley repository. The result demonstrates that data balance improves accuracy marginally, whereas hyperparameter adjustment and feature selection improve accuracy significantly. The performance of machine learning algorithms is improved by combining all fine-tuned factors, outperforming existing research works. The result shows that tuning factors enhance the efficiency of machine learning algorithms. For Dataset-1, Random Forest (RF) and Gradient Boosting (XGB) achieve accuracy rates of 97.44% and 97.47%, respectively. Gradient Boosting (GB) and Extreme Gradient Boosting (XGB) achieve accuracy values of 98.27% and 98.21%, respectively, for Dataset-2.

Owing to the latest advancements in networking devices and functionalities, there is a need to build future intelligent networks that provide intellectualization, activation, and customization. Software-defined networks (SDN) are one of the latest and most trusted technologies that provide a method of network management that provides network virtualization. Although traditional networks still have a strong presence in the industry, software-defined networks have begun to replace them at faster rates. When network technologies emerge at a steady rate, SDN will be implemented at higher rates in the upcoming years in all fields. Although SDN technology removes the complexity of tying control and data plane together over traditional networks, certain aspects such as security, controllability, and economy of network resources are vulnerable. Among these aspects, security is one of the main concerns that are to be viewed seriously as far as the applications of SDN are concerned. This paper presents the most recent security issues SDN environment followed by preventive mechanisms. This study focuses on Internet control message protocol (ICMP) attacks in SDN networks. This study proposes a security policy protocol (SPP) to detect attacks that target devices such as switches and the SDN controller in the SDN networks. The mechanism is based on ICMP attacks, which are the main source of flooding attacks in the SDN networks. The proposed model focuses on two aspects: security policy process verification and client authentication verification. Experimental results shows that the proposed model can effectively defend against flooding attacks in SDN network environments.

On-demand computing ability and efficient service delivery are the major benefits of cloud systems. The limitation in resource availability in single data centers causes the extraction of additional resources from the cloud providers group. The federation scheme dynamically increases resource availability in response to service requests. The dynamic increase in resource count leads to excessive energy consumption, maximum cost, and carbon footprints emission. Hence, the reduction of resources is the major requirement to construct the optimized cloud source models for profit maximization without considering energy mix and CO₂. This paper proposes the novel migration method to reduce carbon emissions and energy consumption. The initial stage in the proposed work is the categorization of data centers based on the MIPS and cost prior to job allocation offers scalable and efficient services and resources to the cloud user. Then, the job with the maximum size is allotted to the VM only if its capacity is less than the cumulative capacity of data centers. A novel migration based on overutilized and underutilized levels provides the services to the user even if the particular VM fails. The proposed work offers efficient maintenance of resource availability and maximizes the profit of the cloud providers associated with the federated cloud environment. The comparative analysis of the proposed algorithm with the existing methods regarding the response time, accuracy, profit, carbon emission, and energy consumption assures the effectiveness in a confederated cloud environment.

This chapter explores the field of digital twin technologies as an innovative approach to examine the continuous change in healthcare systems. The utilization of digital twins offers novel methods to improve resource optimization, patient care, and operational efficiency as the healthcare sector faces previously unheard-of difficulties. The current research delves deeply into the fundamental principles and uses of digital twin technology in the healthcare sector, emphasizing its capacity to transform healthcare facilities, telemedicine, and patient outcomes. Through the evaluation of case studies and new developments, the authors highlight the critical function that digital twins perform in advancing predictive analytics, remote monitoring, and customized care. In order to help policymakers, healthcare professionals, and tech entrepreneurs navigate the changing environment of healthcare systems in the digital age, this chapter aspires to provide a thorough knowledge of the revolutionary potential of digital twins in healthcare.