EMPOWERING MANETS WITH ADVANCED MULTIMODAL BIOMETRIC AUTHENTICATION AND ENCRYPTION

International Journal of Network Security & Its Applications (IJNSA) Vol.16, No.2, March 2024
DOI: 10.5121/ijnsa.2024.16202 17
EMPOWERING MANETS WITH ADVANCED
MULTIMODAL BIOMETRIC
AUTHENTICATION AND ENCRYPTION
Alaa M. Elbanaa1
, Mahmoud Y. Shams2,*
, Roayat I. Abdelfatah1
,
Mohamed E. Nasr1
1
Electronics and Electrical Communications Engineering Department, Faculty of
Engineering, Tanta University, Tanta, Egypt
2
Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, 33516,
Egypt
ABSTRACT
In a mobile ad hoc network (MANET), nodes communicate wirelessly, facing unique challenges.
Traditional MANETs suffer from issues like erroneous transmission and vulnerability to unauthorized
nodes joining the network, posing security risks. Authentication within MANETs is a significant security
concern, prompting ongoing research for enhancements. Our solution integrates multimodal biometric
authentication with RSA and AES encryption, providing robust security for user authentication and data
protection in MANETs. This approach effectively addresses risks such as unauthorized access and data
tampering, crucial for secure communication in dynamic, resource-limited MANET environments. Our
proposed system utilizes a combination of face and fingerprint biometrics for encryption, enhancing
network security. Through testing, our system demonstrates a high authentication rate of 92.42% with
minimal processing times: 0.042 ms for key generation, 0.019 ms for encryption, and 0.032 ms for
decryption, based on a 1024-bit key size. These practical results showcase the resilience and efficiency of
our secure system.
KEYWORDS
MANET, Public Cryptography, Face, Fingerprint, Security Key, AES, RSA
1. INTRODUCTION
A Mobile Ad-hoc Network (MANET) is a dynamic arrangement of mobile wireless nodes
interacting without a central authority. Devices within this network must handle all functions
autonomously, including packet routing, security, and Quality of Service (QoS), to combat
threats like black holes, wormholes, and rapid assaults [1]. The decentralized nature of MANETs
poses challenges for key management and secure routing due to constantly shifting topologies
and inherent trust issues among nodes [2], [3]. In our study, we propose a decentralized key
management approach tailored for on-demand routing protocols. We assume MANETs are
segmented into groups, each led by a group leader responsible for key management. This
approach eliminates the need for a Trusted Third Party (TTP) and ensures mutual verification
between new nodes and group leaders before network entry. Our secure routing protocol
authenticates communication parties and intermediary nodes while maintaining message integrity
[4]. The allure of MANETs for military applications has surged alongside advancements in
mobile computing and wireless communications [5]. However, their vulnerability to security
threats—stemming from open channels, node mobility, and lack of centralized security—

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underscores the importance of supporting security-sensitive applications [6], [7]. Continuous user
authentication is vital in high-security MANETs to prevent unauthorized access, particularly in
environments prone to device seizure [8], [9]. User authentication in MANETs typically involves
knowledge factors (e.g., passwords), possession factors (e.g., tokens), and biometric factors.
Biometric technologies offer promising solutions by automating user recognition based on
physiological or behavioural characteristics, reducing reliance on direct user input [10], [11]. Our
proposed Multimodal Biometric Authentication System for MANET Encryption combines
multiple biometric traits—face, and fingerprints—for user authentication [12]. Once
authenticated, the system establishes secure communication channels between nodes using RSA
and AES encryption. RSA facilitates secure key exchange for AES encryption, ensuring
confidentiality and integrity of transmitted data [13].
