In addition to their conventional use in electric motor drives, DC-DC converters have a variety of other uses, such as energy storage, energy conversion, cyber security systems, uninterruptible power ...supplies, and renewable energy systems. An innovative DC-DC converter is suggested in this article. Designing a new, high-gain DC-DC converter scheme known as a double-switch SEPIC-buck converter (DSSB) is possible after making some adjustments to the SEPIC converter that is currently known in accordance with accepted techniques. The output voltage magnitude of the proposed converter is either larger than or less than the input voltage magnitude and is the same sign as the input voltage. According to the theoretical and analytical study that has been supported by the real-world application, high voltage gain, low switching stress, and low inductor current ripple are the main characteristics of the proposed DSSB converter. The related small-signal model was also used to build the closed-loop system. The frequency response and output voltage behavior were investigated when the input source voltage abruptly changed as a step function. Based on the comparison study with other DC-DC converters, the DSSB converter outperforms currently known DC-DC converters such as Buck, SEPIC, Boost, Buck-Boost, and other SEPIC converter topologies in terms of voltage gain, harmonic content, normalized current ripple, dynamic performance, and efficiency. Additionally, the frequency response and control of the proposed converter using an alternate current (AC), small-signal, analysis-based, current-mode control technique are both provided. Thus, the DSSB is regarded as safe in overcurrent situations because of the small-signal analysis with the current control strategy. As a result of the verification of the proposed control technique, the resistance to changes in the DSSB parameters, improved dynamic performance, and higher control accuracy are further advantages of current-mode control based on small-signal analysis over other control approaches (PI controllers). Finally, the experimental and simulation results from Simplorer 7 and MATLAB/Simulink are used to validate the findings of the analytical and comparative investigation.
Online banking has become increasingly popular in recent years, making it a target for cyberattacks. Banks have implemented various user authentication methods to protect their customers' online ...accounts. This paper reviews the state-of-the-art user authentication methods used in online banking and potential cyber threats. This paper starts by exploring different user authentication methods, such as knowledge-based authentication (KBA), biometrics-based authentication (BBA), possession-based authentication (PBA), and other methods. The advantages and disadvantages of each user authentication method are then discussed. Furthermore, the paper discusses the various cyber threats that can compromise user authentication for online banking systems, such as malware attacks, social engineering, phishing attacks, man-in-the-middle (MiTM) attacks, denial of service (DoS) attacks, session hijacking, weak passwords, keyloggers, SQL injection, and replay attacks. Also, the paper explores the user authentication methods used by popular banks, which can provide insights into best practices for safeguarding online banking accounts and future user authentication methods in online banking and cyber threats. It states that the increasing use of BBA, two-factor authentication (2FA), and multi-factor authentication (MFA) will help improve the security of online banking systems. However, the paper also warns that new cyber challenges will emerge, and banks need to be vigilant in protecting their customers' online banking accounts.
A robust authentication method is needed to protect online user accounts and data from cyber-attacks. Using only passwords is insufficient because they can be easily stolen or cracked. Multi-factor ...authentication (MFA) increases security by requiring two or more verification factors from the user before granting access to a resource such as an online account or an application. MFA is essential to a strong identity and access management (IAM) policy. This study evaluates and contrasts several MFA methods for online systems, including Microsoft Authenticator, FIDO2 security keys, SMS, voice calls, and biometrics. We assess these methods based on four criteria: security, usability, cost, and compatibility. We discover that only some MFA methods excel across the board. The best MFA method will depend on the organization's and users' specific needs and preferences. Each MFA method has benefits and drawbacks on its own. Based on our analysis, we do, however, make some general observations and recommendations, such as preferring FIDO2 security keys and certificate-based authentication for high-security scenarios, choosing Microsoft Authenticator and biometrics for high-usability scenarios, and avoiding SMS and voice calls for low-security and low-usability scenarios.
Research on human-computer interaction currently focuses on enhancing system usability by establishing an appropriate user interface (UI) that depends on users’ features. Online users typically have ...different perceptions of their favored interface design depending on their preferences. Thus, those interface preferences could be utilized to recognize online users’ identities. User authentication is another critical issue that should be considered to improve online security mechanisms without compromising usability. This study investigates the feasibility of using UI preferences as evidence of user identity. The proposed method applies to the design preferences of users dealing with online systems (e.g., e-exam and e-banking). These preferences are closely associated with individual characteristics, whether physical, cognitive, psychological, psychomotor, demographic, or experience based. Many design characteristics could be used in online systems; for example, the e-exam interface design may use features such as the font (size, color, and face), the number of questions per page, background color, questions group, timer type, and sound alert. The feasibility evaluation of this study indicated that 96.8% of research participants have variations in their preferences, and each participant kept 94.5% of their design preferences throughout different sessions.
