Hybrid Machine Learning Model for Efficient Malware Network

Attack Detection in IoT Environment

Bommireddy Srivani, Ankalugari Niharika, Machapura Sailaja,

Manini Ramyasri and Pochamireddy Tejasree

Ravindra College of Engineering for Women, Near Venkayapalle, Pasupula Village, Nandikotkur Road, Kurnool 518452,

Andhra Pradesh, India

Keywords: Malware Detection, Machine Learning, IoT Security, Network Attacks, Hybrid Model, Cybersecurity.

Abstract: The exponential growth of the Internet of Things (IoT) has significantly increased the attack surface for cyber

threats, making malware-based network attacks a critical security challenge. Traditional intrusion detection

systems (IDS) often struggle to cope with the high volume, complexity, and evolving nature of these attacks.

To address this, we propose a Hybrid Machine Learning Model that integrates supervised learning, ensemble

techniques, and deep learning-based anomaly detection to enhance the accuracy and efficiency of malware

detection in IoT networks. The proposed model leverages feature selection, real-time traffic analysis, and

hybrid classification to detect malicious network activities while minimizing false positives. We employ a

combination of Decision Tree, Random Forest, and Deep Neural Networks (DNNs) to classify benign and

malicious traffic with high precision. Experimental evaluations using benchmark datasets demonstrate that

our model outperforms traditional IDS models, achieving superior detection rates, lower latency, and

enhanced robustness against sophisticated cyberattacks. Despite its high efficiency, challenges such as

adversarial attacks, scalability concerns, and real-time deployment overhead remain open areas for further

research. Future work will explore federated learning, blockchain-based authentication, and explainable AI

(XAI) to further strengthen IoT security. The proposed hybrid approach provides a scalable, intelligent, and

real-time malware detection system, contributing to a more resilient IoT security framework.

1 INTRODUCTION

The proliferation of IoT devices has increased the

attack surface for cybercriminals. Malware network

attacks pose a significant threat, leading to data

breaches, device manipulation, and service

disruptions. Traditional signature-based and

heuristic-based detection methods struggle to adapt to

evolving malware threats. Machine Learning (ML)

has emerged as a powerful solution for identifying

and mitigating such threats. However, individual ML

algorithms often suffer from limitations such as high

false-positive rates and scalability issues. To address

these challenges, this research proposes a hybrid ML

model combining supervised and unsupervised

learning for efficient malware detection in IoT

networks.

The rapid expansion of the Internet of Things

(IoT) has revolutionized various industries, enabling

seamless connectivity between smart devices. From

smart homes to industrial automation, IoT devices

have enhanced efficiency and productivity. However,

the integration of these devices into critical

infrastructures has also introduced significant

security challenges. IoT networks are particularly

vulnerable to malware-based cyberattacks, which

exploit weak authentication, unsecured protocols, and

lack of robust intrusion detection mechanisms. As

traditional security solutions struggle to keep pace

with evolving threats, machine learning (ML)-based

hybrid models have emerged as a promising approach

to detect and mitigate malware attacks effectively.

Malware attacks on IoT networks often leverage

botnets, ransomware, spyware, and other malicious

programs to compromise device integrity, exfiltrate

data, and disrupt operations. Unlike traditional

computing systems, IoT environments pose unique

security challenges due to their heterogeneous

architecture, limited computational power, and

diverse communication protocols. Conventional

signature-based intrusion detection systems (IDS) fail

to detect zero-day attacks, making behavior-based

770

Srivani, B., Niharika, A., Sailaja, M., Ramyasri, M. and Tejasree, P.

Hybrid Machine Learning Model for Efﬁcient Malware Network Attack Detection in IoT Environment.

DOI: 10.5220/0013905300004919

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 3, pages

770-774

ISBN: 978-989-758-777-1

anomaly detection techniques a necessity. Hybrid

machine learning models, which combine multiple

algorithms and learning approaches, offer enhanced

accuracy and adaptability in detecting advanced

malware threats.

Numerous research efforts have been directed

toward improving malware detection in IoT

environments, employing techniques such as deep

learning, ensemble learning, and federated learning.

These models leverage both supervised and

unsupervised learning techniques to classify normal

and malicious traffic effectively. However, challenges

remain in terms of false positives, computational

efficiency, and adaptability to new attack vectors. To

address these issues, researchers have proposed

hybrid models that integrate multiple ML techniques

to improve detection accuracy and minimize

computational overhead.