To address security challenges like the black hole problem, we propose innovative solutions such
as bluff packet generation with virtual destination addresses [14]. Our approach minimizes
network performance impact while effectively detecting black hole attacks. Additionally, digital
signatures can bolster network security against intruder assaults during data transmission. Public-
key cryptography, also known as asymmetric-key cryptography, offers encryption, digital
signatures, and key exchange functionalities. While slower than symmetric-key systems, public-
key cryptosystems excel in providing authentication and key distribution, essential for securing
MANETs [15]. The main contribution of the paper can be listed as follows:
 Integration of multimodal biometric authentication with RSA and AES encryption in
MANETs.
 Utilization of face and fingerprint biometrics for encryption to enhance network security.
 Addressing security challenges such as unauthorized access and data tampering in
MANET environments.
 Demonstrated high authentication rate of 98.24% with minimal processing times in
testing.
 Showcase of system resilience and efficiency in dynamic, resource-limited MANET
scenarios.
Section 2 of the paper provides an overview of related works, while Section 3 details the
proposed methodology. Experimental results and discussions are presented in Section 4, followed
by conclusions and future perspectives in Section 5.
2. RELATED WORK
A Multimodal Biometric Authentication System for MANET (Mobile Ad-Hoc Network)
Encryption based on RSA and AES combines two important aspects of secure communication:
biometric authentication and encryption [16], [17], [18]. Let's break down the key components
and their functionalities: Multimodal Biometric Authentication: Biometric authentication
involves the use of unique physical or behavioral characteristics of individuals to verify their
identity [12], [19]. In MANETs, where traditional authentication methods like passwords or
tokens may not be suitable, multimodal biometrics can provide a robust and reliable
authentication mechanism [20], [21]. Multimodal biometrics typically involve the fusion of
multiple biometric traits, such as fingerprints, iris patterns, voice, face, or gait, to enhance the
accuracy and security of the authentication process [10]. MANET: A Mobile Ad-Hoc Network
(MANET) is a self-configuring network of mobile devices without a centralized infrastructure.
MANETs are characterized by their dynamic topology, limited resources, and vulnerabilities to
various security threats [14], [22]. Implementing secure communication in MANETs is
challenging due to the absence of a trusted central authority and the presence of malicious nodes.
Encryption is the process of converting plaintext (readable data) into ciphertext (unreadable data)

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using cryptographic algorithms [23]. It ensures the confidentiality and integrity of data
transmitted over the network, protecting it from unauthorized access or tampering. In the context
of the Multimodal Biometric Authentication System for MANET, encryption is used to secure
the communication between authenticated nodes. RSA (Rivest-Shamir-Adleman): RSA is a
widely used asymmetric encryption algorithm that leverages the mathematical properties of prime
numbers and modular arithmetic [24]. It involves the generation of a public-private key pair,
where the public key is used for encryption, and the private key is used for decryption. RSA
encryption is computationally intensive, especially for large data sets, but it provides strong
security. AES (Advanced Encryption Standard) is a symmetric encryption algorithm adopted as a
standard by the U.S. government. It uses a symmetric key, meaning the same key is used for both
encryption and decryption [17]. AES is faster than RSA and suitable for encrypting large
amounts of data. It provides a high level of security and is widely used in various applications.
The study presented by Brindha and Meenakshi [25] enhances the ad-hoc on-demand multipath
distance vector (AOMDV) routing protocol in MANETs by integrating heuristic methods and
continuous authentication with biometrics for Sybil attack detection. Specifically, the flower
pollination algorithm (FPA) is proposed to optimize the routing mechanism of AOMDV and
multimodal authentication. Results demonstrate that the FPA-enhanced AOMDV with biometrics
achieves higher Packet Delivery Ratio (PDR) compared to the RSSIAOMDV with biometrics
and the flower pollination AOMDV across various node densities, showing improvements
ranging from 4.72% to 7.19% for different node counts.
The implementation of the system presented by Pravinchandra et al. [26] occurs in two stages.
