INTERNATIONAL JOURNAL OF DATA SCIENCE AND MACHINE LEARNING (ISSN: 2692-5141)
Volume 03, Issue 01, 2023
Published Date: - 04-02-2023 Page no:- 1-5
http://www.academicpublishers.org
P a g e
1 | 5
ENHANCING SECURITY IN MOBILE AD HOC NETWORKS:
INTRUSION DETECTION WITH SVM AND ANT COLONY
OPTIMIZATION
Raman Kumar
Department of Computer Science Engineering, Gnanamani College of Technology,
India
Abstract
Mobile Ad Hoc Networks (MANETs) are dynamic and self-configuring wireless networks,
making them vulnerable to various security threats, including intrusion attempts. Intrusion
detection systems (IDS) play a critical role in safeguarding MANETs against unauthorized access
and malicious activities. In this study, we propose an innovative approach to enhance the security
of MANETs through intrusion detection, leveraging the power of Support Vector Machines (SVM)
with Ant Colony Optimization (ACO). Our approach harnesses the robustness of SVM in pattern
recognition and classification, while ACO optimizes the SVM parameters, improving the accuracy
and efficiency of intrusion detection. Through extensive experiments and evaluations, we
demonstrate the effectiveness of this combined approach in mitigating intrusion threats in
MANETs
.
Key Words
Mobile Ad Hoc Networks (MANETs); Intrusion Detection; Support Vector Machine (SVM);
Ant Colony Optimization (ACO); Network Security; Intrusion Detection Systems (IDS); Wireless
Networks.
INTRODUCTION
Mobile Ad Hoc Networks (MANETs) have emerged as a versatile and dynamic paradigm
for wireless communication, enabling seamless connectivity in scenarios where traditional
infrastructure-based networks are impractical or unavailable. These self-configuring networks,
comprising mobile devices that can communicate directly with each other, are deployed in various
domains, including military operations, emergency response, vehicular communication, and more.
However, the unique characteristics of MANETs, such as their decentralized nature, limited
resources, and dynamic topology, render them susceptible to a multitude of security threats,
including intrusion attempts, data breaches, and denial-of-service attacks.
Intrusion Detection Systems (IDS) serve as the first line of defense against these threats,
actively monitoring network traffic and system activities to identify and respond to malicious
behavior promptly. In the context of MANETs, the development of effective IDS is of paramount
importance to ensure the integrity, confidentiality, and availability of data and services.
In this study, we embark on a journey to enhance the security of MANETs by proposing an
innovative approach to intrusion detection. Leveraging the power of Support Vector Machines
(SVM) in pattern recognition and classification and combining it with Ant Colony Optimization
(ACO) for parameter optimization, our approach aims to bolster the accuracy and efficiency of
intrusion detection in MANETs.
INTERNATIONAL JOURNAL OF DATA SCIENCE AND MACHINE LEARNING (ISSN: 2692-5141)
Volume 03, Issue 01, 2023
Published Date: - 04-02-2023 Page no:- 1-5
http://www.academicpublishers.org
P a g e
2 | 5
SVM has demonstrated exceptional performance in various classification tasks, making it a
compelling choice for intrusion detection. However, the effectiveness of SVM hinges on the
appropriate selection of hyperparameters. This is where ACO steps in, providing an intelligent
optimization technique to fine-tune SVM's parameters, resulting in a more robust and accurate
intrusion detection system.
The significance of our approach lies in its potential to mitigate the evolving security threats
faced by MANETs while optimizing the use of network resources. As we delve deeper into the
intricacies of SVM with ACO for intrusion detection in MANETs, this research aims to contribute
to the safeguarding of these networks, ensuring their reliability and secure operation in critical
applications.
METHOD
In the realm of wireless communication, Mobile Ad Hoc Networks (MANETs) stand as a
beacon of adaptability and connectivity, facilitating communication in environments where
traditional infrastructure-based networks may falter. However, the very attributes that make
MANETs invaluable—decentralization, dynamic topology, and limited resources—also render
them susceptible to security threats. Intrusion Detection Systems (IDS) are the vanguards of
defense in this domain, tasked with identifying and thwarting malicious activities. In this context,
our study introduces an innovative approach to enhance MANET security through intrusion
detection, merging the capabilities of Support Vector Machines (SVM) and Ant Colony
Optimization (ACO). SVM, renowned for its pattern recognition and classification prowess, forms
the foundation of our approach. ACO complements SVM by optimizing its parameters,
culminating in a more robust and efficient intrusion detection system. Our pursuit is driven by the
imperative to fortify the security of MANETs, safeguarding data integrity, confidentiality, and
availability in these dynamic and vital wireless networks.
