Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques
Anomaly detection in network traffic is a critical aspect of network security, particularly in defending against the increasing sophistication of cyber threats. This study investigates the application of various machine learning models for detecting anomalies in network traffic, specifically focusin...
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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/10833631/ |
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| _version_ | 1832583203990274048 |
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| author | Stephanie Ness Vishwanath Eswarakrishnan Harish Sridharan Varun Shinde Naga Venkata Prasad Janapareddy Vineet Dhanawat |
| author_facet | Stephanie Ness Vishwanath Eswarakrishnan Harish Sridharan Varun Shinde Naga Venkata Prasad Janapareddy Vineet Dhanawat |
| author_sort | Stephanie Ness |
| collection | DOAJ |
| description | Anomaly detection in network traffic is a critical aspect of network security, particularly in defending against the increasing sophistication of cyber threats. This study investigates the application of various machine learning models for detecting anomalies in network traffic, specifically focusing on their effectiveness in addressing challenges such as class imbalance and feature complexity. The models assessed include Isolation Forest, Naive Bayes, XGBoost, LightGBM, and SVM classification. Through comprehensive evaluation, this research explores both supervised and unsupervised approaches, comparing their performance across key metrics like accuracy, F1-score, and recall. The results reveal that while models like XGBoost and LightGBM exhibit impressive performance, with LightGBM achieving near-perfect training accuracy (1.0) and solid test accuracy (0.85), others like Isolation Forest show limitations with low accuracy. The study highlights the strengths and weaknesses of each model, providing valuable insights into their practical application for network anomaly detection. By comparing different algorithms, this research contributes to advancing the application of machine learning in network security, offering guidance on model selection and optimization for improved detection of cyber threats. |
| format | Article |
| id | doaj-art-07ae372380e649b18d554a7fb95df877 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-07ae372380e649b18d554a7fb95df8772025-01-29T00:01:09ZengIEEEIEEE Access2169-35362025-01-0113161331614910.1109/ACCESS.2025.352698810833631Anomaly Detection in Network Traffic Using Advanced Machine Learning TechniquesStephanie Ness0https://orcid.org/0009-0004-9654-5722Vishwanath Eswarakrishnan1https://orcid.org/0009-0008-4143-6536Harish Sridharan2Varun Shinde3Naga Venkata Prasad Janapareddy4Vineet Dhanawat5Diplomatic Academy of Vienna, University of Vienna, Vienna, AustriaMeta Platforms Inc., Menlo Park, CA, USACharter Communications, Greenwood Village, CO, USACloudera Inc., Austin, TX, USAF5 Inc., Seattle, WA, USAMeta Platforms Inc., Menlo Park, CA, USAAnomaly detection in network traffic is a critical aspect of network security, particularly in defending against the increasing sophistication of cyber threats. This study investigates the application of various machine learning models for detecting anomalies in network traffic, specifically focusing on their effectiveness in addressing challenges such as class imbalance and feature complexity. The models assessed include Isolation Forest, Naive Bayes, XGBoost, LightGBM, and SVM classification. Through comprehensive evaluation, this research explores both supervised and unsupervised approaches, comparing their performance across key metrics like accuracy, F1-score, and recall. The results reveal that while models like XGBoost and LightGBM exhibit impressive performance, with LightGBM achieving near-perfect training accuracy (1.0) and solid test accuracy (0.85), others like Isolation Forest show limitations with low accuracy. The study highlights the strengths and weaknesses of each model, providing valuable insights into their practical application for network anomaly detection. By comparing different algorithms, this research contributes to advancing the application of machine learning in network security, offering guidance on model selection and optimization for improved detection of cyber threats.https://ieeexplore.ieee.org/document/10833631/Network trafficnetwork anomaly detectionKDDCup99machine learning modelsisolation forestnaive Bayes |
| spellingShingle | Stephanie Ness Vishwanath Eswarakrishnan Harish Sridharan Varun Shinde Naga Venkata Prasad Janapareddy Vineet Dhanawat Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques IEEE Access Network traffic network anomaly detection KDDCup99 machine learning models isolation forest naive Bayes |
| title | Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques |
| title_full | Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques |
| title_fullStr | Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques |
| title_full_unstemmed | Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques |
| title_short | Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques |
| title_sort | anomaly detection in network traffic using advanced machine learning techniques |
| topic | Network traffic network anomaly detection KDDCup99 machine learning models isolation forest naive Bayes |
| url | https://ieeexplore.ieee.org/document/10833631/ |
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