Firstly, they implemented their system using MATLAB. Secondly, the computational
requirements of the device are evaluated using Java Standard Edition and tested on the iPhone
device simulator. In the initial stage, minutia points are generated and transformed into a
cancelable version. Subsequently, a cryptography key is derived from this cancelable version.
The key generation process takes approximately 0.04 milliseconds. The fingerprint size used is
200x200 pixels. In the subsequent stage, using Java Standard Edition, data encryption and
decryption are performed using the generated key. The Advanced Encryption Standard (AES)
algorithm is employed for this purpose, and the system is tested with various data sizes.
The system presented by Saada et al. [5] utilized a small key size and unimodal fingerprint
contributes to the observed outcome. One notable advantage of our approach lies in the
incorporation of the RSA algorithm, significantly bolstering system security and achieving a
dependable authentication rate of up to 97%. The work presented by Patil et al. [27] addressed
the challenge of secure authentication in the Internet of Things (IoT) communication networks by
introducing a Hybrid and Adaptive Cryptographic (HAC) framework. This framework employs
cryptographic operations, including exclusive-or (Ex-or) operation, a hashing function, and
hybrid encryption, to enhance authentication.
Two approaches to hybrid encryption are explored: one combining Advanced Encryption
Standard (AES) and Elliptic Curve Cryptography (ECC), and the other pairing Rivest Shamir
Adleman (RSA) with AES. This hybrid encryption strategy effectively addresses security
vulnerabilities within the cryptographic system while maintaining high robustness and low
complexity. The proposed HAC-based secure authentication framework achieves impressive
performance metrics, including a minimum communication cost of 0.017 seconds, computation
time of 0.060 seconds, and memory usage of 2.502 MB. Table 1 demonstrates the comparison of
related work in secure routing and authentication Systems.

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Table 1. Comparison of related work in secure authentication.
Reference Area Method Results Distinction
Brindha and
Meenakshi,
2023 [25]
MANETs
routing
FPA for
AOMDV,
multimodal
authentication
Improved PDR
(4.72%-7.19%)
FPA optimizes routing
& multimodal
authentication
Pravinchandra
et al., 2012
[26]
Biometric
authentication
Minutia-based
key generation,
AES encryption
Key
generation:
0.04ms, AES
encryption
Cancelable biometrics
for key generation
Saada et al.,
2018 [5]
Biometric
authentication
Small key size,
unimodal
fingerprint
Low security,
97%
authentication
rate
Our approach uses RSA
for higher security
Patil et al.,
2022 [27]
IoT
authentication
HAC framework
with hybrid
encryption
High security,
low
complexity,
efficient
performance
Hybrid encryption for
stronger security &
efficiency
3. PROPOSED METHOD
The provided block diagram, depicted in Figure 1, outlines the steps of the MANET
authentication system employing face and fingerprint biometrics as keys, alongside RSA and
AES encryption to fortify the authentication process. Initially, fingerprint and facial images
undergo enrollment and pre-processing stages, including region of interest (RoI) identification,
facial feature delineation, and extraction of minutiae and core fingerprint details. Subsequent
steps involve data normalization and extraction of features from the facial and fingerprint images.
Following this, a public key is generated based on the cipher data derived from the M decimal
equivalent pixel value of the fingerprint. These encrypted features are then transmitted to the
MANET source, enhancing the authentication process's security. Upon reception, the transmitted
data is reverted to its original message form and relayed back to the MANET system destination.
The decryption process is elucidated in Figure 2.
A face and fingerprint recognition system operates in several key stages, as illustrated in the
figure. First, a sensor captures an image of the user's face and fingerprint (step 1). This can be
done using a fingerprint sensor, which comes in two main varieties: optical sensors that use light
to capture a ridge and valley image, and capacitive sensors that use an electric field for the same
purpose. Next, the captured images undergo preprocessing (step 2). This stage enhances and
normalizes the face and fingerprint data to improve recognition accuracy. Techniques used here
might involve noise reduction, binarization (converting the image to black and white), and ridge
thinning (for fingerprints) to create a clearer representation.