Our quest to enhance security in Mobile Ad Hoc Networks (MANETs) through intrusion
detection unfolds through a meticulously designed methodology that seamlessly combines the
strengths of Support Vector Machines (SVM) and Ant Colony Optimization (ACO).
Data Collection and Preprocessing: We commence by gathering a comprehensive dataset
comprising network traffic, system activities, and instances of known intrusions within a simulated
MANET environment. This dataset serves as the basis for training and evaluating our intrusion
detection system.
Feature Engineering: To facilitate effective intrusion detection, we perform feature
engineering to extract relevant attributes from the dataset. These attributes include packet payload
data, network traffic patterns, and system resource utilization metrics, among others.
Support Vector Machines (SVM): SVM, renowned for its ability to excel in classification
tasks, forms the core of our intrusion detection approach. We configure SVM models to classify
network traffic and system activities as either benign or malicious based on the engineered
features. However, SVM's performance is highly dependent on the selection of appropriate
hyperparameters, which we address through ACO.
Ant Colony Optimization (ACO): ACO, a nature-inspired optimization algorithm, steps in
to fine-tune SVM's hyperparameters. This intelligent optimization technique explores parameter
configurations and identifies the optimal settings for SVM, enhancing its accuracy and robustness
in detecting intrusions.
INTERNATIONAL JOURNAL OF DATA SCIENCE AND MACHINE LEARNING (ISSN: 2692-5141)
Volume 03, Issue 01, 2023
Published Date: - 04-02-2023 Page no:- 1-5
http://www.academicpublishers.org
P a g e
3 | 5
Evaluation: Rigorous evaluation is a cornerstone of our methodology. We employ a variety
of performance metrics, including detection rates, false positive rates, precision, recall, and the F1-
score, to assess the effectiveness of our intrusion detection system. Extensive cross-validation
ensures the reliability and generalizability of our results.
Comparison: We compare the performance of our SVM-ACO-based intrusion detection
system with other established methods, including traditional SVM, neural networks, and rule-
based systems, to highlight its efficacy in MANET security enhancement.
Real-World Simulations: To validate the real-world applicability of our approach, we
conduct simulations using diverse MANET scenarios and intrusion attack types, considering the
dynamic nature of these networks.
Through this comprehensive methodology, we aim to demonstrate the potential of SVM with
ACO as a formidable tool in the arsenal of MANET security. Our approach not only bolsters the
accuracy of intrusion detection but also optimizes resource utilization, making it a promising
frontier in safeguarding the integrity and availability of data and services in MANETs.
RESULTS
Our research into enhancing security in Mobile Ad Hoc Networks (MANETs) through
intrusion detection with Support Vector Machines (SVM) and Ant Colony Optimization (ACO)
has yielded promising results. The outcomes of our study can be summarized as follows:
Improved Detection Accuracy: The combined approach of SVM with ACO significantly
improved the accuracy of intrusion detection in MANETs when compared to traditional SVM and
other established methods. The fine-tuning of SVM's hyperparameters through ACO led to more
precise classification of network traffic and system activities, reducing false positives and false
negatives.
Optimized Resource Utilization: By enhancing the accuracy of intrusion detection, our
approach minimizes unnecessary resource consumption, making it more efficient for deployment
in resource-constrained MANET environments. This optimization is critical for maintaining
network performance while ensuring robust security.
Robustness Across Scenarios: Our methodology demonstrated robustness across a variety of
MANET scenarios and intrusion attack types, showcasing its adaptability and effectiveness in
dynamic network conditions.
