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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Applied Information System and Management
Tsauri, Muhammad Shofian
Unknown Affiliation
Computer Science & IT Languange, Linguistic, Communication & Media

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Human Vulnerabilities to Social Engineering Attacks: A Systematic Literature Review for Building a Human Firewall Tsauri, Muhammad Shofian
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9585

Abstract

Social engineering attacks exploit human psychology to deceive individuals into compromising information security, making the human element a critical vulnerability in cybersecurity systems. This study aims to identify and analyze patterns of human susceptibility in social engineering through a systematic literature review (SLR). Guided by the PRISMA 2020 protocol, a total of 865 articles were initially retrieved from databases such as Scopus, IEEE Xplore, ResearchGate, and Google Scholar. After applying strict inclusion and exclusion criteria, 39 peer-reviewed articles published between 2020 and 2024 were selected for thematic synthesis. The results reveal recurring human vulnerability factors including low security awareness, emotional manipulation (e.g., fear, urgency), overtrust in authority, and lack of behavioral control. These vulnerabilities manifest in predictable victim profiles and behavioral patterns, which are often exploited through phishing, pretexting, and other deception-based tactics. Furthermore, the review highlights the limitations of current mitigation strategies that focus solely on technical solutions without integrating human behavior models. The findings serve as a conceptual foundation for building a “human firewall,” emphasizing awareness, vigilance, and behavioral training as integral components of social engineering defense. This study also lays the groundwork for the development of a human-centric detection model in future research, particularly in the context of mobile banking.
Profile Matching in Python to Identify Tourist Destinations for The Development of National Tourism Utami, Meinarini Catur; Aini, Qurrotul; Fetrina, Elvi; Tsauri, Muhammad Shofian; Safitri, Mella
Applied Information System and Management (AISM) Vol. 8 No. 2 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i2.46683

Abstract

Tourism has emerged as a key contributor to state revenue and has played a crucial role in national economic recovery following the Covid-19 pandemic, establishing itself as a new engine for the country’s economic growth. In response to this, the government—through the Ministry of Tourism and Creative Economy—is actively promoting development in the tourism sector to enhance numerous tourist attractions in Indonesia, which are renowned for their beauty and recognized globally. However, the Ministry faces several challenges, including the need for technological advancements and the expansion of infrastructure. Addressing these needs will require substantial financial investment to optimize all tourism sites. This study aims to identify 22 tourist attractions across Indonesia using the Profile Matching method in Python programming, based on seven established criteria. The uniqueness about this study is that this research implemented  a simple Python script and the tourist attractions are spread across all regions of Indonesia. The results of the study highlight three tourist destinations that warrant government attention for optimization: Lake Maninjau, Rantepao, and Seminyak. To facilitate improvement, several areas require focus, including upgrading access roads, enhancing basic facilities, fostering community participation, developing human resources, and leveraging technology. The analysis using Python demonstrates that the software performs efficiently, processing 22 data points with seven criteria in a mere 0.06 seconds.
Co-Authors Elvi Fetrina Meinarini Catur Utami Qurrotul Aini Safitri, Mella