Machine Learning Techniques for Enhanced Intrusion Detection in IoT Security
Network Intrusion Detection Systems (NIDSs) are fundamental to safeguarding computer networks. Intrusion detection systems must become more effective as new attacks are developed and networks grow. Anomaly-based automated detection stands out due to its superior performance among the various detecti...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10887215/ |
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| author | Hanadi Hakami Muhammad Faheem Majid Bashir Ahmad |
| author_facet | Hanadi Hakami Muhammad Faheem Majid Bashir Ahmad |
| author_sort | Hanadi Hakami |
| collection | DOAJ |
| description | Network Intrusion Detection Systems (NIDSs) are fundamental to safeguarding computer networks. Intrusion detection systems must become more effective as new attacks are developed and networks grow. Anomaly-based automated detection stands out due to its superior performance among the various detection techniques. However, with the increasing complexity and frequency of cyberattacks, managing vast amounts of data remains challenging for anomaly-based NIDS. Therefore, it is necessary to find an efficient method for solving the problem by using classification with an intrusion detection system which analyzes enormous amounts of traffic data. This research introduces a new model that leverages machine learning (ML) and deep learning (DL) to enhance detection effectiveness and ensure reliability. The approach optimizes data preprocessing by integrating SMOTE for effective data balancing and Pearson’s Correlation Coefficient (PCC) for feature selection. We compared several ML and DL techniques to detect and address the most efficient one for our pipeline. Compared with other approaches, LSTM and RF show superior results when tested on the WSN-DS, UNSW-NB15, and CIC-IDS 2017 datasets. Additionally, the proposed solution prevents biases from arising by addressing imbalanced datasets. |
| format | Article |
| id | doaj-art-ed95f93aaca64005b7712b66b3f8ff6d |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ed95f93aaca64005b7712b66b3f8ff6d2025-08-20T03:11:55ZengIEEEIEEE Access2169-35362025-01-0113311403115810.1109/ACCESS.2025.354222710887215Machine Learning Techniques for Enhanced Intrusion Detection in IoT SecurityHanadi Hakami0https://orcid.org/0000-0001-5627-6805Muhammad Faheem1https://orcid.org/0009-0000-2274-6821Majid Bashir Ahmad2https://orcid.org/0009-0009-0760-3529Department of Software Engineering, College of Engineering, University of Business and Technology, Jeddah, Saudi ArabiaSchool of Technology and Innovations, University of Vaasa, Vaasa, FinlandDepartment of Computer Sciences, COMSATS University Islamabad, Vehari, PakistanNetwork Intrusion Detection Systems (NIDSs) are fundamental to safeguarding computer networks. Intrusion detection systems must become more effective as new attacks are developed and networks grow. Anomaly-based automated detection stands out due to its superior performance among the various detection techniques. However, with the increasing complexity and frequency of cyberattacks, managing vast amounts of data remains challenging for anomaly-based NIDS. Therefore, it is necessary to find an efficient method for solving the problem by using classification with an intrusion detection system which analyzes enormous amounts of traffic data. This research introduces a new model that leverages machine learning (ML) and deep learning (DL) to enhance detection effectiveness and ensure reliability. The approach optimizes data preprocessing by integrating SMOTE for effective data balancing and Pearson’s Correlation Coefficient (PCC) for feature selection. We compared several ML and DL techniques to detect and address the most efficient one for our pipeline. Compared with other approaches, LSTM and RF show superior results when tested on the WSN-DS, UNSW-NB15, and CIC-IDS 2017 datasets. Additionally, the proposed solution prevents biases from arising by addressing imbalanced datasets.https://ieeexplore.ieee.org/document/10887215/Intrusion detectionIoTclassificationmachine/deep learningrandom forestslong-short-term-memory |
| spellingShingle | Hanadi Hakami Muhammad Faheem Majid Bashir Ahmad Machine Learning Techniques for Enhanced Intrusion Detection in IoT Security IEEE Access Intrusion detection IoT classification machine/deep learning random forests long-short-term-memory |
| title | Machine Learning Techniques for Enhanced Intrusion Detection in IoT Security |
| title_full | Machine Learning Techniques for Enhanced Intrusion Detection in IoT Security |
| title_fullStr | Machine Learning Techniques for Enhanced Intrusion Detection in IoT Security |
| title_full_unstemmed | Machine Learning Techniques for Enhanced Intrusion Detection in IoT Security |
| title_short | Machine Learning Techniques for Enhanced Intrusion Detection in IoT Security |
| title_sort | machine learning techniques for enhanced intrusion detection in iot security |
| topic | Intrusion detection IoT classification machine/deep learning random forests long-short-term-memory |
| url | https://ieeexplore.ieee.org/document/10887215/ |
| work_keys_str_mv | AT hanadihakami machinelearningtechniquesforenhancedintrusiondetectioniniotsecurity AT muhammadfaheem machinelearningtechniquesforenhancedintrusiondetectioniniotsecurity AT majidbashirahmad machinelearningtechniquesforenhancedintrusiondetectioniniotsecurity |