DISCUSSION
The discussion surrounding the results of our research centers on the implications and
significance of our findings:
Enhancing MANET Security: The improved accuracy of intrusion detection achieved
through SVM with ACO has far-reaching implications for MANET security. The ability to detect
INTERNATIONAL JOURNAL OF DATA SCIENCE AND MACHINE LEARNING (ISSN: 2692-5141)
Volume 03, Issue 01, 2023
Published Date: - 04-02-2023 Page no:- 1-5
http://www.academicpublishers.org
P a g e
4 | 5
and respond to malicious activities more effectively enhances the overall security posture of these
self-configuring networks.
Resource-Efficient Security: In MANETs, where resources such as bandwidth and power
are limited, resource-efficient security mechanisms are paramount. Our approach strikes a balance
between security and resource utilization, ensuring that the network remains operational even in
the presence of potential threats.
Adaptability and Generalizability: The robustness of our approach across different MANET
scenarios underscores its adaptability and generalizability. This adaptability is crucial, given the
dynamic and unpredictable nature of MANETs.
CONCLUSION
In conclusion, our study represents a significant advancement in enhancing the security of
Mobile Ad Hoc Networks. By harnessing the power of Support Vector Machines with Ant Colony
Optimization, we have achieved a notable improvement in intrusion detection accuracy while
optimizing resource utilization. This approach not only bolsters the security of MANETs but also
ensures the efficient operation of these networks in various scenarios.
As MANETs continue to gain prominence in applications ranging from military operations
to disaster response, our research contributes to the development of more resilient and secure
communication infrastructures. The fusion of machine learning and optimization techniques in the
realm of intrusion detection holds immense promise for safeguarding the integrity, confidentiality,
and availability of data and services in these dynamic and vital wireless networks.
REFERENCES
1.
J. Jabez and B. Muthukumar, “Intrusion Detection System (IDS): Anomaly
Detection using Outlier Detection Approach”, Procedia Computer Science, Vol. 48, pp. 38- 346,
2015.
2.
Gulshan Kumar, Krishan Kumar and Monika Sachdeva, “The Use of Artificial
Intelligence Based Techniques for Intrusion Detection: A Review”, Artificial Intelligence Review,
Vol. 34, No. 4, pp. 369-387, 2010.
3.
Zahra Bazrafshan, Hashem Hashemi, Seyed Mehdi Hazrati Fard and Ali Hamzeh,
“A Survey on Heuristic Malware Detection Techniques”, Proceedings of 5th International
Conference on Information and Knowledge Technology, pp. 113-120, 2013.
4.
N. Ye, S.M. Emran, Q. Chen and S. Vilbert, “Multivariate Statistical Analysis of
Audit Trials for Host-Based Intrusion Detection”, IEEE Transactions on Computers, Vol. 51, No.
7, pp. 810-820, 2002.
5.
P. Garcia-Teodoro, J. Diaz-Verdejo, G. Macia Fernandez and E. Vazquez,
“Anomaly based Network Intrusion Detection: Techniques, Systems and Challenges”, Computer
and Security, Vol. 28, pp. 18-28, 2009.
6.
C. Kruegel, D. Mutz, W. Robertson and F. Valeur, “Bayesian Event Classification
for Intrusion Detection”, Proceedings of International Conference on Annual Computer Security
Applications, pp. 14-23, 2003.
7.
D.Y. Yeung and Y. Ding, “Host-Based Intrusion Detection using Dynamic and
Static Behavioral Models”, Pattern Recognition, Vol. 36, No. 1, pp. 229-243, 2003.
INTERNATIONAL JOURNAL OF DATA SCIENCE AND MACHINE LEARNING (ISSN: 2692-5141)
Volume 03, Issue 01, 2023
Published Date: - 04-02-2023 Page no:- 1-5
http://www.academicpublishers.org
P a g e
5 | 5
8.
A.M. Cansian, E. Moreira, A. Carvalho and J.M. Bonifacio, “Network Intrusion
Detection using Neural Networks”, Proceedings of International Conference on Computational
Intelligence and Multimedia Applications, pp. 276-280, 1997.
9.
T.R. Srinivasan, R. Shanmugalakshmi and B. Madhusudhanan, “Dynamic Remote
Host Classification in Grid Computing using Clonalg”, Proceedings of International Conference
and Workshop on Emerging Trends in Technology, pp. 198-201, 2010.
