Big Data and Deep Learning for Scalable Network Traffic

Monitoring and Analysis

V. Lakshmi Chaitanya, M. Sharmila Devi, K. Vyshnavi, U. Deepika,

S. Sana Samreen and U. Jayanthi

Department of Computer Science and Engineering, Santhiram Engineering College,

Nandyal‑518501, Andhra Pradesh, India

Keywords: Network Traffic, Deep Learning, Big Data, RNN, CNN, Apache Spark, Hadoop, Neural Networks.

Abstract: Network traffic analysis is important to assure the security, efficiency and proper management of digital

communications. Due to the increased speed and complexity of cyberspace, traditional methods for

monitoring network traffic are often difficult to enable threats. Modern network security is based on advanced

data analytics techniques that handle large amounts of information to identify potential threats and anomalies.

This paper focuses on implementing big data technologies and deep learning models to improve large-scale

network traffic analytics. Machine learning techniques and deep learning techniques such as folding fish

networks (CNNS) and repeating neural networks (RNNs) can help identify suspicious activity by determining

patterns of network traffic. As the internet speed increases and more devices are created on the network,

traditional methods become effective for large data flows. Using big data frameworks such as Apache Spark

and Hadoop, systems can efficiently process and analyze network traffic in real time. Big data integration in

deep learning improves security by quickly capturing and reducing cyber threats such as: This paper examines

how these advanced technologies can enhance network security, prevent attacks, and improve overall Internet

security. Additionally, the future developments and improvements in network surveillance to ensure a safer

and more efficient digital environment. The ultimate goal is to create a system that protects our data, improves

online security, and ensures seamless digital interaction.

1 INTRODUCTION

The rapid expansion of digital networks and the

increasing reliance on internet-based transactions

have led to critical concerns about network traffic

monitoring and safety. Network Traffic Analysis

(NTA) plays a key role in detecting malicious activity

and often relies on intrusion detection systems (IDs)

and various surveillance models. However,

traditional methods include sensitivity to cyber threat

development, scalability issues, and inefficiency in

the conversion of large amounts of network traffic.

For example, a signature-based ID relies on

predefined attack patterns and is not effective against

new or unknown threats. Furthermore, traditional

approaches for machine learning need to fight and

require continuous manual updates in adapting to

dynamic network conditions that limit long-term

effectiveness. To address these challenges, this study

suggests an advanced framework for network traffic

analysis using large data technologies and deep

learning models to obtain more accurate and efficient

intrusion recognition. Technologies such as Apache

Spark and Hadoop allow large-scale data processing,

while deep learning models, particularly long-term

memory networks (LSTMs), improve the system's

ability to recognize complex attack patterns over

time. In contrast to traditional methods, deep learning

models can automatically learn from a vast dataset,

reducing dependency on predefined rules and

improving the ability to recognize threats.

Additionally, the system integrates a hybrid learning

approach combining monitored techniques and

unmanned techniques to improve accuracy and

adaptability. By using this progress, the proposed

framework improves security, real-time recognition

of threats, and ensures more effective network

monitoring. This study examines the implementation

of these methods, the impact of cybersecurity

538

Chaitanya, V. L., Devi, M. S., Vyshnavi, K., Deepika, U., Samreen, S. S. and Jayanthi, U.

Big Data and Deep Learning for Scalable Network Trafﬁc Monitoring and Analysis.

DOI: 10.5220/0013886100004919

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

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 2, pages

538-542

ISBN: 978-989-758-777-1

impacts, and future improvements to create a safer

and more resilient digital network infrastructure.

2 LITERATURE REVIEW

Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P.,

Iorkyase, E. & Tachtatzis, C. (2021) “Threat Analysis

of IoT Networks Using Machine Learning" This

study focuses on using models for machine learning,

including random forests and support vector

machines (SVMs) in IoT network security. We

demonstrated how these models can recognize a

variety of cyber threats in an IoT environment.

However, it also raised concerns about scalability and

the need to adapt to continuous learning models to

adapt to threat development.

Lee, W. & Stolfo, S.J. In this study, we proposed

a related rule for machine learning methods,

especially decision-making-tree and detection of

network representations. This approach showed an

improved accuracy compared to traditional rule-

based systems. However, effectiveness was limited

by selection of characteristics requiring manual

adjustment and expert intervention.

