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Opportunities and Challenges in AI-Driven Cybersecurity: A Systematic Literature Shahidi, Shahwali; Darmel, Farid Ahmad; Jalalzai, Safiullah; Amiri, Ghulam Ali
Journal of Social Science Utilizing Technology Vol. 2 No. 4 (2024)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i4.1541

Background. The need for more sophisticated security strategies has become apparent as the number of cyber threats grows. AI is one framework that has been shown to boost security by providing advanced threat detection and response capabilities. Nonetheless, AI integration introduces inherently ethical and privacy-related concerns. Purpose. This research examines the AI implementation factors influencing the overall performance of the AI for cybersecurity and data privacy in both critical infrastructures and financial services. Method. This research derives its data from the extensive literature published from 2019 to 2024 in notable databases such as IEEE, Science Direct, MDPI, and Wiley Library, with more than 300 records. This analysis examined, with the help of artificial intelligence tools, the patterns and recurrent problems about the place of AI in cybersecurity, setting sights on the present challenges in the domains of intrusion detection and mitigation. Results. The results indicate that better threat detection in industry is enabled by AI. However, disadvantages of bias, the need for privacy, and suboptimal data management are evident, necessitating the need for stronger machine and human-readable regulations. Conclusion. Although AI strengthens security in an age of cyber-insecurity, its shortcomings point to the need for further development. Post-quantitative encryption palliatives and integration models will be effectively handled as cybersecurity-harming threats evolve.

Opportunities and Challenges in AI-Driven Cybersecurity: A Systematic Literature Shahidi, Shahwali; Darmel, Farid Ahmad; Jalalzai, Safiullah; Amiri, Ghulam Ali
Journal of Social Science Utilizing Technology Vol. 2 No. 4 (2024)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i4.1541

Background. The need for more sophisticated security strategies has become apparent as the number of cyber threats grows. AI is one framework that has been shown to boost security by providing advanced threat detection and response capabilities. Nonetheless, AI integration introduces inherently ethical and privacy-related concerns. Purpose. This research examines the AI implementation factors influencing the overall performance of the AI for cybersecurity and data privacy in both critical infrastructures and financial services. Method. This research derives its data from the extensive literature published from 2019 to 2024 in notable databases such as IEEE, Science Direct, MDPI, and Wiley Library, with more than 300 records. This analysis examined, with the help of artificial intelligence tools, the patterns and recurrent problems about the place of AI in cybersecurity, setting sights on the present challenges in the domains of intrusion detection and mitigation. Results. The results indicate that better threat detection in industry is enabled by AI. However, disadvantages of bias, the need for privacy, and suboptimal data management are evident, necessitating the need for stronger machine and human-readable regulations. Conclusion. Although AI strengthens security in an age of cyber-insecurity, its shortcomings point to the need for further development. Post-quantitative encryption palliatives and integration models will be effectively handled as cybersecurity-harming threats evolve.

Effective Data Preprocessing in Data Science: From Method Selection to Domain-Specific Optimization Shahidi, Shahwali; Wahid Samadzai, Abdul; Shahbazi, Hafizullah
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 4 (2025): Journal of Advanced Computer Knowledge and Algorithms - October 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i4.22886

In the era of big data and artificial intelligence, data preprocessing has emerged as a critical step in the data science pipeline, influencing the quality, performance, and reliability of machine learning models. Despite its importance, the diversity of techniques, challenges, and evolving practices necessitate a structured understanding of this domain. This study conducts a systematic literature review (SLR) to explore current data preprocessing techniques, their domain-specific applications, associated challenges, and emerging trends. A total of 21 peer-reviewed articles from 2016 to 2024 were analyzed using well-defined inclusion and exclusion criteria, with a focus on machine learning and big data contexts. The results reveal that normalization, data cleaning, feature selection, and dimensionality reduction are the most commonly applied techniques. Key challenges identified include handling missing values, high dimensionality, and imbalanced data. Moreover, recent trends such as automated preprocessing (AutoML), privacy-preserving methods, and scalable preprocessing for distributed systems are gaining momentum. The review concludes that while traditional methods remain foundational, there is a shift toward adaptive and intelligent preprocessing strategies to meet the growing complexity of data environments. This study offers valuable insights for researchers and practitioners aiming to optimize data preparation processes in modern data science workflows

Challenges and Opportunities of Implementing Augmented Reality (AR) and Virtual Reality (VR) in Public Universities of Afghanistan Shahidi, Shahwali; Ali Frugh, Qurban; Kror Shahidzay, Amir
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 4 (2025): Journal of Advanced Computer Knowledge and Algorithms - October 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i4.22962

This study explores the challenges and opportunities related to the implementation of Augmented Reality (AR) and Virtual Reality (VR) technologies in public universities in Afghanistan, with a focus on Kabul University. The research aims to assess the levels of awareness, perceived usefulness, readiness to adopt, and barriers affecting AR/VR integration in higher education. Using a quantitative research design, data were collected from 392 respondents comprising students, faculty, and administrative staff through a structured questionnaire. Descriptive analysis showed moderate awareness and positive perceptions of AR/VR’s potential benefits for enhancing learning. Inferential statistics revealed significant associations between respondents’ roles and their willingness to adopt AR/VR, as well as a strong positive relationship between digital literacy and perceived usefulness. Regression analysis identified awareness, digital literacy, and institutional support as key predictors of adoption readiness. The study highlights existing infrastructural and digital literacy challenges but emphasizes the promising potential of AR/VR to transform educational experiences in Afghan universities. The findings provide valuable insights for stakeholders aiming to promote innovative educational technologies in similar contexts.

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