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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Okokpujie, Imhade P.
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Computer Science & IT Electrical & Electronics Engineering

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Development of a Network Intrusion Detection Model using Hybridised Machine Learning Algorithms Mary, Ogundele Oluwafeyisayo; Kennedy, Okokpujie; King-David, Maha Ojimaojo; Ijeh, Adaora P.; Okokpujie, Imhade P.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 3: September 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i3.5890

Abstract

Cyber threats continue to grow in this era since the bad actors are attempting to exploit individuals, organisations, and systems. The latest development in artificial intelligence has unleashed strong agents at the fingertips of humanity. As open as it is, it has made more room for possible bad actors. Systems that can successfully counter these threat actors need to be created to rescue humanity. In this research work, RNN and Random Forest classifiers' hybridised models are combined for the development of a Network Intrusion Detection System (NIDS) based on the benchmark dataset (CICIDS 2017) The requirement for an efficient and accurate method to detect network intrusions, both known and zero-day anomalies, is the primary problem considered. This research aims to enhance the accuracy and reliability of intrusion detection systems through a hybrid modelling approach. For evaluating the performance of the proposed model, various measures like accuracy, precision, recall, F1 measure, true positive rate, and true negative rate were employed. The hybrid model showed very good results with testing accuracy of 96.08%, precision of 96.0%, and recall of 96.0%, along with an F1 measure of 96.0%. The result of the experiment indicates that the model is effective and, when implemented, can detect and classify cyberattacks in modern environments.
Implementation of a network intrusion detection system for man-in-the-middle attacks Okokpujie, Kennedy; Abdulateef-Adoga, William A.; Owivri, Oghenetega C.; Ijeh, Adaora P.; Okokpujie, Imhade P.; Awomoy, Morayo E.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp3913-3927

Abstract

Intrusion detection systems (IDS) are critical tools designed to detect and prevent unauthorized access and potential network threats. While IDS is well-established in traditional wired networks, deploying them in wireless environments presents distinct challenges, including limited computational resources and complex infrastructure configurations. Packet sniffing and man-in-the-middle (MitM) attacks also pose significant threats, potentially compromising sensitive data and disrupting communication. Traditional security measures like firewalls may not be sufficient to detect these sophisticated attacks. This paper implements a network intrusion detection system that monitors a computer network to detect Address Resolution Protocol spoofing attacks in real-time. The system comprises three host machines forming the network. Using Kali Linux, a bash script is deployed to monitor the network for signs of address resolution protocol (ARP) poisoning. An email alert system is integrated into the bash script, running in the background as a service for the network administrator. Various ARP spoofing attack scenarios are performed on the network to evaluate the efficiency of the network IDS. Results indicate that deploying IDS as a background service ensures continuous protection against ARP spoofing and poisoning. This is crucial in dynamic network environments where threats may arise unexpectedly.
Co-Authors Abdulateef-Adoga, William A. Awomoy, Morayo E. Ijeh, Adaora P. Kennedy, Okokpujie King-David, Maha Ojimaojo Mary, Ogundele Oluwafeyisayo Okokpujie, Kennedy Owivri, Oghenetega C.