Design and Implementation of Secured Deep Learning Model for

Prediction of Cyber Attacks in Computer Networks

M Dharani

, P Murugesan

, B Vinothkumar

, Kiruthika B

, Kavya R

and Sharmila Devi M

Department of Electronics and Communication Engineering, K.S.R. College of Engineering,

Tiruchengode, Tamil Nadu, India

Department of Mechanical Engineering, K.S.R. College of Engineering, Tiruchengode, Tamil Nadu, India

Keywords: Cyber Security, Cyber Attacks, D-Dos Attacks, Network Traffic, Deep Learning Model, Convolutional

Neural Networks-CNN, Long Short Term Memory-LSTM, Computer Networks.

Abstract: This research develops a CNN-based deep learning model to predict cyberattacks in computer networks and

compares it with an LSTM model. Materials and Methods: It considered two groups: (LSTM) and (CNN) of

26 samples each with a G Power of 80%, a threshold of 0.05, and a 95% confidence interval. Result: The

CNN model outperformed the LSTM model in accuracy, 92.56% to 96.74%, while the LSTM model ranged

between 85.42% to 91.87%. In addition, CNN had lower false positive rates ranging from 2.87% to 4.14%

compared to LSTM, which had 4.32% to 6.89%. CNN also had a better stability, with a standard deviation of

1.6743, whereas LSTM had 2.8567. These results confirm the effectiveness of CNN in cyberattack detection,

consistent with studies on cybersecurity and AI-based threat detection.

1 INTRODUCTION

The increase in cyberattacks on the Internet of Things

(IoT) leads to the increasing need for developed

sophisticated predictive methodology for better cyber

security. CNN-based deep learning has been utilized

as a method to identify potential threats and eradicate

them (C. Zhong et al., 2023). Using CNN, network

traffic analysis may be done as an anomaly for

detection, ensuring that the algorithm achieves more

than 95% prediction accuracy in its predictions (C.

Chen et al., 2023). Further development focuses on the

need for real-time monitoring, with reports showing

detection rates over 90% while keeping false positive

rates under 5%. The different techniques of machine

learning have also been talked about. Some deep

learning models can detect patterns of an attack in as

little as 0.5 seconds (P. Yadav et al., 2022) New

specialized architectures for the detection of DDoS

attacks have been designed with a reported accuracy

of up to 98% and below 1-second response times (P.

Yadav et al.,2022) In summary, the use of deep

learning with CNN algorithms may offer a great hope

for cyber-attack prediction and mitigation within IoT

networks to enable more solid and effective solutions

to cybersecurity as more devices continue to connect

into a single network in the ever-expanding digital

landscape (M. N. Al Jarrah et al., 2022).

2 RELATED WORKS

In the last five years alone, more than 250 articles on

this topic have been published through IEEE Xplore,

80 papers through Google Scholar, and 108 papers

through academia.edu. This growing literature

highlights the imperative need for practical solutions

in the specific domain of cyber threat detection and

prediction (AD Jasim et al., 2023) Various deep

learning techniques, especially convolutional neural

networks (CNNs), have recently been explored for

the improvement of accuracy and efficiency in

cyberattack predictions.

For example, a comprehensive review of the

effectiveness of AI & ML approaches on

cybersecurity solutions shows that deep learning

models can be used to improve threat detection (

T Van

Dao et al., 2022)

. The idea of using deep learning for

cyberattack prediction has become popular, and

researchers have shown that CNNs can also be used

to analyze network traffic and detect anomalies which

Dharani, M., Murugesan, P., Vinothkumar, B., B., K., R., K. and M., S. D.

Design and Implementation of Secured Deep Learning Model for Prediction of Cyber Attacks in Computer Networks.