Breiman, L. (2001) “Random Forest" Breiman's

work demonstrated the random forest algorithm. This

was often used in network security to recognize cyber

threats. Researchers used it in network traffic analysis

because it can handle high-dimensional data and

improve classification accuracy. Recognizing known

threats, Random Forest fell smoothly, but his ability

to recognize zero-day attacks was limited.

Tavallaee, M., Bagheri, E., Lu, W. & Ghorbani,

A. A. (2009) "Detailed analysis of KDD Cup 99 data

records." This article critically analyzed a wide range

of KDDCUP99 data records, highlighting their

redundancy and limitations. This has led to the

development of new data records such as NSL-KDD

and CICIDS2017.

Goodfellow, I., Bengio, Y. & Courville, A. (2016)

“Deep Learning" This work laid the foundation for

deep learning techniques applied to a variety of fields,

including cybersecurity. The authors have introduced

advanced neuron network architectures such as

CNNS and LSTM. This has become critical when

recognising networked styles. The deep learning

model has proven effective against complex patterns

of learning from network traffic, but requires high

computational resources.

3 METHODOLOGY

The proposed methodology focuses on the integration

of large data processing with deep learning

techniques to improve network traffic analysis and

intrusion recognition. Traditional methods make it

important for scalability and adaptability to develop

robust and intelligent systems that can efficiently

master large amounts of network traffic.

Figure 1

show the System Architecture.

Data collection and processing: A network traffic is

collected from different types of thresholds, including

networks (ID), and data sets), and NSL-KDD. The

collected data was processed to experience the cancel

of noise, handle the lost values, and the appropriate

surface network compounds for the filter.

Great data processing framework: To control -

scale large-network data is good; this method

combines a distributing distribution as an Apache

Sneak and Hadoop. This technology is controlled as

preparing the scale, sending monitoring and analysis

of a periodic. The use of high data framework can

store, restore, and a large network list.

Removal and selection of parts: The network traffic

results, such as the size of the package, protocol type,

and chat, and relevant to providing great knowledge

of the network. Damaged conditions such as the

analysis of key areas (PCA) and autoencoder are used

to optimize symbols and improves modeling. The

meaning of the display indicates the identification of

the time period depending on the calculations and

calculations in traffic models.

Development of study patterns in -depth: The

main reason of this method is the application of

accessory global classic composition (CNSS) and

short memory networks (LSTM): CNN is used to

take language startup data from network traffic data,

capturing difficult models related to normal and

negative action. LSTM is used to cut the youth trust

and find the long-term conditions and increases

complex attack patterns. The mixed model is trained

using the square basement, using methods such as

preparing and restrictions to form the examples.

Big Data and Deep Learning for Scalable Network Trafﬁc Monitoring and Analysis

539

3.1 Architecture

Figure 1: System Architecture.

3.2 Dataset Information

Dataset are very important for the deep learning

models to analyse the network traffic. Every dataset

consists of both normal and abnormal activities in the

traffic. The network dataset consists of some special

features like source ip address, destination ip address,

source port, destination port, protocol type. Sometime

based features are flow duration, packet inter arrival

time and number of packets per second. Some of the

attacks like deniol of service, brute force attack, web-

based attack, malware and botnet attacks Some of the

mathematical formulas are: Table 1 show the

Network Traffic Data Set.

Packet inter arrival time: IAT=T

i+1

-T

Where T=time

Flow duration Calculation: D

= T

end

- T

start

Where D

=flow duration

= end time

start

= start time

Packet rate per second: PRPS = N

/ D

Where Df= flow duration

Packet Rate: Total Time /Interval Total Packets

Byte Rate: Total Time /Interval Total Bytes

Data Set:

Table 1: Network Traffic Data Set.

Flow

Duration

Total

Fwd

Packets

Total

Backward

Packets

Fwd

Packet

Length

Max

Bwd

Packet

Length

Max

Flow Bytes/s

Flow

Packets/s

Label

16795 10 6 1747 969 742.3619315 28.96217147 0

1860 5 1 1493 1286 1940.269246 25.19838823 0

39158 4 5 1935 1857 3889.343012 8.797657125 1

45732 2 1 1071 1463 1711.990842 22.97159368 0

12284 10 4 834 1507 2928.002992 45.95605866 0

7265 19 2 1745 872 3775.762295 41.36101835 0

17850 1 10 536 993 985.7467159 14.72231143 1

38194 5 5 1488 1250 3583.841073 25.99735031 0

22962 13 10 777 1679 913.4189947 41.75254412 0

48191 4 1 1719 790 4843.08384 8.032304965 0

45131 16 6 1415 1667 2697.773585 22.43266636 0

4 IMPLEMENTATION AND

RESULTS

In this section the implementation details are

mentioned to detect the network traffic. 4.1 Section

contains the model selection and 4.2 section contains

the results of the implements.