This study aims to explore a Hybrid Machine

Learning Model that efficiently detects malware

attacks in IoT networks. The proposed framework

leverages a combination of deep learning, ensemble

learning, and feature selection techniques to enhance

detection accuracy. By utilizing real-world datasets

such as CICIDS2017, IoT-23, and UNSW-NB15, the

model is trained to recognize sophisticated attack

patterns and minimize false positives. Additionally,

this work investigates the impact of feature

engineering, model optimization, and real-time

processing on the effectiveness of malware detection

in IoT ecosystems.

The rest of this paper is structured as follows:

Section 2 presents a detailed literature review on

existing IoT malware detection techniques. Section 3

discusses the proposed hybrid machine learning

model, including data preprocessing, feature

selection, and classification methods. Section 4

covers the experimental results, evaluation metrics,

and comparison with existing models. Section 5

provides a detailed discussion on the findings,

limitations, and future research directions. Finally,

Section 6 concludes the paper, summarizing key

contributions and potential improvements in the field

of IoT security.

2 LITERATURE REVIEW

Several studies have explored ML-based approaches

for malware detection in IoT networks.

• Supervised Learning Models: Researchers

have utilized classification algorithms such as

Decision Trees, Support Vector Machines

(SVM), and Neural Networks to detect

malware. However, these methods require

extensive labeled datasets.

• Unsupervised Learning Models: Clustering

techniques like K-Means and Autoencoders

have been employed for anomaly detection,

but they struggle with precision and recall.

• Hybrid Approaches: Recent works suggest

combining multiple ML techniques to enhance

detection accuracy. However, existing models

often lack an optimized feature selection

process, leading to computational

inefficiencies.

This paper builds upon these works by integrating

feature selection, ensemble learning, and an

optimized hybrid ML framework. Table 1 shows the

Summary of Existing Machine Learning-Based IoT

Malware Detection Technique.

Table 1: Summary of existing machine learning-based IoT malware detection techniques.

Reference Methodology Dataset Used

ML Model(s)

Used

Key Findings Limitations

N. Moustafa

et al. (2019)

Big Data analytics for

intrusion detection

UNSW-NB15

Decision Trees,

Random Forest

Improved

detection rate

High false-

positive rate

M. Roesch

(1999)

Signature-based

intrusion detection

(Snort)

Custom

network logs

Rule-based

Effective for

known attacks

Fails for zero-day

attacks

Y. Meidan et

al. (2019)

IoT device anomaly

detection

UNSW-

NB15, IoT-23

Isolation Forest,

SVM

Detects

unauthorized

devices

Requires high

computational

power

Hybrid Machine Learning Model for Efﬁcient Malware Network Attack Detection in IoT Environment

771

S. Kumar and

R. Kaur

(2020)

Deep learning-based

anomaly detection

CICIDS2017 CNN, LSTM

High accuracy

in intrusion

detection

Limited

explainability

A. Javaid et

al. (2016)

Hybrid deep learning

for intrusion detection

NSL-KDD

Autoencoders,

ANN

Effective in

real-time

detection

High training time

R. Sommer

and V. Paxson

(2010)

ML for network

anomaly detection

DARPA

dataset

Naïve Bayes,

Decision Trees

Reduced

manual effort

Needs feature

engineering

S. H. Kim et

al. (2021)

Feature selection for

IoT security

CICIDS2017 XGBoost

Efficient in

feature

reduction

Limited dataset

size

M. S. Hossain

et al. (2019)

Voice-based malware

detection

Custom IoT

voice data

DNN

High accuracy

in voice

anomaly

detection

Not applicable for

all IoT devices

D. U. Nobles

(2020)

Case study on

malware detection

Custom

enterprise

dataset

SVM, Logistic

Regression

Demonstrated

feasibility of

ML-based

malware

detection

Requires

continuous model

updates

HaddadPajouh

et al. (2018)

IoT malware threat

hunting

IoT-23 RNN, GRU

Detects

complex

malware

ehavio

High false alarms

A. Rezvy et

al. (2021)

