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All Journal Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Ekonomi dan Bisnis Digital (MINISTAL) Formosa Journal of Science and Technology (FJST) Formosa Journal of Applied Sciences (FJAS) Formosa Journal of Computer and Information Science Indonesian Journal of Advanced Research (IJAR) Journal of Electrical Engineering
Mahjabeen, Farhana
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Assessing the Vulnerabilities of Mobile Banking Applications and Developing Strategies to Improve Their Security Hossain, Mohammad Amir; Raza, Md. Adil; Mahjabeen, Farhana; Rahman, Jami Yaseer
Jurnal Ekonomi dan Bisnis Digital Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ministal.v4i1.13371

Abstract

Mobile banking apps have changed the way financial services are provided, allowing users to perform banking operations from anywhere. Though this progress has granted consumers unprecedented convenience, it has also opened new doors to vulnerabilities creating an ideal target for hackers on mobile banking applications. It explores the security issues, vulnerable regions of mobile banking applications such as using insecure communication, weak authentication, unprotected storage, and susceptible to malware. Through empiric testing and existing vulnerability assessment frameworks, critical vulnerabilities and their potential consequences on user data and financial systems are identified. It also recommends specific measures to reduce these vulnerabilities, such as upgraded encryption protocols, multifactor authentication (MFA), secure coding strategies and realtime threat monitoring. Through the identification and exploration of these vulnerabilities, the study seeks to contribute to the ongoing efforts of enhancing the security and resilience of mobile banking applications, which ultimately protects user trust and ensures adherence to regulatory standards.
Evaluating the Human Factor in Bank Cybersecurity: Strategies for Improving Employee Awareness and Reducing Insider Threats Raza, Md. Adil; Hossain, Mohammad Amir; Mahjabeen, Farhana; Rahman, Jami Yaseer; Rahman, Taqi Yaseer
Indonesian Journal of Advanced Research Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijar.v4i1.13399

Abstract

The human factor proves to be a considerable weakness in the banking security infrastructure even when advanced cybersecurity technologies are being implemented by various banks. Breaches involving employees have been a huge factor in bank cybersecurity issues this research, look at the impact of employees in bank security, and how breaches have an insidious human behavior element. It notes risks like phishing attacks, negligence and intentional insider threats and heavy strategies need to respond to these risks. The research shows employing targeted awareness programs, continuous training, and behavioural analytics bases can help organizations reduce such humanrelated vulnerabilities through a combination of case studies, employee surveys and expert consultations. It also addresses the adoption of Zero Trust Architecture and continuous monitoring of activities to detect and prevent insider threats. Fostering employee awareness and building a culture of security will protect banks from both outside and inside cybersecurity threats. It helps derive actionable findings to create a resilient humancentric cybersecurity structure in the banking domain.
Ensuring Cybersecurity and Resilience in Solar Smart Grids: Challenges and Solutions Hossain, Mohammad; Mahjabeen, Farhana
Journal of Electrical Engineering Vol. 2 No. 1 (2025): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jte.v2i1.3877

Abstract

Solar power integration with smart grids has made modern energy systems more efficient and sustainable and increased operational flexibility. The technological benefits of solar smart grids are matched by parallel security issues that need solutions for proper solar smart grid operation. The three main cybersecurity hazards, namely data breaches, denial-of-service attacks, and ransomware, create critical risks that compromise the stability of power grids, consumer privacy, and fundamental infrastructure protection. Solar smart grids need strong resilience against security threats from both cyber and physical sources because their future stability depends on this capability during expanding decentralized energy resource integration. The present work examines major cybersecurity issues that solar smart grids encounter. It investigates methods for solutions through advanced encryption techniques combined with intrusion detection systems and strong communication protocol implementation. To improve security, the paper stresses that distributed control systems and real-time monitoring offer resilience strategies that reduce grid risks. Solar smart grids will maintain their capacity to deliver dependable, sustainable energy through the solutions to these risks.
Data Science in Solar and Wind Energy Optimization: A Review Mahjabeen, Farhana
Formosa Journal of Computer and Information Science Vol. 3 No. 2 (2024): August 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v3i2.11534

Abstract

This article explores the transformative role of artificial intelligence in optimizing solar and wind energy systems. Harvard researchers leverage extensive datasets and Microsoft Azure to analyze complex interactions between various signals, exceeding human capabilities. The article examines how AI enhances operational efficiency, stability, and sustainability of renewable energy systems, delving into fundamental AI applications and data science techniques. It further explores emerging trends like deploying advanced AI algorithms on energy grids. The research also analyzes the economic and environmental impacts of AI on renewable energy, addressing challenges such as economic barriers and regulatory frameworks. Finally, it discusses relevant regulations and standards for AI-powered renewable energy, considering diverse stakeholder perspectives
Electromechanical Devices of Adaptive and Control-Tracking Systems G.S., Kerimzade; Jain, Vishal; Mahjabeen, Farhana; Ahmad, Munir; Patil, Dipak P.; Garba, Auwal; Mahajan, Vinod Shantaram
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 3 No. 2 (2023): September 2023
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v3i2.11537

Abstract

In the presented work, some characteristics of devices of adaptive and control-tracking control systems are considered. The characteristic features of angle sensors, methods of converting analog signals with high accuracy (for example, sine-cosine transformers) occupies a leading position among the development and research of tracking systems. The modern electronic element base opens up new possibilities - the creation of tracking digital angle converters (TDAC) using the principles of digital tracking and adaptive control in them. Stability, efficiency, load form determines the reliability, accuracy, economy, service life of electromechanical automation devices, test equipment, etc. Determining the characteristics, establishing analytical relationships between the initial data and output parameters is one of the stages of the algorithm for solving the problems of designing equipment parameters for monitoring and tracking control systems, which in turn contributes to the development of a mathematical model from a system of equations, the joint solution of which allows you to establish analytical relationships between the initial data and settings.
Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review Mahjabeen, Farhana
Formosa Journal of Science and Technology Vol. 3 No. 10 (2024): October 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v3i10.11552

Abstract

Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, results, and the prevalence of ML/DL approaches. The analysis highlights the application of both classical ML algorithms, such as K-Nearest Neighbors and Random Forest, and advanced DL models, including Convolutional Neural Networks, for PV fault diagnosis.
Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review Mahjabeen, Farhana
Formosa Journal of Applied Sciences Vol. 3 No. 10 (2024): October 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjas.v3i10.11536

Abstract

The increasing global demand for renewable energy has propelled the adoption of photovoltaic systems as a key component of sustainable energy infrastructure. Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, results, and the prevalence of ML/DL approaches. The analysis highlights the application of both classical ML algorithms, such as K-Nearest Neighbors and Random Forest, and advanced DL models, including Convolutional Neural Networks, for PV fault diagnosis.
Co-Authors Ahmad, Munir G.S., Kerimzade Garba, Auwal Hossain, Mohammad Hossain, Mohammad Amir Jain, Vishal Mahajan, Vinod Shantaram Patil, Dipak P. Rahman, Jami Yaseer Rahman, Taqi Yaseer Raza, Md. Adil