Analysis of Machine Learning-Based Methods for Network Traffic

Anomaly Detection and Prediction

Jingyao Wang

Santa Monica College, California, 90401, U.S.A.

Keywords: Machine Learning, Network Traffic Analysis, Network Traffic Prediction, Deep Learning, Anomaly

Detection.

Abstract: In the era of rapid development of network technology, the volume of network data traffic has grown

exponentially. Network traffic analysis and prediction can effectively facilitate network management, enable

timely detection of network attacks, and enhance security protection and optimization of internet resources.

This paper introduces the current application of machine learning in network traffic anomaly detection and

prediction, along with key technologies such as data preprocessing, feature engineering, model evaluation,

and optimization. It describes the technological advancements in traditional machine learning and deep

learning methods for traffic classification, anomaly detection, and traffic prediction. The paper highlights the

challenges faced by machine learning in network traffic analysis and prediction, including data complexity,

real-time processing, and privacy protection. To address these challenges, machine learning in network traffic

analysis and detection will rely on interdisciplinary collaboration and technological innovation to develop

more automated, intelligent models that emphasize privacy protection, model interpretability, and real-time

processing capabilities.

1 INTRODUCTION

With the widespread adoption and application of

networks, the reception, storage, and processing of

Internet data have experienced exponential growth. In

this context, network data traffic has expanded

rapidly and exhibits certain characteristics. Network

traffic not only reflects user behavior and activities

but also indicates the state of the network. Accurate

detection and predictive modeling of network traffic

can help collect user behavior data on time, optimize

user experience, and enhance network performance.

Detecting abnormal traffic signals can safeguard

network security and identify potential network

attacks. Therefore, finding an efficient and accurate

method for network traffic detection and analysis is

essential.

Traditional non-machine learning methods for

network traffic detection, such as NetFlow

technology, lack predictive capabilities and require

further data processing and analysis (Bai, 2024). In an

era of explosive growth in network traffic and

increasing concerns about user privacy, there is an

https://orcid.org/0009-0000-0176-8845

urgent need to develop new, more secure, and

efficient methods for network traffic detection and

analysis.

Unlike traditional traffic analysis techniques like

NetFlow, machine learning methods can

automatically learn the characteristics and patterns of

network traffic. By building models, machine

learning enables efficient traffic classification,

anomaly detection, and even data analysis and traffic

prediction. This approach reduces the need for

extensive data transmission, improves efficiency, and

enhances network performance and resource

utilization. Moreover, machine learning can process

data locally or in a distributed architecture,

eliminating the need to upload data to central servers

and avoiding centralized data storage, thereby

enhancing network security.

This paper systematically reviews the application

of machine learning in network traffic anomaly

detection and prediction, focusing on the following

aspects: basic characteristics of network traffic, an

overview of machine learning methods, specific

methods for traffic anomaly detection, data

550

Wang, J.

Analysis of Machine Learning-Based Methods for Network Trafﬁc Anomaly Detection and Prediction.

DOI: 10.5220/0013701800004670

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Data Science and Engineering (ICDSE 2025), pages 550-554

ISBN: 978-989-758-765-8

preprocessing and feature engineering, model

evaluation and optimization, and future development

directions.

2 OVERVIEW OF MACHINE

LEARNING METHODS

2.1 Basic Concepts of Network Traffic

Analysis and Prediction

Network traffic exhibits characteristics such as

autocorrelation, burstiness, non-stationarity, and

periodicity. Autocorrelation refers to the temporal

correlation of network traffic over time. Burstiness

indicates that network traffic can increase sharply

within a short period. Non-stationarity means that the

statistical properties of network traffic change over

time. Network traffic analysis primarily involves

categorizing traffic into different types, such as video

streaming, web browsing, and P2P downloads, to

analyze user behavior patterns and create user profiles

for behavior prediction. Network traffic prediction

faces challenges such as data dynamism, complexity,

and uncertainty. Traffic patterns change over time,

requiring models to adapt dynamically. Additionally,

network traffic is influenced by various factors,

including user behavior, network topology, and

application types. Noise and outliers in network

traffic further complicate prediction.

2.2 Traditional Machine Learning

Methods

Traditional machine learning methods include

supervised learning, unsupervised learning, and semi-

supervised learning. Supervised learning, which uses

labeled data to learn the mapping between inputs and

outputs, is the primary approach for network traffic

analysis and prediction. Common supervised learning

methods include decision trees, which classify or

regress by constructing tree-like models; support

vector machines (SVM), which classify by finding

optimal hyperplanes; and naive Bayes classifiers,

which use Bayesian probability for classification.

These methods, combined with algorithm

optimization, yield efficient and accurate results in

network traffic analysis and detection.

2.3 Deep Learning Methods

Deep learning methods include neural networks (NN)

and their derivatives, such as convolutional neural

networks (CNN), recurrent neural networks (RNN),

and their variants (e.g., LSTM and GRU), as well as

the Transformer architecture. NN achieve complex

function mapping through the combination of

multiple layers of neurons. CNNs are suitable for

feature extraction in image and sequence data, while

RNNs and their variants (e.g., LSTM and GRU) are

better suited for time-series data. The Transformer

architecture, based on self-attention mechanisms,

captures long-range dependencies.