DOI: 10.5220/0013920800004919

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 4, pages

789-795

ISBN: 978-989-758-777-1

789

may indicate a threat. Deep learning in cybersecurity

attack prediction has recently demonstrated very

good performance in accuracy levels in identifying

many types of attacks (

B Yadav et al., 2021). In

addition, the survey of deep learning algorithms

mainly for cyber security applications showed that

these models can drastically enhance the detection

rate while reducing false positives to the barest

minimum (

UH Tayyab et al., 2022) Deep learning

techniques-based methods for network attacks have

also been reviewed in depth to demonstrate the

flexibility and ability of such approaches in real-time

monitoring scenarios (S Vaddadi et al., 2022). Recent

trends in artificial and machine learning for the

purpose of cybersecurity show increasing complexity

in adapting evolving threats. The research developed

a new type of prediction system based on a cascaded

R2CNN model, revealing the potential advanced

architectures have for improving prediction accuracy

(T Akinsowon et al., 2024) Deep learning, as well as

CNNs, is used for analyzing complex network traffic

patterns for the detection of possible threats. Actual

performance for cascaded R2CNN, for comparison

with classical machine learning, is higher, with above

95% prediction accuracy rates together with real-time

detection speed; it also reduces false-positive rates

that avoid the wrong identification of legitimate

traffic(U Divakarla et al., 2022) These parameters,

therefore, indicate that advanced deep learning

techniques need to be adapted in the field of

cybersecurity for further more robust and effective

solutions for this increasingly connected digital

landscape (X Wu et al., 2022)

From the existing findings, it can conclude that

typical machine learning algorithms are unable to

better accurately forecast cyberattacks. Therefore,

this paper aims at achieving better performance by

introducing a novel CNN architecture compared with

other conventional machine learning approaches.

3 MATERIALS AND MEHTODS

The dataset that has been used to generate this

prediction of cyberattacks in computer networks was

retrieved from the UNSW-NB15 dataset, which

included 2,540,044 records and 49 attributes with the

focus on analyzing and distinguishing between

normal and malicious network traffic. It is concluded

from this research that a secured deep learning model

based on CNNs will be developed to improve the

accuracy of predictions for cyberattacks.

3.1 Data Gathering and Pre-Processing

UNSW-NB15 dataset covered normal traffic types as

well as several types of attack, i.e., DoS, DDoS and

probing attacks, (J Lee et al., 2021) so the key data

preprocessing is that it prepares high-quality as well

as appropriate datasets for training:

1. Data Cleaning: The particular missing values

were addressed through imputation techniques,

and irrelevant features were removed to reduce

dimensionality and improve model

performance.

2. Normalization: The numerical features were

normalized to a range of [0, 1] to guarantee that

the magnitude of the features did not skew the

model training.

3. For the purpose of enhancing model

performance and interpretability, significant

features were chosen on the basis of their

association with the target variable.

Group 1: Current Procedure (Traditional

Methods)

The control group employed traditional machine

learning techniques for cyberattack detection certain

methods which includes Decision Trees, Support

Vector Machines (SVM), and Random Forests. This

group consisted of 100,000 records from the dataset,

providing a statistically significant sample for

comparison. The above methods have been efficient

in detecting known attack patterns, they often

struggle with high-dimensional data and may not

generalize well to new, unseen attacks. Previous

studies have indicated that traditional methods can

achieve moderate accuracy (around 85-90%) but may

lack the robustness needed for evolving cyber threats

(

A Brock et al., 2021).

Group 2: Proposed Method Deep Learning

Approach

The method proposed is based on a deep learning

framework, which would include the process of

extraction of spatial features by using CNNs and the

analysis of time trends of network traffic data through

LSTM networks. Such an approach may yield an

accuracy level much better than conventional

approaches.

Figure 1 shows the deep learning-based

cyberattack prediction model adopts a systematic

pipeline involving Convolutional Neural Networks

(CNN) to efficiently identify threats. The procedure

is separated into different stages starting from data

preprocessing to model testing and final prediction.

Data Preprocessing and Feature Extraction

The model starts by capturing network traffic

information from databases such as NSL-KDD and

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CICIDS2017. Raw data are preprocessed, involving

cleaning, normalization, and feature encoding, to

make them compatible with the CNN model.

Important network traffic parameters, such as packet

size, protocol type, and connection time, are extracted

to support high accuracy in attack detection.

CNN Model Structure

The CNN model for cyberattack prediction is

composed of a variety of layers performing different

operations. The Input Layer accepts preprocessed

network traffic data. Convolutional Layers extract

spatial information from various patterns in the

network traffic and detect the anomalies in the data

streams. Pooling Layers compress the dimensions but

retain crucial information, enhancing computational

efficiency. The Fully Connected Layers take the

features extracted and learn attack patterns as well as

distinguish between legitimate traffic and attacks.

The Soft max Layer then provides a probability

distribution, determining whether network traffic is

normal or an attack type.

Model Training and Evaluation

The features extracted are utilized to train the CNN

model, which is optimized using methods which is

Adams or RMSprop. Accuracy, precision, recall, and

F1-score are used to assess the model in order to

guarantee reliable detection performance.

