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All Journal Journal of Information Systems and Informatics Journal of Innovation Information Technology and Application (JINITA)
Saviour Inyang
Akwa Ibom State University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Securing Against Zero-Day Attacks: A Machine Learning Approach for Classification and Organizations’ Perception of its Impact Anietie P. Ekong; Aniebiet Etuk; Saviour Inyang; Mary Ekere-obong
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.546

Abstract

Zero-day malware is a type of malware that exploits system vulnerabilities before it is detected and sealed. This type of malware is a significant threat to enterprise cybersecurity and has tremendous impact on organizations’ performance, as it can spread widely before organizations can clamp down on the threat. Unfortunately, exploit developers can attack system’s vulnerabilities at a pace that is faster than defensive patches. In this research, classification of zero-day attack was carried out. Exploratory Data Analysis (EDA) on malware zero data was conducted. Then feature selection was carried out using Principal Component Analysis (PCA) for the selection of the most important features in the dataset after which a Random Forest (RF) Algorithm was adopted for the classification of zero-day attack. The impact of such attacks was also analyzed, and results were evaluated using confusion matrix and an accuracy of 95% in the classification of zero-day attack with a class error of 3.8% was obtained. A survey of the perception of the potential impacts of these attacks on organization was also carried out. These results indicate efficiency of machine learning algorithm in the classification of attacks as zero-day malware attacks or not. The research also offered pragmatic insights into the perception by organizations of its potential negative impacts and their eagerness to embrace and prioritize proffered cyber security solution(s) to avoid such attacks in order to avert undesirable consequences.
From Text to Insights: NLP-Driven Classification of Infectious Diseases Based on Ecological Risk Factors Saviour Inyang; Imeh Umoren
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.2084

Abstract

Numerous factors can affect the development of infectious diseases that emerge. While many are the result of natural procedures, such as the gradual emergence of viruses over time, certain ones are the result of human activity. Human activities form an integral part of our ecosystem, and especially the ecological aspect of human activities can encourage disease transmission. Additionally, Health ecologists examine changes in the biological, physical, social, and economic settings to understand how these alterations impact the mental and physical well-being of individuals. Hence, this research adopts a Framework-Based Method (FBM) in carrying out the task of classification of infectious diseases. The Framework-Based Method outlines all phases that this research follows to carry out the infectious disease classification process, providing a structured and reproducible approach. Results show that: XGB: Confusion matrix accuracy: 76%, Kappa: 73%, RF: Confusion matrix accuracy: 65%, Kappa: 60%, SVM: Confusion matrix accuracy: 63%, Kappa: 58%, ANN: Confusion matrix accuracy: 71%, Kappa: 67%, LDA: Confusion matrix accuracy: 76%, Kappa: 73%, GBM: Confusion matrix accuracy: 60%, Kappa: 53%, KNN: Confusion matrix accuracy: 43%, Kappa: 34%, and DT: Confusion matrix accuracy: 37%, Kappa: 29%. Furthermore, a Deep Learning model BERT was integrated with the best classification model XGBoots to create an interactive interface for users to carry out infectious disease classification. This integration enhances user experience and accessibility, contributing to the practical application of machine learning and Natural language processing in ecological disease classification
Co-Authors Aniebiet Etuk Anietie P. Ekong Imeh Umoren Mary Ekere-obong