2.4 Federated Learning and

Distributed Learning

Federated learning is a distributed machine learning

method that enables model training across multiple

devices or institutions while preserving data privacy.

It offers significant advantages in processing

distributed network traffic data.

3 MACHINE LEARNING-BASED

NETWORK TRAFFIC

ANALYSIS METHODS

3.1 Traffic Classification

Traffic classification is a fundamental task in network

traffic analysis. Machine learning-based methods

include feature engineering-based classification,

which extracts statistical features (e.g., packet size,

transmission rate) and combines them with traditional

machine learning algorithms (e.g., SVM, decision

trees) for classification. Deep learning methods such

as CNN and RNN excel in encrypted traffic

classification. For example, CNNs extract local

features, while RNNs capture dynamic temporal

characteristics (Cui, 2024).

3.2 Anomaly Detection

Anomaly detection identifies abnormal network

traffic for further analysis. Statistical metrics (e.g.,

mean, variance) can be used to detect anomalies, or

different models' statistical metrics can be compared

to determine accuracy. For instance, Wang Ruixue

compared the accuracy of the GAFSA-SVR model

with CPSO-SVR and GA-SVR models using mean

relative error and root mean square error (Wang,

2013). Algorithms like random forests and

autoencoders are also used for anomaly detection.

Random forests handle high-dimensional data, while

Analysis of Machine Learning-Based Methods for Network Trafﬁc Anomaly Detection and Prediction

551

autoencoders detect anomalies through

reconstruction errors.

3.3 Behavior Analysis

Behavior analysis models user network behavior

patterns for user profiling and behavior prediction.

Deep learning methods like LSTM capture temporal

characteristics of user behavior, enabling precise

behavior analysis.

4 MACHINE LEARNING-BASED

NETWORK TRAFFIC

PREDICTION METHODS

Time series prediction is a primary method for

network traffic prediction. The ARIMA model is a

classic statistical time series model suitable for

stationary time series data. LSTM and GRU, based on

RNNs, capture long- and short-term dependencies in

time series data.

Deep learning models have made significant

progress in traffic prediction. For example, the

Transformer architecture captures long-range

dependencies through self-attention mechanisms,

significantly improving prediction accuracy (Ji,

2024).

Distributed and federated learning methods are

crucial in network traffic prediction. Federated

learning stores models on client devices and

exchanges model parameters with servers at specific

times, optimizing models without uploading user

data. This approach enhances security in network

anomaly detection compared to traditional methods,

enabling joint traffic detection across multiple base

stations while preserving data privacy.

5 APPLICATIONS AND IMPACT

OF MACHINE LEARNING IN

NETWORK TRAFFIC

ANALYSIS AND PREDICTION

This section discusses three application areas:

network traffic classification and anomaly detection,

network traffic prediction and resource optimization,

and network security situational awareness and early

warning.

5.1 Network Traffic Classification and

Anomaly Detection

Machine learning plays a vital role in network traffic

classification. By analyzing traffic features such as

packet size, transmission protocol, and source and

destination addresses, machine learning algorithms

classify traffic and identify different application types

or services. This classification helps network

administrators better understand network usage and

optimize resource allocation. Sun Yu proposed a

phishing attribution analysis based on deep learning

interpretability methods, focusing on visual features

like website logos, buttons, and navigation bars (Sun,

2024). By leveraging multi-modal data and self-

attention mechanisms, the model extracts more

discriminative features for phishing website

detection.

In anomaly detection, machine learning analyzes

historical traffic data to learn normal behavior

patterns. When traffic deviates from these patterns,

machine learning algorithms quickly identify

anomalies, which may indicate network attacks or

failures. For example, deep learning algorithms can

monitor network traffic, device logs, and signal

strength in real-time, accurately locating faults or

attack sources with over 98% accuracy. Liu Jingrui

proposed a deep clustering model, Cluster-AAE,

which learns low-dimensional representations of

network traffic data and uses the SNNDC algorithm

for clustering analysis to establish behavior rule

libraries(Liu, 2024) . Test data is then matched

against these rules to predict attack behavior.

5.2 Network Traffic Prediction and

Resource Optimization

Machine learning models can predict network traffic

fluctuations in advance. By analyzing historical data,

such as seasonal traffic trends and holiday traffic

peaks, machine learning models accurately forecast

future network traffic. This prediction helps network

operators allocate resources and bandwidth

proactively, optimizing resource utilization during

traffic peaks. Wang Yuewen developed a wireless

cellular network traffic prediction method based on

residual networks and RNNs, using attention modules

to optimize the model. The method was implemented

in a prototype system for wireless cellular network

traffic services (Wang, 2021).

Machine learning models also assist in network

resource optimization. For example:

Bandwidth Allocation Optimization: Based on

traffic predictions, network operators can adjust

bandwidth allocation to ensure sufficient resources

during peak periods, avoiding congestion and

improving user experience (Zhang, 2024).

