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InfoTech Spectrum: Iraqi Journal of Data Science

· eISSN 3007-5467 · DOI 10.51173/ijds
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Research article Open Access EN pp. 21–31 · 30 Jun 2025

An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction

  1. Department of Computer Engineering, Altinbas University, Istanbul, Turkey
✱ Corresponding author: [email protected]
DOI 10.51173/ijds.v2i2.67 IJDS-TRK-2026-67 2 views 0 downloads

Abstract

Intrusion Detection Systems (IDS) are elementary building blocks of network security that can be used to detect unauthorized access and malicious activity. But traditional IDS approaches often suffer from problems such as high false positives, inability to adapt quickly to new threats, and scalability. This paper presents an advanced intrusion detection model that uses machine learning algorithms like Random Forest, Support Vector Machine (SVM), and Neural Networks to enhance detection. Using the KDD Cup 1999 data, the framework was highly preprocessed, feature engineered, and hyperparameters adjusted to achieve optimal performance. The Neural Network model outperformed other algorithms at 92.5% accuracy, 93.8% recall, and 92.4% F1-score, proving its ability to identify complex attack patterns with minimal false positives effectively. Additionally, the proposed framework reflected significant improvement over existing IDS solutions that always achieve accuracies of 80–85%. Intrusion Detection Systems (IDS) are important components of security, assuming the task of monitoring, detecting, and responding to unauthorized activities in network frameworks. This work's most notable contributions are its integration of sophisticated machine learning methods, systematic assessment of detection performance on a wide range of attack types, and comparison with well-established IDS benchmarks. In spite of facing issues like the complexity of the dataset and computational requirements, findings point to the efficacy of machine learning-based IDS in countering modern-day cybersecurity threats. Real-time data fusion and improving model interpretability for real-world implementation are areas that need to be addressed in the future.
Keywords
Computer science Intrusion detection system Machine learning False positive paradox Support vector machine Artificial intelligence Interpretability Random forest Scalability Artificial neural network Network security Data mining Algorithm Computer security Database

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How to cite

Hussein Alrammahi and Mohammed Thakir Mahmood. 2025. An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction. InfoTech Spectrum: Iraqi Journal of Data Science 2, 2 (2025), 21–31. https://doi.org/10.51173/ijds.v2i2.67

Alrammahi, H. ; Mahmood, M. T.. An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction. InfoTech Spectrum: Iraqi Journal of Data Science 2025, 2 (2), 21–31. https://doi.org/10.51173/ijds.v2i2.67

Alrammahi, H. & Mahmood, M. T. (2025). An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction. InfoTech Spectrum: Iraqi Journal of Data Science, 2(2), 21–31. https://doi.org/10.51173/ijds.v2i2.67

ALRAMMAHI, Hussein ; MAHMOOD, Mohammed Thakir. An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction. InfoTech Spectrum: Iraqi Journal of Data Science, v. 2, n. 2, p. 21–31, 2025. https://doi.org/10.51173/ijds.v2i2.67

Alrammahi, Hussein and Mahmood, Mohammed Thakir. 2025. "An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction." InfoTech Spectrum: Iraqi Journal of Data Science 2 (2): 21–31. https://doi.org/10.51173/ijds.v2i2.67

Alrammahi, H. and Mahmood, M. T. (2025) 'An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction', InfoTech Spectrum: Iraqi Journal of Data Science, 2(2), pp. 21–31. https://doi.org/10.51173/ijds.v2i2.67

H. Alrammahi and M. T. Mahmood, "An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction," InfoTech Spectrum: Iraqi Journal of Data Science, vol. 2, no. 2, pp. 21–31, 2025. doi: https://doi.org/10.51173/ijds.v2i2.67.

Alrammahi, Hussein and Mahmood, Mohammed Thakir. "An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction." InfoTech Spectrum: Iraqi Journal of Data Science, vol. 2, no. 2, 2025, pp. 21–31. https://doi.org/10.51173/ijds.v2i2.67

Hussein Alrammahi and Mohammed Thakir Mahmood. "An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction." InfoTech Spectrum: Iraqi Journal of Data Science 2, no. 2 (2025): 21–31. https://doi.org/10.51173/ijds.v2i2.67

Alrammahi H , Mahmood MT. An Advanced Framework for Intrusion Detection in Network Security Utilizing Machine Learning Algorithms: Challenges, Solutions, and Future Direction. InfoTech Spectrum: Iraqi Journal of Data Science. 2025;2(2):21–31. https://doi.org/10.51173/ijds.v2i2.67

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