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All Journal Journal of ICT, Design, Engineering and Technological Science
Shams Ul Arfeen Laghari
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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Evaluating Machine Learning Models for Real-Time IoT Intrusion Detection: A Comparative Study with RTSS Analysis Ahmed Alwan; Asadullah Shah; Alwan Abdullah Abdul Rahman Alwan; Shams Ul Arfeen Laghari
Journal of ICT, Design, Engineering and Technological Science Volume 8, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-8.2.1

Abstract

With the ever-increasing sophistication and volume of cyber-attacks, there is a critical need for effective intrusion Detection Systems (IDS) to protect computer networks. Machine Learning (ML) offers powerful tools for IDS by automatically identifying patterns of malicious behavior. This research proposal aims to evaluate and compare the performance of several supervised ML algorithms for network threat detection using the CICIDS 2023 dataset. This paper focuses on widely-used classifiers—logistic regression, Support Vector Machine (SVM), Random Forest, eXtreme Gradient Boosting (XGBoost), and k-Nearest Neighbors (KNN) – applied to both binary (benign vs. attack) and multi-class (multiple attack types) classification tasks. This paper outlines a methodology for data preprocessing, model training, and performance evaluation using metrics like accuracy, precision, recall, and F1-score. By leveraging the comprehensive CICIDS 2023 intrusion dataset, which includes 33 modern attack scenarios across seven categories, this paper expects to gain insights into the relative strengths of each ML approach in detecting diverse cyber threats. The anticipated outcome is an identification of which algorithms (or combination thereof) are most promising for intrusion detection in contemporary network environments, guiding future developments of intelligent IDS. This proposal details the problem motivation, related work, planned methodology, and expected results, establishing a foundation for a thorough experimental study.
Evaluating Supervised Machine Learning Algorithms for Cybersecurity Threat Detection Using the CICIDS 2023 Dataset Ahmed Alwan; Asadullah Shah; Alwan Abdullah Abdulrahman Alwan; Shams Ul Arfeen Laghari
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 1
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.1.1

Abstract

With the increasing volume and sophistication of network threats in IoT environments, real-time intrusion detection has become essential for securing cyber-physical systems. This study investigates the use of supervised machine learning algorithms to detect network intrusions using the CICIDS 2023 dataset. Five classification models—Logistic Regression, Support Vector Machine, Random Forest, XGBoost, and k-Nearest Neighbors—were evaluated for their effectiveness in both binary and multi-class classification tasks. The study incorporates feature selection, dimensionality reduction, and a deployment-oriented performance metric called Real-Time Suitability Score (RTSS) to assess the trade-off between accuracy, inference speed, and model size. The experimental results highlight the potential of lightweight models for deployment in constrained environments and demonstrate the impact of feature importance and classification performance on real-time detection. The findings contribute to the design of efficient and explainable AI-based intrusion detection systems, and recommendations for future work include improving model interpretability and expanding evaluation to more diverse threat categories.
Co-Authors Ahmed Alwan Alwan Abdullah Abdul Rahman Alwan Alwan Abdullah Abdulrahman Alwan Asadullah Shah