Once preprocessed, the system extracts unique features from the images (step 3). In facial
recognition, these features could be specific facial components like eyes or nose. For fingerprints,
minutiae points (where ridges end or split) are identified. Finally, the extracted features are
matched against a database of stored templates (step 4). This comparison allows the system to
identify the user.
The figure also shows how this system can be used in a special kind of network, like a mobile ad-
hoc network (MANET), where devices connect directly with each other, without needing a
central hub. Imagine it like a walkie-talkie network, but for all sorts of devices. But because

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security is important, especially for sensitive information like your face and fingerprints, the
system scrambles this data before sending it over the network. Think of it like writing a secret
message in a code before sending it to someone. This scrambling uses a powerful tool called AES
and another called RSA, like extra layers of security. It's important to note that there's a small
mistake in the explanation though. AES is actually like a special key that unlocks and locks the
message, not two separate keys.
Figure 1. The architecture of MANET authentication using face, fingerprint, RSA, and AES.
Figure 2. Decryption process of the encrypted data.

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The transmission process within a Mobile Ad-hoc Network (MANET) involves several crucial
steps, as illustrated in Figure 3. When a node, termed Source (S), intends to transmit data to
another node, Destination (D), through the network path, several security measures are
implemented [28]. Before the data transmission, the face and fingerprint properties of node S are
utilized to secure and encrypt the data using a public key. Upon receipt, node D decrypts the
encrypted data using its private key. This paper introduces hybrid encryption schemes, merging
the strengths of both symmetric and asymmetric encryption techniques to optimize speed and
security. Symmetric encryption, exemplified by Advanced Encryption Standard (AES), employs
a single key for both encryption and decryption, ensuring swift processing. Conversely,
asymmetric encryption, such as RSA, utilizes distinct keys for encryption and decryption,
offering heightened security but slower processing. Hybrid encryption combines these methods,
using asymmetric encryption to securely exchange a symmetric key, followed by symmetric
encryption for actual data encryption and decryption. The article explores the use of AES and
RSA in hybrid encryption, discussing the benefits and challenges associated with this approach.
Figure 3. The process of data transmission within MANET from the source node to the destination node.
RSA, an asymmetric encryption algorithm, relies on the complexity of factoring large prime
numbers. It utilizes a public-private key pair, enabling encryption with the public key and
decryption with the private key. While RSA offers enhanced security and flexibility compared to
symmetric encryption, it is slower and more complex. The hybrid encryption process entails four
key steps: symmetric key generation, data encryption using the symmetric key, asymmetric
encryption of the symmetric key, and transmission of both encrypted data and key. In a MANET
environment, hybrid encryption employing AES and RSA ensures secure communication
between mobile devices. The process involves generating a symmetric key, encrypting data using
AES, encrypting the symmetric key with RSA, and transmitting both encrypted data and key.
Upon receipt, the recipient decrypts the symmetric key using RSA and then decrypts the data
using AES.
This hybrid encryption approach combines the speed of symmetric encryption and the security of
asymmetric encryption, safeguarding MANET communication from unauthorized access. To
further enhance security, a combination of face and fingerprint biometric authentication can be
integrated into the authentication process. During user enrollment, biometric data is captured and
stored securely for future authentication. When authentication is requested, the user's biometric

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data is captured and compared with the enrolled reference data, followed by feature extraction
and biometric matching. Upon successful biometric authentication, the hybrid encryption scheme
utilizing AES and RSA is employed for secure communication within the MANET. This multi-
layered approach ensures robust authentication and encryption, maintaining the confidentiality
and integrity of data transmission within the MANET. The steps of the authentication process are
sown in Algorithm 1.
Algorithm 1. Authentication Procedure for Face and Fingerprint Concatenation with RSA and
AES Encryption.
Step 1: User Enrollment
Step 2: Authentication Request
- User's authentication request is captured.