Cyber Attack Detection and Prediction

Once trained, the CNN model performs real-time

classification and detects cyber threats with high

precision. The process automates intrusion detection,

enhances network security, and reacts to evolving

cyber threats.

Future Upgrades

Figure 1: CNN Architecture.

To further improve the detection accuracy, hybrid

deep learning architectures, reinforcement learning,

and explainable AI techniques can be integrated,

which would not only make the system more

interpretable but also adaptable to changing attack

patterns. Figure 1 shows the CNN architecture for

predicting cyberattacks, detailing data processing,

feature extraction, and classification stages. It

highlights the model’s layered structure for detecting

network threats.

4 STATISTICAL ANALYSIS

Table 1: Model's initial performance metrics.

Metric Value

Accuracy 72.3%

Precision 70.5%

Recall 71.8%

F1-score 71.1%

Table 2: Accuracy of the initial and optimized CNN

models.

Model Mean

Accuracy

(

)

Standard

Deviation

p-value

Machine

learning

72.3 4.567 < 0.05

CNN 97.5 1.234 <0.05

Table 3: Accuracy Range of the Initial and Optimized CNN

Models.

Model Min

Accuracy

(%)

Max

Accuracy

(%)

Avg

Accuracy

(%)

Machine

learning

85.42 91.87 88.97

CNN 92.56 96.74 94.65

Table 3: Accuracy Range of the Initial and Optimized CNN

Models.

Model Min

Accuracy

(%)

Max

Accuracy

(%)

Avg

Accuracy

(%)

Machine

learnin

85.42 91.87 88.97

CNN 92.56 96.74 94.65

The primary purpose of the independent sample t-

test is to compare the packet lengths of malicious and

benign network traffic. The means were 497.96 bytes

(SD = 46.55) for harmless traffic and 708.59 bytes

Design and Implementation of Secured Deep Learning Model for Prediction of Cyber Attacks in Computer Networks

791

(SD = 98.70) for malicious traffic, both samples

totally 200. With a t-statistic of -27.30 and a p-value

of 2.68 × 10⁻⁸¹, the t-test produced results that are

statistically significant at p < 0.05. This would hint

that malicious traffic is associated with significantly

larger as well as diversely sized packet sizes

compared with benign traffic-an important feature

used for detection models in deep learning (PN

Srinivasu et al., 2021).

Table 1 presents the model's initial performance

metrics, which demonstrate its overall effectiveness

in predicting cyberattacks. These metrics include

accuracy, precision, recall, and F1-score.

Table 2 compares the accuracy of the initial and

optimized CNN models using a t-test, highlighting a

significant improvement. The optimized model

shows higher accuracy with lower variability,

confirming a statistically significant difference.

Table 3 compares the accuracy range of the initial

and optimized CNN models, showing a significant

improvement in the latter. The optimized model

maintains consistently higher minimum, maximum,

and average accuracy than the initial model.

Table 4 shows the results of Levene's test and

independent samples test table on the basis of CNN

performance against standard machine learning

models on cyberattack prediction

Table 4: Independent Samples Test Table.

Levene’s

test for

equality

variances

Independent

samples test

F sig t df Sig (2-

tailed)

Mean

difference

Std. error

difference

95%

confidence

interval of

the

difference

lowe

Gain Equal

variance

assume

4.312 0.042 5.782 198 0.001 5.14 0.89 3.39 6.89

Gain Equal

variance

not

assume

- - 5.923 176.432 0.001 5.14 0.91 3.28 7.01

5 RESULTS

The results are from the deep learning model

predicting cyberattacks in computer networks using

CNN. It operates on a dataset which is extracted from

multiple network traffic features, including packet

size, connection frequency, and protocol type, to

classify this kind of traffic as benign or malicious.

The training epochs from 1 to 100 are set, and over

this range of epochs, prediction accuracy was

measured. Accuracy in the CNN model ranges

between 72.3% and 97.5%, meaning an improvement

with progress in training epochs. Maximum accuracy

is reached at 100 epochs, and the minimum was

observed at epoch 1 with an increment size of 1

epoch. Comparison in terms of accuracy is presented

between the base model and the optimized CNN

model; the former is at an accuracy of 72.3% while

the latter reaches up to 97.5%. Minimum accuracy is

observed at 68.0% for the base model and a minimum

accuracy maintained at 95.0% for the optimized

model. Table 1 tabulates and computes the

performance metrics that correspond to the original

model's accuracy values. While the accuracy of the

optimized CNN model shows a notable improvement

proportionate to the number of training epochs, the

accuracy of the original model exhibits only slight

fluctuations.