4.1 Model Selection

Model 1: Random Forest: An effective ensemble

learning technique for intrusion detection and

network traffic analysis is Random Forest. In order to

ensure high accuracy and resistance to overfitting, it

builds numerous decision trees and uses majority

voting to classify network traffic. By processing high-

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

540

dimensional datasets and choosing key traits, it

effectively manages massive amounts of data. Data

collection, preprocessing, feature selection, model

training, and classification of network traffic into

normal or attack categories, such as DoS, DDoS, and

botnet incursions, are all part of the implementation

process. Prior to being implemented in real-time

monitoring systems, the model is assessed using

metrics for recall, accuracy, and precision. It is very

effective in improving network security because of its

scalability, handling of missing values, and flexibility

in response to changing threats. It improves

cybersecurity through real-time threat detection and

mitigation when combined with big data frameworks

and deep learning techniques.

Model-2: Decision Tree: Because of its ease of use

and interpretability, decision trees are a popular

machine learning technique for network traffic

analysis and intrusion detection. It creates a tree-like

structure with each node representing a decision rule

by dividing data into branches according to feature

requirements. Until a final prediction is produced, the

classification process proceeds through a succession

of logical conclusions. By examining network traffic

characteristics like packet size, flow time, and

protocol type, decision trees are useful for spotting

attack trends. They can identify threats like DoS,

DDoS, and brute force assaults and categorize traffic

into benign or malevolent groups. Accuracy,

precision, and recall measures are used to assess the

algorithm's performance once it has been trained on

labelled datasets. Nevertheless, pruning strategies or

incorporating them into ensemble approaches like

Random Forest might help reduce the overfitting that

Decision Trees may have. They are useful for real-

time network security applications because of their

capacity to manage big datasets and offer transparent

decision-making logic.

Model-3: Logistic Regression: Logistic regression

is frequently used in conjunction with deep neural

networks (DNNs) or convolutional neural networks

(CNNs) in deep learning-based network security. In

this case, intricate patterns are extracted from

network traffic data using deep learning models, and

attack probabilities are predicted using logistic

regression in the last classification layer. Large

datasets with characteristics like packet size, flow

time, and protocol type, such as CICIDS2017 are

used to train the model. Logistic Regression

determines the best weights to efficiently distinguish

between malicious and legitimate traffic using

gradient descent and backpropagation. The capacity

of logistic regression to effectively manage massive

network traffic and produce probabilistic outputs that

can support decision-making is one of the benefits of

utilizing it in deep learning. Deep learning

architectures, on the other hand, improve its

capabilities by extracting hierarchical and non-linear

features, as it presupposes a linear decision boundary.

Threats including Denial-of-Service (DoS), botnets,

and brute-force attacks can be accurately detected

using deep learning models employing Logistic

Regression as the last classification layer in real-time

intrusion detection systems.

4.2 Results

Table 2 show the accuracy, precision, recall and F1-

score values for the different algorithms are shown

below.

Figure 2 show the Accuracy of Deep Learning

Models.

Table 2: Performance of Different Deep Learning Models.

Model

Accuracy

(%)

Precision

(%)

Recall

(%)

F1-

Score

(

)

Neural

Networks

90.6 84.6 95.7 89.8

Random

Forest

81.1 72.4 91.3 80.8

Decision

Tree

67.9 60.7 73.9 66.7

Logistic

ression

60.4 53.3 69.6 60.4

Figure 2: Accuracy of Deep Learning Models.

Big Data and Deep Learning for Scalable Network Trafﬁc Monitoring and Analysis

541

5 CONCLUSIONS

This paper shows the size of the data and browse a

network activity to make the most of the internet

wounds. Using deep study patterns such as CNSS,

RNN, and the transformers, we can discard the

amount of network data. Although these conditions

showcase the most important promises, challenges

such as major compound costs, the need for data

labeled and is still in the same description. Future

work needs to focus on speed, and know more, and

can be learned from small data. The combination of

study in the study and traditional security methods

can also be better way for network protection. To

briefly, the use of major and study data in the analysis

of network transfer is essential to threatening and

compares to the digital environment.

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

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