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All Journal Indonesian Journal of Electrical Engineering and Computer Science INOVTEK Polbeng - Seri Informatika Journal of Applied Computer and Information Technology
Mohamad Fadli Zolkipli
Universiti Utara Malaysia
Computer Science & IT

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Enabling efficient business process mining using flatten sequential structure model Ang Jin Sheng; Jastini Mohd Jamil; Izwan Nizal Mohd Shaharanee; Mohamad Fadli Zolkipli
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp531-541

Abstract

The volume of extensible markup language (XML) format documents is increasing every day due to the development of internet and the use of XML format in business process log file. Storing business process log data in XML format is preferable due to the ability of extensible and storing data irrespective of how it will be represented. However, mining XML format data poses challenges due to its complex data structure and dimensions. This paper proposes a method to convert XML format document into a structured format without ignoring the structural information. Converting semi-structured business process log data into structured format will allow more data mining techniques and statistical test be conducted and extract information from the business process log data. The experiment in this study performs t-test on a set of synthetic data and a set of real-world data to prove that information in business process log can be extracted through normal statistical test. Empirical results show that statistical analysis can be conducted on business process log data especially in XML format after flatten sequential structure model (FSSM) is used.
Navigating Deceptive Realities: Public Perceptions and Cybersecurity Threats of Deepfake Technology Nur Anis Shafiqah Mazlan; Hapini Awang; Nur Suhaili Mansor; Mohamad Fadli Zolkipli; Bingxin Jin
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 3 (2026): August
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/fqwbw793

Abstract

The rapid advancement of deepfake technology presents a profound cybersecurity threat by seamlessly fabricating synthetic media, which severely erodes digital trust. This study aims to evaluate the community's awareness of deepfake threats and assess the necessity of multifaceted mitigation strategies. Employing a quantitative methodology, an online survey was conducted with 67 respondents, predominantly young adults, to measure their exposure, psychological vulnerability, and perspectives on cybersecurity countermeasures. The findings reveal that while the public is generally aware of deepfakes, 75 per cent struggle to visually differentiate manipulated content from authentic media. Consequently, the proliferation of deepfakes has diminished perceived societal trust in online information. Notably, there is a unanimous consensus among respondents demanding strict legal frameworks and comprehensive public education to combat this menace. The study concludes that relying exclusively on technical detection algorithms is insufficient. Instead, preserving information integrity requires a multidisciplinary approach combining robust technological defences, proactive policymaking, and widespread digital literacy trainings.
Cybersecurity risk management for digital retail: Strategies, frameworks and implementation Nurul Hanna Mohd Saleh; Nur Anis Atiqah Hussin; Maharubiney Suthursan Kumar; Mohamad Fadli Zolkipli
Journal of Applied Computer and Information Technology Vol. 1 No. 1 (2026): Journal of Applied Computer and Information Technology (JACoIT)
Publisher : Global Research Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67131/jacoit.v1i1.6

Abstract

The digital transformation of wholesale, supermarket and e-commerce operations has rendered cybersecurity a cornerstone of business resilience in the modern trading and retail sector. This study examines the critical cybersecurity threats facing this industry through a qualitative risk assessment, identifying phishing, ransomware, supply chain attacks and data breaches as the most prevalent and impactful risks. The analysis underscores that these threats exploit the sector's inherent characteristics: high transaction volumes, reliance on interconnected digital ecosystems and the processing of large quantities of sensitive customer data. In response, the study advocates for a strategic, integrated approach to cybersecurity governance. It proposes that combining the risk-based structure of the NIST Cybersecurity Framework (CSF) with the prescriptive payment security controls of the PCI DSS and the governance rigor of ISO/IEC 27001 provides a comprehensive model for effective risk mitigation. The findings highlight that moving beyond compliance-centric checklists to develop proactive cyber resilience is crucial. This requires strategic investments in foundational controls, robust incident response planning and strict third-party risk management to safeguard operations, ensure regulatory adherence and maintain customer trust in an increasingly hostile digital landscape.
Cybersecurity risk management strategy for AI and SaaS platforms: A NIST framework approach Muhammad Zaim Zainuddin; Muhammad Sharin Yasin; Muhammad Azerul Azaman; Mohamad Fadli Zolkipli
Journal of Applied Computer and Information Technology Vol. 1 No. 1 (2026): Journal of Applied Computer and Information Technology (JACoIT)
Publisher : Global Research Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67131/jacoit.v1i1.9

Abstract

The rapid growth in Information Technology (IT) and Software industries particularly within Artificial Intelligence (AI) and Software-as-a-Service (SaaS) has accelerated the pace of the fourth industrial innovation, and at the same time, this growth produced complex vulnerabilities to security. The conventional defense mechanisms are becoming less effective over time against the evolving threats like adversarial data poisoning, API exploits and advanced ransomware attacks targeting cloud infrastructures. The primary goals of this paper are to address these issues by developing a comprehensive risk management plan that is based on the NIST Cybersecurity Framework (CSF). Additionally, this study identifies critical vulnerabilities in modern AI and SaaS environments using a qualitative risk assessment approach and a likelihood-versus-impact matrix. The analysis shows that data breaches and API exploitation are the most serious threats, which have significant impact on organization operations and the high likelihood. Moreover, the findings indicate that incorporating the NIST CSF core capabilities such as Identify, Protect, Detect, Respond and Recover is a well-organized framework of minimizing these high-priority threats using layered preventive and detective controls. Ultimately, the results highlight how important it is to embrace standards-based systems to shift organizations from reactive security measures to proactive resilience to ensure the integrity and continuity of the interconnected software ecosystem.
Co-Authors Ang Jin Sheng Bingxin Jin Hapini Awang Izwan Nizal Mohd Shaharanee Jastini Mohd Jamil Maharubiney Suthursan Kumar Muhammad Azerul Azaman Muhammad Sharin Yasin Muhammad Zaim Zainuddin Nur Anis Atiqah Hussin Nur Anis Shafiqah Mazlan Nur Suhaili Mansor Nurul Hanna Mohd Saleh