Detecting Network Traffic Anomalies with Machine Learning: A Comprehensive Approach

Abstract

Continued growth in network traffic as well as increased complexity in network architecture make anomaly detection critical to maintaining the security and reliability of networks. This abstract provides a detailed review of utilizing machine learning techniques in anomaly detection for network traffic. Machine learning methodologies have thus far proven promising approaches in identifying anomalies of network traffic since they can detect pattern-specific characteristics in large datasets. Different approaches to machine learning; that this abstract discusses, both supervised and unsupervised learning techniques deployed in anomaly detection. The supervised learning methods are basically those in which classifiers are trained on labeled data to classify data into normal versus anomalies in terms of traffic patterns. Instead, this technique employs unsupervised learning that finds the presence of anomalies without pre-classified data. A copy of the similar instances would be made and outliers flagged out. Another significant area that this abstract talks about is the difficulties that arise in anomaly detection in network traffic. These include the imbalanced nature of network data, evolving attack strategies, and the requirement for real-time detection. It aims to develop robust models that can identify different types of anomalies which may arise with cyberattacks like DDoS, port scanning, or even simple data exfiltration. Using LOF, SVM, KNN.

Introduction

With over 4 billion internet users and growing, cyberattacks have become increasingly common. Two main methods for detecting these attacks are:

• Signature-based detection: Effective for known threats but fails against new (zero-day) attacks and encrypted traffic.

• Anomaly-based detection: Detects unusual behavior and is effective even against encrypted and zero-day attacks.

This study focuses on anomaly detection using machine learning (ML) to identify unusual patterns in network traffic.

???? Goals & Objectives

• Goals: Explore ML algorithms for network anomaly detection, assess their performance, and compare with existing methods.

• Objectives: Review literature, choose appropriate datasets, algorithms, platforms, and benchmarking criteria for evaluation.

???? Key Concepts

• Anomaly Detection: Identifies deviations from normal network behavior that may indicate cyber threats.

• Types of Anomalies:

  • Point anomaly: Unusual single data point.

  • Contextual anomaly: Unusual under specific context.

  • Collective anomaly: Abnormal group behavior.

• Common Network Attacks: DoS, DDoS, data exfiltration, probing, U2R, R2L, etc.

???? Existing Methods

• Traditional methods include:

  • Rule-based, Statistical, and Signature-based detection.

  • Machine learning models like Naive Bayes (86%), Random Forest (94%), AdaBoost (94%), MLP (83%).

???? Literature Review Highlights

• Studies emphasize supervised, unsupervised, and deep learning (CNN, RNN, Autoencoders).

• PCA, clustering, and hybrid approaches show promise in high-dimensional data environments.

???? Proposed Methodology

• Apply various ML models on network datasets:

  • K-Means (99.61%)

  • Isolation Forest (99%)

  • One-Class SVM (94%)

  • LOF (94%)

  • Scikit-learn’s Nearest Neighbors (94%)

  • PyOD KNN (95%)

• Focus on unsupervised/semi-supervised learning due to lack of labeled data.

????? Dataset Description

• Contains 42 features including connection time, protocol type, service type, flag, source/destination bytes, etc.

• Includes normal and anomaly labels.

• Common datasets: UNSW-NB15, KDD Cup 1999, CICIDS2017.

?? Project Workflow

1. Data Collection: From logs, flows, or packet captures.

2. Preprocessing: Cleaning, scaling, encoding, feature selection, dimensionality reduction, handling imbalance, time-series formatting.

3. EDA: Explore distributions and patterns.

4. Model Selection: Supervised, unsupervised, or semi-supervised models.

5. Training & Validation: Use cross-validation, tune hyperparameters, and avoid overfitting.

6. Testing: Evaluate using metrics like accuracy, precision, recall, F1, ROC-AUC.

7. Deployment & Monitoring: Real-time testing, retraining with new data, feedback loops.

Conclusion

Machine learning-based anomaly detection in network traffic has been of great use in identifying possible security threats as well as performance issues and network failures. By using supervised, unsupervised, or semi-supervised learning, machine learning algorithms can thus identify anomalies in usual network traffic for possible malicious activities like a Distributed Denial of Service attack, intrusion attempts, or unusual user behavior. The execution of these models can monitor in real-time with increased precision and is capable of processing large-scale network data efficiently. As more advanced algorithms and deep learning techniques have been released into the market, it is expected that the ability to detect known and unknown, or zero-day threats, becomes more robust. But there are challenges in the interpretability of the models, handling evolving streams of data, and high false-positive rates that have to be removed

References

[1] Chandola, V., Banerjee, A., & Kumar, V. (2009). \"Anomaly detection: A survey.\" ACM Computing Surveys (CSUR), 41(3), 1-58. [2] Sommer, P., & Paxson, V. \"Outside the Closed World: On Using Machine Learning For Network Intrusion Detection.\" 2010 IEEE European Symposium on Security and Privacy. [3] Ahamed, M., Mahmood, A. N., & Hu, J. (2016). \"A survey of network anomaly detection techniques.\" Journal of Network and Computer Applications, 60, 19-31. [4] Tavallaee, M., Bagheri, A., Sharif, B., & Hamdi, M. (2009). \"A detailed analysis of the KDD CUP 99 data set.\" 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications. [5] Yin, C., Zhang, Z., & Yang, Y. (2018). \"Deep learning for network anomaly detection: A review.\" IEEE Access, 6, 65625-65638. [6] Liu, Y., Wu, J., & Liu, W. (2018). \"A novel hybrid method for network anomaly detection using convolutional neural networks.\" IEEE Transactions on Neural Networks and Learning Systems. [7] Khan, M. A., & Alzubaidi, J. (2021). \"Machine Learning Approaches for Network Intrusion Detection: A Survey.\" IEEE Access, 9, 151572-151596. [8] Hodge, V. J., & Austin, J. (2004). \"A survey of outlier detection methodologies.\" Artificial Intelligence Review, 22(2), 85-126. [9] Buczak, A. L., & Guven, E. (2016). \"A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection.\" IEEE Communications Surveys & Tutorials, 18(2), 1138-1159. [10] Tavallaee, M., et al. \"Toward the Evaluation of Intrusion Detection Systems.\" 2009 1st International Conference on Computer and Electrical Engineering. [11] Zhou, W., et al. \"Anomaly Detection in Network Traffic Based on Convolutional Neural Network.\" IEEE Access, 8, 175246-175259. [12] Makhdoom, I., et al. 2020. \"Network Anomaly Detection Using Deep Learning: A Review.\" IEEE Access 8, pp. 148223-148238. [13] Rashid, M. T., et al. 2021. \"An Enhanced Machine Learning Approach for Network Intrusion Detection.\" International Journal of Computer Applications, 975, 8887. [14] Sahu, S. K., et al. \"A Review of Machine Learning Approaches for Network Traffic Analysis and Anomaly Detection.\" Journal of King Saud University - Computer and Information Sciences. [15] Gonzalez, S., et al. \"Exploring Deep Learning Techniques for Anomaly Detection in Network Traffic.\" Journal of Network and Computer Applications, 217, 103331.

Copyright

Copyright © 2025 N. Vinisha, Sri Gopala Krishna. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.