Step 3: Biometric Authentication - Face and Fingerprint
- User's face is captured using a camera.
- User's fingerprint is captured using a fingerprint scanner.
Step 4: Biometric Feature Extraction
- Unique features are extracted from the captured face.
- Unique features are extracted from the captured fingerprint.
Step 5: Biometric Matching
- Enrolled reference data for the user is retrieved.
- Extracted face features are compared with enrolled reference face features.
- Extracted fingerprint features are compared with enrolled reference fingerprint features.
Step 6: Hybrid Encryption and Authentication
- If both face and fingerprint matches:
- Authentication is successful.
- A symmetric key (AES) is generated for encryption and decryption.
- The symmetric key is encrypted with the recipient's public key (RSA).
- Data is encrypted using the symmetric key (AES).
- Encrypted data and encrypted symmetric key are sent to the recipient.
- If authentication fails:
- Access is denied.
End of Authentication Process
4. EXPERIMENTAL RESULTS AND DISCUSSION
The proposed system's empirical evaluation is conducted using two main datasets: CASIA-
FaceV5 [29] and FVC2000 [30]. The CASIA-FaceV5 dataset comprises 2500 facial images with
considerable intraclass variation, while the FVC2000 datasets contain 880 fingerprints obtained
from 110 different fingers. The samples of the applied datasets are shown in Figure 4. Minutiae
feature extraction techniques are applied to capture the unique characteristics of each enrolled
fingerprint, which are then encoded using the RSA algorithm. System performance is assessed
based on various criteria, with particular emphasis on the time required for key generation,
encryption, and decryption, which are crucial metrics in the MANET context. The grayscale
fingerprint images are segmented into endpoints and bifurcations, extracting key points in
minutiae regions during the feature extraction process. Despite the initial picture's potential low
quality, minutiae-based feature extraction enhances ridge-valley patterns, facilitating more
accurate minutiae extraction. Preprocessing and feature extraction for the concatenated face and
fingerprint data follow the methodology outlined by Shams et al. [19], utilizing the Local
Gradient Pattern (LGP) algorithm. The resulting concatenated feature vector is composed of 1024
bits. We evaluate the system's performance using Receiver Operating Characteristic (ROC)
analysis, which illustrates the relationship between the Genuine Acceptance Rate (GAR) and the

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False Acceptance Rate (FAR), as depicted in Figure 5. This analysis allows us to
comprehensively assess the system's accuracy and effectiveness in distinguishing genuine users
from impostors across varying thresholds. The results were determined by splitting the data into
80% for training and 20% for testing. The obtained Genuine Acceptance Rate (GAR), which
signifies the authentication rate, was found to be 96.77% for the training dataset and 92.42% for
the testing dataset. These figures demonstrate the effectiveness of the system in accurately
authenticating users based on the concatenated face and fingerprint data.
Figure 4. Samples of Face images and Fingerprint images from CASIA-FaceV5 [29] and FVC2000 [30].
Figure 5. The ROC curve for the concatenated Face and Fingerprint.
The results presented in Table 2 highlight the superior speed of the proposed hybrid encryption
algorithm compared to RSA encryption, both in terms of encryption and decryption. This
efficiency is attributed to the use of AES, a symmetric encryption algorithm, in the proposed
method, whereas RSA relies on public-key encryption. Symmetric encryption algorithms
typically outperform public-key encryption counterparts due to their avoidance of resource-
intensive operations like modular exponentiation. Additionally, the table illustrates that the
proposed algorithm surpasses RSA encryption in key generation speed. This advantage arises
from the necessity of generating only one symmetric key for encryption and decryption in the
proposed method, whereas RSA requires the creation of both a public and private key. Notably,
the performance gap between the proposed algorithm and RSA encryption widens with
increasing key sizes, underscoring RSA's greater computational complexity, especially for larger
keys. Consequently, the findings affirm the potential of the proposed hybrid encryption algorithm

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as an alternative offering superior performance to RSA encryption. The substantial performance
disparity across all key sizes suggests that the proposed algorithm holds promise for applications
prioritizing efficiency, such as real-time encryption and embedded systems.