Routing Optimization: By analyzing network

data, optimal data transmission paths can be

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determined, reducing latency and congestion and

enabling self-healing networks.

Fault Prediction and Maintenance: Machine

learning predicts network component failures by

analyzing historical and real-time data, enabling

proactive maintenance and reducing network

downtime. Ji Jingchan utilized CNNs and RNNs for

feature extraction and pattern recognition, identifying

abnormal network activities(Ji, 2024). By integrating

various machine learning techniques, including

neural networks, SVMs, random forests, decision

trees, deep learning, and ensemble learning,

significant improvements were achieved in traffic

prediction, real-time traffic classification, network

resource optimization, anomaly detection, and

security threat analysis, enhancing the overall

performance and efficiency of 5G networks.

5.3 Network Security Situational

Awareness and Early Warning

Machine learning is widely used in network security

monitoring and early warning systems. Sardar Shan

Ali Naqvi proposed a DDoS attack detection model

based on multi-level autoencoder feature learning

(Naqvi, 2024). Using unsupervised learning, the

model combines multiple shallow and deep

autoencoders with multi-kernel learning (MKL) to

detect DDoS attacks in smart grids, enabling timely

security situational awareness and early warning.

Machine learning algorithms are shifting from

passive defense to proactive prevention. By analyzing

past attack patterns and current system activities,

these algorithms can issue warnings before potential

threats materialize.

5.4 The Role of Machine Learning in

Network Traffic Analysis and

Prediction

Machine learning has driven a paradigm shift from

rule-based to data-driven approaches in network

traffic analysis and detection. Traditional methods

relied on predefined rules and feature matching,

which struggled to cope with increasingly complex

and diverse network attacks. Machine learning,

especially deep learning, enables systems to

automatically learn and extract features from vast

amounts of network traffic data, achieving precise

anomaly detection. Moreover, machine learning

models are adaptive and scalable, continuously

updating and optimizing as network environments

and attack methods evolve. This ensures effective

network security protection even against new and

unknown threats.

Machine learning enhances the accuracy and

efficiency of network traffic analysis through

automated feature extraction, efficient handling of

complex patterns, and real-time data analysis. In

network automation, machine learning algorithms are

applied to log analysis, fault prediction, and resource

optimization, enabling automated and intelligent

operations.

6 CHALLENGES AND FUTURE

DIRECTIONS

6.1 Challenges in Machine Learning

for Network Traffic Analysis and

Prediction

Despite significant progress, machine learning in

network traffic anomaly detection and prediction

faces several challenges. The dynamic and complex

nature of network traffic data limits model accuracy

and generalization. Real-time analysis of big data

demands more efficient algorithms and

computational power. Imbalanced data samples,

adversarial data, and the need for multi-source data

fusion further complicate model generalization.

Balancing model performance and interpretability is

also a critical issue, requiring tailored approaches

based on specific needs and scenarios.

In summary, machine learning in network traffic

analysis and prediction faces challenges related to

data complexity, real-time processing, and privacy

protection (Liu etal, 2024). Addressing these issues

requires technological innovation and

interdisciplinary collaboration.

6.2 Future Directions for Machine

Learning in Network Traffic

Analysis and Prediction

Future developments in machine learning for network

traffic analysis and detection will focus on the

following areas:

The application of deep learning and

reinforcement learning is developing rapidly, driving

the construction of automated and intelligent

decision-making systems. These systems use

distributed processing frameworks to enhance their

processing capabilities, so that they can efficiently

process and analyze large amounts of data. At the

same time, with the rise of multi-source data fusion

Analysis of Machine Learning-Based Methods for Network Trafﬁc Anomaly Detection and Prediction

553

and cross-domain learning, the system can more

comprehensively understand and utilize information

from different sources.

In this process, the interpretability, transparency,

and visual interface of the model become particularly

important, which help users understand the decision-

making process of the model and enhance the trust of

the system. In addition, the application of real-time

processing and stream computing frameworks

enables data to be analyzed and processed at the

moment of generation, meeting the demand for rapid

response.

In order to achieve a higher level of technology,

cooperation and innovation in different fields become

essential, especially the collaboration between

computer science, statistics, and network security.

This interdisciplinary cooperation will promote the

birth and application of new technologies and lay the

foundation for future development.

In the future, machine learning in network traffic

analysis and detection will become more automated

and intelligent, emphasizing privacy protection,

model interpretability, and real-time processing

capabilities. Interdisciplinary collaboration and

technological innovation will be key drivers of

progress in this field.

7 CONCLUSIONS

This paper reviewed the application of machine

learning in network traffic anomaly detection and

prediction, covering key technologies such as data

preprocessing, feature engineering, model evaluation,

and optimization. It described the advancements in

traditional machine learning and deep learning

methods for traffic classification, anomaly detection,

and traffic prediction. The paper highlighted the

challenges of data complexity, real-time processing,

and privacy protection in network traffic analysis and

prediction. To address these challenges, machine

learning will rely on interdisciplinary collaboration

and technological innovation to develop more

automated, intelligent models that prioritize privacy

protection, model interpretability, and real-time

processing capabilities.

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