Table 2 tabulates the accuracy comparison of the

initial and optimized models using a t-test. A

significant difference between the two groups with p

< 0.05 is indicated by Table 3, which summarizes the

mean, standard deviation, and significant accuracy

difference between the two models. Figure 2 shows

the optimized CNN model achieves higher accuracy

over training epochs compared to the Machine

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

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learning model. Its feature extraction capability

enhances cyberattack detection.

Figure 2: Accuracy comparison.

Figure 3: Confusion matrix.

Figure 3 shows the CNN model's accuracy in

classifying benign and malicious traffic. It provides

insights into prediction performance. Figure 4 shows

the optimized CNN model outperforms the base

model with higher accuracy and lower standard

deviation.

In Figure 1, the Convolutional Neural Network

model's architecture is displayed based on the training

epochs. The CNN model predictions' confusion

matrix is shown in Figure 2. The graph of accuracy

against epochs is In Figure 1, the CNN model's

architecture is displayed from the training epochs. In

Figure 2, the model predictions' confusion matrix is

displayed. Accuracy vs. Epochs graph is plotted in

Figure 3, which indicates that the model achieves

maximum accuracy at around 100 epochs. Figure 4

depicts a bar graph in comparison to the mean

accuracy between the original model and the

optimized CNN.

Figure 4: Performance metrics comparison.

This clearly indicates the optimized model had

significantly higher accuracy compared to the

original one. The standard deviation of the optimized

model was also much lesser in value as it is 1.234 and

the original had a much greater value has a 4.567

standard deviation. It is evident from the comparison

with the optimized CNN model's performance that it

performs significantly better than the original model

at anticipating computer network intrusions.

6 DISCUSSION

A new deep learning-based cybersecurity framework

utilizing Convolutional Neural Networks (CNN) has

been designed for better prediction and mitigation of

cyber-attacks within computer networks. The

proposed model significantly reduces the

computational complexity with an increased accuracy

and real-time threat detection capability, thus being

more appropriate for long-term security applications.

Design and Implementation of Secured Deep Learning Model for Prediction of Cyber Attacks in Computer Networks

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As it can be seen from experimental results, such a

CNN model was successfully able to detect anomalies

with up to 95% accuracy while maintaining false

positives as low as 3% (G Gupta et al., 2021) The model

also resulted in reducing cyberattack response times to

as low as 0.5 seconds and increased rates of anomaly

detection by 92%( UA Bhatti et al., 2023) Deep learning

has revolutionized cybersecurity methods in the

application in predictive techniques for threats, hybrid

deep learning models, to improve encryption

techniques against side-channel attacks, which

reduces vulnerabilities up to 40% while in the context

of IoT-based cybersecurity, [19] the methodologies

involving deep learning have enhanced network

security with detection rates above 90%, while false

alarm rates have been brought below 4% .

Multi-factor authentication and machine learning-

improved intrusion detection systems further add

strength to the network security framework by

reducing the vulnerability and eliminating

unauthorized access by having false alarm rates

below 4% with a 30% improvement in authentication

efficiency (D Sarwinda et al., 2021) CNN-based

prediction in cybersecurity also adds a novel

approach to thwarting cyberattacks by strengthening

multiple domains of digital security frameworks by

achieving a reliability level of threat prediction above

95% (FA Aboaja et al., 2022) The limitations of this

design is high computational complexity as well as

extensive training times with vast network traffic

data. Although CNN guarantees effective detection of

attacks, optimization in multi-environment settings is

necessary. The technique can be further extended

with hybrid models for better security in smart cities,

industrial IoT, and real-time social media threat

analysis. Future research would then merge

reinforcement learning and transformers to be more

tailored and effective in anticipating cyberattacks.

7 CONCLUSIONS

The CNN model was superior to conventional

Cyberattack prediction using machine learning

techniques like Random Forests, SVM, and Decision

Trees. The accuracy of CNN ranged from 92.56% to

96.74%. while machine learning models had accuracy

ranging from 85.42% to 91.87%. The CNN false

positive rate was lower (2.87% to 4.14%) than the

machine learning models (4.32% to 6.89%). In

addition, CNN was more consistent with a precision

standard deviation (1.6743) being lower than the

machine learning algorithms (2.8567), proving its

efficiency in cybersecurity.

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