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All Journal International Journal of Artificial Intelligence International Journal of Education, Science, Technology, and Engineering (IJESTE)
Isijola, Ayomitope
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Network Anomaly Detection System using Transformer Neural Networks and Clustering Techniques Isijola, Ayomitope; Asefon, Michael; Ogude, Ufuoma; Sola, Adetoro Mayowa; Adebowale, Temiloluwa; Akunekwu, Isabella
International Journal of Artificial Intelligence Vol 12 No 1: June 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01201.837

Abstract

This study proposes a hybrid approach for network anomaly detection by integrating a Transformer-based model with clustering techniques. The methodology begins with the application of K-means clustering as a preprocessing step to group similar network traffic data, thereby reducing data complexity and highlighting significant patterns. The clustered data is then fed into a Transformer model, which utilizes multi-head self-attention mechanisms to capture intricate temporal dependencies and contextual relationships within sequential data. This dual-stage approach enhances the model’s ability to differentiate between normal and anomalous behaviors in network traffic. Trained on a network security dataset, the system effectively identifies both common and rare attack types. According to the results, the suggested ensemble classifier outperformed existing deep learning models with an accuracy of over 99.5%, 98.5%, and 99.9% on the UNSW-NB15 dataset. The synergy between the unsupervised pattern recognition of clustering and the deep learning capabilities of Transformers enables a scalable and adaptable solution for real-world network security applications, making it suitable for proactive cyber threat detection and mitigation.
Design and Evaluation of a Fuzzy Logic Based Intrusion Detection System for Network Security Isijola, Ayomitope; Afuadajo, Emmanuel; Asefon, Michael; Ogude, Ufuoma; Akande, Jamiu; Joseph, Promise
International Journal of Artificial Intelligence Vol 12 No 2: December 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01202.870

Abstract

With the proliferation of networked systems, intrusion detection systems (IDS) have become vital in identifying and mitigating cyber threats and unauthorized access. Traditional IDS approaches, such as signature-based and anomaly-based methods, often struggle to detect novel attacks and tend to generate high false alarm rates. This study presents a robust, fuzzy logic-based IDS designed to detect network intrusions and assess their risk levels while minimizing false positives. The IDS classifies network intrusions by analyzing parameters such as source bytes, destination bytes, and packet rates, categorizing them into risk levels through defined fuzzy rules. Implemented in Python using libraries like scikit-fuzzy and pandas, the system utilizes the KDD Cup 99 dataset, a widely recognized IDS benchmark. Fuzzy membership functions and inference rules were defined for the primary input variables, enabling the system to infer intrusion likelihood. The IDS was tested using both two-variable and multi-variable input setups. It achieved a precision of 0.89, a recall of 0.85, and an F1-score of 0.87 in the multi-variable scenario. Results indicate that the fuzzy logic-based IDS achieves a balanced trade-off between detection accuracy and interpretability. It offers a transparent decision-making framework suitable for real-time applications due to its adaptability and potential for integration with live data streams. This research proposes future improvements by creating a foundation for hybrid intrusion detection systems (IDS) that integrate fuzzy logic and machine learning to enhance accuracy and interpretability. It recommends future research on adaptive fuzzy rules, real-time data processing, and explainable AI (XAI) to improve system flexibility, responsiveness, and transparency in cybersecurity applications.
An Exploratory Data Analysis Approach for Tax Revenue Systems Isijola, Ayomitope; Adesioye, Eriitunu; Egwu, Mirabel; Asefon, Michael; Ojo, Abiola; Okafor, Chikwado; Adekoya, Azizat; Okoh, Samuel
International Journal of Education, Science, Technology, and Engineering (IJESTE) Vol 8 No 2: December 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijeste-0802.887

Abstract

Tax collation is an important part of any company's revenue system. Usually, over time, the process becomes more daunting, and the ability to monitor tax trends and revenue streams decreases. Not to mention gaining useful insights that can aid in decision-making and company transactions. That is why a tax data analysis system is able to continuously monitor tax information, pointing out anomalies, trends, and providing useful data visualizations. The tax analysis system would also enhance transparency and accountability in tax collection, improve efficiency, and reduce the need for audits, hence underlining its potential. Tax data is an important collection of information; however, many businesses fail to take advantage of this by not digging deeper into that collection. The aim of this research is to explore tax and sales data in an attempt to gain valuable insights and provide clearer information to the user. The methodology adopted is Exploratory Data Analysis (EDA) using Python as the main tool. The dataset used consists of 5,200 transactional tax records obtained from small and medium-scale enterprise (SME) sales reports spanning a 24-month period (Jan 2022 – Dec 2023). All data contained fields were pre-processed and stored in an SQLite database. Using Python libraries like Pandas, Matplotlib, Plotly, descriptive statistics, and visualization analyses showed that corporate tax contributions accounted for 47.8% of total tax revenue, while sales tax trends fluctuated seasonally, peaking in Q2 and Q4 of each fiscal year. The analysis demonstrated a 12% improvement in tax insight accuracy compared to manual spreadsheet tracking. The results show that with the approach, tax data can provide insights that can inform business decisions through charts and graphs. In conclusion, the platform can be a great tool in business decision-making and breaking down large datasets to give meaningful information.
Co-Authors Adebowale, Temiloluwa Adekoya, Azizat Adesioye, Eriitunu Afuadajo, Emmanuel Akande, Jamiu Akunekwu, Isabella Asefon, Michael Egwu, Mirabel Joseph, Promise Ogude, Ufuoma Ojo, Abiola Okafor, Chikwado Okoh, Samuel Sola, Adetoro Mayowa