Cyber threats are attempts to secure unauthorized access to, change, or delete private information, to demand money from victims, or to disrupt business. Cybercrime includes everything from identity theft, malware threats, email and online fraud, to bank fraud. Businesses and individuals use this method to guard their data centers and other digital systems. The lack of scalability, sluggish response times, and inability to spot advanced and insider threats are among some of the problems with conventional approaches to network security. These flaws highlight the need for research to build more efficient and all-encompassing security methods to guard against the expanding variety of network attacks. Cybercriminals use AI and data poisoning, as well as model theft strategies to automate their attacks. A cyber security technique based on artificial intelligence is presented in this study for financial sector management (CS-FSM). In order to map and prevent unexpected risks from devouring a business, artificial intelligence is one of the best technologies. Using the proposed technique, cyberattack problems can be classified and solved. To ensure the security of financial sector information, algorithms such as the Enhanced Encryption Standard (EES) encrypt and decrypt data. By learning from the training data, the K-Nearest Neighbor (KNN) algorithm produces predictions. In the financial sector, it is used to detect and stop malware attacks. The proposed method increases cyber security systems’ performance by increasing their defense against cyberattacks. CS-FSM enhances data privacy (18.3%), scalability (17.2%), risk reduction (13.2%), data protection (16.2%), and attack avoidance (11.2%) ratios.

The proliferation of not only power electronics supported consumption technologies but also the expansion of the renewable-based distributed generation (DG) systems has given rise to severe power quality (PQ) phenomena in consort with the offered technical, economic and environmental benefits under deregulated environment. The forthcoming complexity of distribution power networks caused by incorporation of a large number of DG units in deregulated electricity market unquestionably makes PQ assessment procedure a quite cumbersome one. In present work, an analytic hierarchy process (AHP) inspired methodology is proposed for PQ assessment of distorted distribution power systems under the presence of renewable-based DGs. The proposed PQ assessment approach is based on formulating a unified power quality index (UPQI) for assessing the overall PQ performance of individual buses of the network along with the entire distribution network (DN) considered taking four PQ phenomena, viz. voltage harmonics, voltage sags, voltage unbalance and steady-state voltage profile at each bus into account. The proposed PQ assessment approach can streamline the PQ performance assessment as well as benchmarking procedure of modern DNs up to a thoroughgoing extent. The application significance of the presented methodology is established by utilizing it on an IEEE 13 bus test distribution system modified through incorporating the nonlinear loads and DG systems based on three types of RES namely, photovoltaic (PV), wind and fuel cell, in MATLAB/Simulink environment. The results achieved validates the efficacy of the presented approach in assessing the overall PQ performance of each of the buses and the entire DN along with benchmarking it with respect to the threshold level of unity. Based on obtained results, also the comparative analysis is performed among PQ performances of DN with selected three RES based DGs. Moreover, the impact of the employing the custom power devices (CPDs) as well as excessive penetration level of renewable energy over PQ performance of distribution network, are also investigated by the application of the formulated index.

Today, security is a major challenge linked with computer network companies that cannot defend against cyber-attacks. Numerous vulnerable factors increase security risks and cyber-attacks, including viruses, the internet, communications, and hackers. Internets of Things (IoT) devices are more effective, and the number of devices connected to the internet is constantly increasing, and governments and businesses are also using these technologies to perform business activities effectively. However, the increasing uses of technologies also increase risks, such as password attacks, social engineering, and phishing attacks. Humans play a major role in the field of cybersecurity. It is observed that more than 39% of security risks are related to the human factor, and 95% of successful cyber-attacks are caused by human error, with most of them being insider threats. The major human factor issue in cybersecurity is a lack of user awareness of cyber threats. This study focuses on the human factor by surveying the vulnerabilities and reducing the risk by focusing on human nature and reacting to different situations. This study highlighted that most of the participants are not experienced with cybersecurity threats and how to protect their personal information. Moreover, the lack of awareness of the top three vulnerabilities related to the human factor in cybersecurity, such as phishing attacks, passwords, attacks, and social engineering, are major problems that need to be addressed and reduced through proper awareness and training.

The main intent of the cloud computing to provide utilities to the demands of the users that are booming day by day. To meet the requirements, existing scheduling algorithms focus on the improving the performance and neglecting the energy consumed to fulfill those demands. Hence, we propose a new Hybrid Scheduling Algorithm (HS) which is based on the Genetic Algorithm (GA) and Flower Pollination based Algorithm (FPA) for cloud environments. The proposed scheduling algorithm has surpassed in terms of performance across various parameters, i.e. completion time, resource utilization, cost of computation, and energy consumption for both cloud environments than the existing scheduling algorithms (GA and FPA). The simulation results revealed that HS has demonstrated maximum resource utilization with minimum energy consumption in less completion time for the execution of the tasks as compared to the existing scheduling algorithms in both environments. The simulation results have shown that HS has utilization of the resources, 36% better than GA and 16% better than FPA in homogeneous environment whereas in heterogenous environment, HS has performed 12% better than GA and 3.8% better than FPA. The performance of HS has an improvement of 2.6% from FPA and 6.9% from the GA for completion time in homogeneous environment whereas the completion time of the HS is reduced by 17.8% from FPA and 33.7% from GA in heterogeneous environment. For energy consumption, HS has improved 22% than FPA and 11% from GA in the homogeneous environment and HS is 4% better than FPA and 14% from GA in heterogeneous environment.

A photovoltaic-powered electric vehicle is a complex system that necessitates the use of a high-performance control algorithm. This paper aims to boost the performance of a photovoltaic system by employing a suitable algorithm to control the power interface. The main goal is to find an effective and optimal control law that will enable the photovoltaic generator (GPV) to generate the maximum amount of power possible. The main facts dealt with in this article are the mathematical simulation of the photovoltaic system, its function, and its characteristics, considering the synthesis of the step-up converter and the analysis of the maximum power point tracking algorithm. This study examines and compares two control techniques for extracting full power from the solar energy system. These two techniques are the classical “perturbation and observation” (P&O) method and the intelligent solution “particle swarm optimization (PSO) method.” The PSO solution is tested for two versions: the online PSO version and the table PSO version. The Simulink/MATLAB tool is used for simulation and comparative experiments based on the performance metrics provided. The study revealed that smart technology delivers improved efficiency than the classic edition.