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p-Index From 2021 - 2026
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All Journal Scientific Journal of Computer Science Kwaghe International Journal of Engineering and Information Technology
Ishaq, Ashraf
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering

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Breaking Class Imbalance Barriers in Intrusion Detection Systems: A Clustering-Based Hybrid Framework Hambali, Moshood Abiola; Bako, Nahum Zhema; Dalhatu, Mu’awuya; Ishaq, Ashraf
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.378

Abstract

Intrusion Detection Systems (IDS) deal with issues concerning the ever-escalating level of sophistication observed within cyber threats. Nonetheless, IDS performance is deteriorated by class imbalance and excessively high-dimensional features, which cause biased classifier training towards major traffic patterns. Thus, this research introduces an innovative hybrid clustering IDS approach that utilizes MiniBatchKMeans clustering and ensemble machine learning strategies to mitigate these challenges. The suggested IDS approach utilizes the Synthetic Minority Over-sampling Technique for addressing class imbalance problems, Fast Correlation-Based Filter for reducing high-dimensional features, and Hyperopt Tree-structured Parzen Estimator for optimizing clustering and machine classifiers' parameters. Four supervised machine classifiers — Decision Tree classifier, Random Forest classifier, Extra Trees classifier, and XGBoost classifier— were trained and validated on the NSL-KDD IDS dataset. Additionally, experimental analysis indicated a superior detection accuracy for all classifiers, for which the best-optimized XGBoost classifier and best-optimized Random Forest classifier provided 99.57% and 99.51% accuracy, respectively. The proposed clustering-optimized machine IDS approach provided substantial improvements for identifying minority class attacks, along with sustainability and high generalization capabilities. The obtained outcomes support the research premise concerning the efficacy of cluster-aware sampling and ensemble optimizations for designing more balanced, accurate, and adaptive IDS systems for effectively protecting against ever-escalating real-life threats within the cyberworld.
Tiktok Through AI Eyes: A Deep Learning Approach to Sentiment Analysis Abiola, Hambali Moshood; Iyanuoluwa, Ayo; A., Akinyemi Adesina; Gadafi, Adamu Muhammed; Ishaq, Ashraf
Kwaghe International Journal of Engineering and Information Technology Vol 2 No 2 (2025): Kwaghe International Journal of Engineering and Information Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/kijeit.v2i2.5485

Abstract

Background: The rapid growth of social media has transformed communication, with TikTok standing out among younger users for its short-form videos. Understanding user sentiment on these platforms is key to analyzing public opinion, trends, and engagement. Aim: This study explores sentiment analysis of TikTok user reviews using deep learning approaches, specifically Recurrent Neural Networks with Long Short-Term Memory (RNN-LSTM) and Deep Belief Networks (DBN). With over 144,000 reviews collected from Google Play and Apple App stores, the dataset was preprocessed using techniques such as lemmatization, tokenization, and GloVe word embeddings. The reviews were then classified into positive and negative sentiments. Both models were trained and evaluated based on metrics including accuracy, precision, recall, F1-score, and ROC-AUC. Result: Experimental results revealed that the RNN-LSTM model outperformed the DBN, achieving an accuracy of 81.99% and an AUC of 0.8874, compared to DBN's 78.53% accuracy and 0.8577 AUC. The findings demonstrate the effectiveness of deep learning—particularly LSTM—in capturing sentiment from noisy, user-generated content on platforms like TikTok. This work contributes to the growing field of AI-driven sentiment analysis and provides a foundation for future improvements through hybrid or multimodal approaches.
The Role of Blockchain in Securing IoT Devices Jibrin, Abubakar; Ishaq, Ashraf; Ahmed, Aliyu; Gadafi, Adamu Muhammad
Kwaghe International Journal of Engineering and Information Technology Vol 2 No 2 (2025): Kwaghe International Journal of Engineering and Information Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/kijeit.v2i2.5584

Abstract

The proliferation of Internet of Things (IoT) devices has introduced unprecedented security challenges, including data breaches, unauthorized access, and the exploitation of centralized network vulnerabilities. Traditional security architectures struggle to provide robust protection due to the distributed and resource-constrained nature of IoT environments. Blockchain technology, with its decentralized ledger, cryptographic security, and smart contract functionality, presents a promising approach to mitigating these risks. By ensuring data integrity, enabling secure authentication, and facilitating trustless interactions among IoT devices, blockchain can enhance the overall security framework of IoT ecosystems. This paper critically examines the role of blockchain in securing IoT networks, outlining its key benefits, potential real-world applications, and associated limitations. While blockchain addresses fundamental IoT security concerns, challenges such as scalability, computational overhead, and integration complexity hinder widespread adoption. The study underscores the need for further research into optimizing blockchain protocols for IoT environments and explores potential advancements in hybrid security models.
Co-Authors A., Akinyemi Adesina Abiola, Hambali Moshood Ahmed, Aliyu Bako, Nahum Zhema Dalhatu, Mu’awuya Gadafi, Adamu Muhammad Gadafi, Adamu Muhammed Hambali, Moshood Abiola Iyanuoluwa, Ayo Jibrin, Abubakar