This study investigates the current state of mobile shopping in Jordan and the integration of big data and AI technologies in this context. A mixed-methods approach, combining qualitative and ...quantitative data collection techniques, utilized to gather comprehensive insights. The survey questionnaire distributed to 105 individuals engaged in mobile shopping in Jordan. The findings highlight the popularity of mobile shopping and the preference for mobile apps as the primary platform. Personalized product recommendations emerged as a crucial factor in enhancing the mobile shopping experience. Privacy concerns regarding data sharing were present among respondents. Trust in AI-powered virtual assistants varied, indicating the potential for leveraging AI technologies. Respondents recognized the potential of big data and AI in improving the mobile shopping experience. The study concludes that businesses can enhance mobile shopping by utilizing AI-powered virtual assistants and prioritizing data security. The findings contribute to understanding mobile shopping dynamics and provide guidance for businesses and policymakers in optimizing mobile shopping experiences and driving economic growth in Jordan's digital economy. Future research and implementation efforts are encouraged to harness the potential of big data and AI in the mobile shopping landscape.
Web attacks often target web applications because they can be accessed over a network and often have vulnerabilities. The success of an intrusion detection system (IDS) in detecting web attacks ...depends on an effective traffic classification system. Several previous studies have utilized machine learning classification methods to create an efficient IDS with various datasets for different types of attacks. This paper utilizes the Canadian Institute for Cyber Security’s (CIC-IDS2017) IDS dataset to assess web attacks. Importantly, the dataset contains 80 attributes of recent assaults, as reported in the 2016 McAfee report. Three machine learning algorithms have been evaluated in this research, namely random forests (RF), k-nearest neighbor (KNN), and naive bayes (NB). The primary goal of this research is to propose an effective machine learning algorithm for the IDS web attacks model. The evaluation compares the performance of three algorithms (RF, KNN, and NB) based on their accuracy and precision in detecting anomalous traffic. The results indicate that the RF outperformed the NB and KNN in terms of average accuracy achieved during the training phase. During the testing phase, the KNN algorithm outperformed others, achieving an average accuracy of 99.4916%. However, RF and KNN achieved 100% average precision and recall rates compared to other algorithms. Finally, the RF and KNN algorithms have been identified as the most effective for detecting IDS web attacks.
Cloud computing technology is known as a distributed computing network, which consists of a large number of servers connected via the internet. This technology involves many worthwhile resources, ...such as applications, services, and large database storage. Users have the ability to access cloud services and resources through web services. Cloud computing provides a considerable number of benefits, such as effective virtualized resources, cost efficiency, self-service access, flexibility, and scalability. However, many security issues are present in cloud computing environment. One of the most common security challenges in the cloud computing environment is the trojan horses. Trojan horses can disrupt cloud computing services and damage the resources, applications, or virtual machines in the cloud structure. Trojan horse attacks are dangerous, complicated and very difficult to be detected. In this research, eight machine learning classifiers for trojan horse detection in a cloud-based environment have been investigated. The accuracy of the cloud trojan horses detection rate has been investigated using dynamic analysis, Cukoo sandbox, and the Weka data mining tool. Based on the conducted experiments, the SMO and Multilayer Perceptron have been found to be the best classifiers for trojan horse detection in a cloud-based environment. Although SMO and Multilayer Perceptron have achieved the highest accuracy rate of 95.86%, Multilayer Perceptron has outperformed SMO in term of Receiver Operating Characteristic (ROC) area.
An innovative breakthrough in computer science is cloud computing and involves several computers which are connected via the Internet or it is dispersed over a network. A large database, services, ...applications, software and resources are an integral part of this technology. It has the capability to operate a program or applications on numerous connected computers simultaneously and permits the users to enter applications and resources through a web browser or web service via the Internet anytime and anywhere. Current susceptibility in elementary technologies gravitates to expose doors for intrusions. Cloud computing offers enormous advantages such as cost reduction, dynamic virtualized resources, significant data storage and enhanced productivity. At the same time, numerous risks occur regarding security and intrusions, for example, worm can intercept cloud computing services, impair service, application or virtual in the cloud formation. Worm attacks are now more complex and resourceful making intruders more difficult to detect than previously. The motivation of this research is founded on ramifications presented by the worms. This paper presents different intrusion detection systems affecting cloud resources and service. Moreover, this paper illustrates how genetic algorithm can be integrated in detecting worm attacks in cloud computing more efficiently.
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