Table 2. Key Size, Key Generation Time, Encryption Time, and Decryption Time for Proposed
Authentication, RSA, and AES in MANET
Key Size 128 192 256 512 1024
Key generation (ms)
RSA 0.055 0.064 0.073 0.088 0.095
AES 0.034 0.037 0.038 0.045 0.049
RSA+AES 0.033 0.036 0.037 0.038 0.042
Encryption (ms)
RSA 0.012 0.017 0.024 0.026 0.031
AES 0.013 0.013 0.014 0.016 0.021
RSA+AES 0.012 0.013 0.013 0.015 0.019
Decryption (ms)
RSA 0.022 0.028 0.032 0.045 0.049
AES 0.02 0.023 0.024 0.04 0.048
RSA+AES 0.016 0.017 0.018 0.022 0.032
Table 3 illustrates the comparative performance of the proposed system against Shanthini et al.
[10], focusing on overhead and security level metrics. Compared to Shanthini et al. [11], our
system demonstrates a smaller key size of 64 and achieves superior delay times. This
improvement is attributed to the utilization of a smaller key size and a unimodal fingerprint in our
approach. A notable advantage of our method is the integration of the RSA algorithm, which
enhances system security with a trustworthy authentication rate of 97% for [5]. Table provides a
visual comparison of our system with references [11] and [5], highlighting key generation,
encryption, and decryption delay times (ms). The efficiency of our approach can be attributed to
the RSA algorithm's utilization of a smaller key size and a straightforward encryption
mechanism, resulting in reduced key generation, encryption, and decryption times.
Table 3. Comparative Analysis of the Proposed Method and Recent MANET Authentication Systems
Methods Shanthini et al. [11] Saada et al. [5] Proposed Method
Key Size 64 128 192 256 64 1024
Key generation
(ms)
0.06 0.13 0.08 0.13 0.06 0.042
Encryption (ms) 0.04 0.1 0.08 0.12 0.02 0.019
Decryption (ms) 0.03 0.1 0.07 0.11 0.03 0.032
5. CONCLUSION AND FUTURE WORK
This paper introduces an innovative approach to securing MANETs by combining facial and
fingerprint recognition as essential encryption keys, supported by RSA and AES algorithms.
MANETs, being decentralized and lacking infrastructure, pose unique security challenges,
particularly in authentication processes. By integrating multimodal biometric authentication with
RSA and AES encryption, our system effectively addresses these concerns. RSA encryption
enables the secure exchange of symmetric keys for AES encryption, ensuring data confidentiality
and integrity, thereby mitigating risks like unauthorized access and data tampering. Our system

26
achieves a high Genuine Acceptance Rate (GAR) of 96.77% for the training dataset and 92.42%
for the testing dataset, indicating strong authentication performance. With key sizes of 1024 bits
and minimal processing times (0.042 ms for key generation, 0.019 ms for encryption, and 0.032
ms for decryption), our system offers efficient encryption and decryption capabilities. Moving
forward, there are several avenues for enhancing our system. Integrating additional biometric
modalities, such as iris patterns, voice, or gait, could further improve accuracy and security.
Optimizing the computational overhead of RSA encryption, especially for large datasets,
warrants investigation to enhance performance. Further analysis is also needed to ensure the
system's resilience against security attacks like impersonation or replay attacks. Additionally,
evaluating scalability as MANET node numbers increase will provide insights into performance
under varying network conditions. Our proposed system has undergone rigorous testing,
demonstrating resilience and effectiveness in securing MANET communications. Continued
research and development efforts will contribute to enhancing MANET security, enabling their
deployment across diverse applications requiring secure and reliable communication.
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