Federated learning for

IoT security

Custom IoT

logs

FedAvg, CNN

Privacy-

preserving

approach

Requires high

communication

overhead

Vinayakumar

et al. (2021)

Adversarial ML for

detecting malicious

domains

DNS-based

datasets

CNN, RNN

Detects

adversarial

malware

Susceptible to

evasion

techniques

M. Litchfield

(2022)

Smart home IoT

attack case study

IoT network

dataset

Random Forest,

KNN

Highlights

IoT security

vulnerabilities

Limited to smart

home devices

H. O. Alanazi

(2021)

Hybrid ML for

network anomaly

detection

UNSW-NB15 RF + SVM

High accuracy

in real-time

Performance

drops with large

datasets

S. A.

Chowdhury

(2023)

Comprehensive ML-

based IoT security

framework

Custom IoT

datasets

Ensemble

Learning (RF +

XGBoost)

Balanced

performance

with minimal

overhead

Requires tuning

for new attacks

3 IMPLEMENTATION

METHODOLOGY

3.1 Data Collection and Preprocessing

• IoT network traffic datasets from public

repositories such as CICIDS and UNSW-

NB15 are used.

• Data preprocessing includes feature

extraction, normalization, and noise reduction

to enhance model performance.

3.2 Feature Selection

• Recursive Feature Elimination (RFE) and

Principal Component Analysis (PCA) are

applied to identify the most relevant network

traffic features.

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3.3 Hybrid Machine Learning Model

• Phase 1: Unsupervised Learning (Autoencod-

ers, Isolation Forest) is used for anomaly de-

tection.

• Phase 2: Supervised Learning (Random For-

est, XGBoost) is applied for malware classifi-

cation.

• Phase 3: Ensemble Learning aggregates

predictions from multiple models for higher

Accuracy.

3.4 Model Training and Validation

• The model is trained on 80% of the dataset and

tested on the remaining 20% using cross-

validation.

• Performance metrics include Accuracy,

Precision, Recall, F1-score, and False Positive

Rate (FPR).

4 RESULTS AND DISCUSSION

The proposed hybrid model was evaluated against

benchmark ML models.

Table 2 SHOWS THE

Performance Metrics Comparison of Machine

Learning Models for IoT Malware Detection.

Figure

1 shows the Performance Comparison of ML Models

for IoT Malware Detection.

4.1 Performance Comparison

Figure 1: Performance comparison of ML models for IoT

malware detection.

Table 2: Performance metrics comparison of machine learning models for IoT malware detection.

Model Accuracy Precision Recall F1-Score FPR

Decision Tree 85.30% 82.10% 83.40% 82.70% 7.50%

SVM 88.90% 86.70% 87.20% 86.90% 6.10%

Random Forest 92.40% 91.30% 90.80% 91.00% 4.70%

Hybrid Model (Proposed) 96.20% 95.50% 95.10% 95.30% 2.30%

4.2 Key Findings

• The hybrid model outperforms traditional

ML approaches in accuracy and precision.

• Feature selection significantly reduces

computational overhead while improving

detection rates.

• The ensemble learning approach minimizes

false positives, enhancing reliability.

5 CONCLUSIONS

In this study, we proposed a Hybrid Machine

Learning Model for detecting malware-based

network attacks in the IoT environment. By

integrating multiple machine learning techniques,

including supervised and ensemble learning

approaches, we developed a system that effectively

identifies malicious traffic patterns while minimizing

false positives. Our model leverages feature selection,

deep learning-based anomaly detection, and

optimized classification algorithms to enhance

detection accuracy and scalability. Experimental

results demonstrate that the proposed hybrid model

outperforms conventional standalone models in terms

of detection rate, precision, recall, and F1-score. The

integration of Decision Tree, Random Forest, and

Deep Neural Networks (DNNs) significantly

improves the model's adaptability to evolving attack

patterns. Furthermore, the use of network traffic

Hybrid Machine Learning Model for Efﬁcient Malware Network Attack Detection in IoT Environment

773

feature engineering and real-time monitoring ensures

the system’s applicability in practical IoT security

frameworks. Despite these promising results,

challenges such as scalability, adversarial attacks, and

computational overhead remain areas for future

research. The incorporation of federated learning,

blockchain-based authentication, and explainable AI

(XAI) can further enhance the robustness and

trustworthiness of IoT security solutions.

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