Wireless Communication in Cyber Security E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
WIRELESS COMMUNICATION in CYBERSECURITY Presenting the concepts and advances of wireless communication in cybersecurity, this volume, written and edited by a global team of experts, also goes into the practical applications for the engineer, student, and other industry professionals. Rapid advancement in wireless communications and related technologies has led to the use of newer technologies like 6G, Internet of Things (IoT), Radar, and others. Not only are the technologies expanding, but the impact of wireless communication is also changing, becoming an inevitable part of daily life. With increased use comes great responsibilities and challenges for any newer technology. The growing risks in the direction of security, authentication, and encryption are some major areas of concern, together with user privacy and security. We have seen significant development in blockchain technology along with development in a wireless network that has proved extremely useful in solving various security issues. Quite efficient secure cyber-physical systems can be constructed using these technologies. This comprehensive new volume covers the many methods and technologies used in intrusion detection in wireless networks. This book allows readers to reach their solutions using various predictive algorithm-based approaches and some curated real-time protective examples that are defined herein. Artificial intelligence (AI) concepts are devised and proposed for helping readers understand the core concepts of efficiencies of threats, and the parallel solutions are covered. The chapters also state the challenges in privacy and security levels for various algorithms and various techniques and tools are proposed for each challenge. It focuses on providing exposure to readers about data security and privacy for wider domains. The editorial and author team aims to address all possible solutions to the various problems faced in the newer techniques of wireless communications, improving the accuracies and reliability over the possible vulnerabilities and security threats to wireless communications. It is a must have for any engineer, scientist, or other industry professional working in this area.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 BBUCAF: A Biometric-Based User Clustering Authentication Framework in Wireless Sensor Network
1.1 Introduction to Wireless Sensor Network
1.2 Background Study
1.3 A Biometric-Based User Clustering Authentication Framework
1.4 Experimental Analysis
1.5 Conclusion
References
2 DeepNet: Dynamic Detection of Malwares Using Deep Learning Techniques
2.1 Introduction
2.2 Literature Survey
2.3 Malware Datasets
2.4 Deep Learning Architecture
2.5 Proposed System
2.6 Result and Analysis
2.7 Conclusion & Future Work
References
3 State of Art of Security and Risk in Wireless Environment Along with Healthcare Case Study
3.1 Introduction
3.2 Literature Survey
3.3 Applications of Wireless Networks
3.4 Types of Attacks
3.5 Active Attacks
3.6 Layered Attacks in WSN
3.7 Security Models
3.8 Case Study: Healthcare
3.9 Minimize the Risks in a Wireless Environment
3.10 Conclusion
References
4 Machine Learning-Based Malicious Threat Detection and Security Analysis on Software-Defined Networking for Industry 4.0
4.1 Introduction
4.2 Related Works
4.3 Proposed Work for Threat Detection and Security Analysis
4.4 Implementation and Results
4.5 Conclusion
References
5 Privacy Enhancement for Wireless Sensor Networks and the Internet of Things Based on Cryptological Techniques
5.1 Introduction
5.2 System Architecture
5.3 Literature Review
5.4 Proposed Methodology
5.5 Results and Discussion
5.6 Analysis of Various Security and Assaults
5.7 Conclusion
References
6 Security and Confidentiality Concerns in Blockchain Technology: A Review
6.1 Introduction
6.2 Blockchain Technology
6.3 Blockchain Revolution Drivers
6.4 Blockchain Classification
6.5 Blockchain Components and Operation
6.6 Blockchain Technology Applications
6.7 Difficulties
6.8 Conclusion
References
7 Explainable Artificial Intelligence for Cybersecurity
7.1 Introduction
7.2 Cyberattacks
7.3 XAI and Its Categorization
7.4 XAI Framework
7.5 Applications of XAI in Cybersecurity
7.6 Challenges of XAI Applications in Cybersecurity
7.7 Future Research Directions
7.8 Conclusion
References
8 AI-Enabled Threat Detection and Security Analysis
8.1 Introduction
8.2 Literature Survey
8.3 Proposed Work
8.4 System Evaluation
8.5 Conclusion
References
9 Security Risks and Its Preservation Mechanism Using Dynamic Trusted Scheme
9.1 Introduction
9.2 Related Work
9.3 Proposed Framework
9.4 Performance Analysis
9.5 Results Discussion
9.6 Empirical Analysis
9.7 Conclusion
References
10 6G Systems in Secure Data Transmission
10.1 Introduction
10.2 Evolution of 6G
10.3 Functionality
10.4 6G Security Architectural Requirements
10.5 Future Enhancements
10.6 Summary
References
11 A Trust-Based Information Forwarding Mechanism for IoT Systems
11.1 Introduction
11.2 Related Works
11.3 Estimated Trusted Model
11.4 Blockchain Network
11.5 Performance Analysis
11.6 Results Discussion
11.7 Empirical Analysis
11.8 Conclusion
References
About the Editors
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Datasets used in the proposed system.
Table 2.2 Examination of ML-based techniques with the proposed DeepNet model...
Table 2.3 Examination of DL-based techniques with the proposed DeepNet model...
Table 2.4 Examination of ML and DL models with the proposed DeepNet model fo...
Table 2.5 Examination of DL-based techniques with the proposed DeepNet model...
Table 2.6 Examination of existing works with the proposed DeepNet model for ...
Chapter 3
Table 3.1 Previous studies of security risks in wireless environment.
Table 3.2 Security attacks in OSI protocol layers.
Chapter 4
Table 4.1 Relative study of connected works.
Table 4.2 Dataset description.
Table 4.3 Performance of attack detection for synthetic data set.
Table 4.4 Performance of attack detection for KDD data set.
Chapter 5
Table 5.1 Result analysis of the proposed technique.
Table 5.2 Comparison of existing and proposed Diffie-Hellman algorithm.
Chapter 6
Table 6.1 Time to encrypt (in milliseconds) for file sizes in character.
Chapter 8
Table 8.1 The different textual content features performance on D1 dataset w...
Table 8.2 Performance of the proposed AO-RNN hyperlink features on D1 with d...
Table 8.3 Various feature combinations on dataset D1.
Table 8.4 Comparison of AO-RNN vs. Other Standard Approaches (for Dataset 1...
Chapter 9
Table 9.1 Literature survey.
Chapter 11
Table 11.1 Literature survey.
List of Illustrations
Chapter 1
Figure 1.1 Architecture of BBUCAF.
Figure 1.2 Different stages to getting the encrypted data.
Figure 1.3 The authentication stage and secret key generation for each clust...
Figure 1.4 The clustering stage and keying process to protect from different...
Figure 1.5 Latency of BBUCAF.
Figure 1.6 Computational time of BBUCAF.
Figure 1.7 Energy usage of BBUCAF.
Figure 1.8 Delay rate of BBUCAF.
Figure 1.9 Attack detection rate of BBUCAF.
Figure 1.10 Computational time BBUCAF.
Chapter 2
Figure 2.1 Architecture of DNN.
Figure 2.2 Architecture of CNN.
Figure 2.3 Architecture of RNN.
Figure 2.4 Overall design & flow of the proposed DeepNet model.
Figure 2.5 Architecture of the proposed DeepNet methodology.
Figure 2.6 Structure of DBN model.
Figure 2.7 Structure of SAE model.
Figure 2.8 Comparison of ML-based techniques with the proposed DeepNet model...
Figure 2.9 Comparison of DL-based techniques with the proposed DeepNet model...
Figure 2.10 Comparison of ML & DL-based techniques with the proposed DeepNet...
Figure 2.11 Confusion matrix for the Malimg dataset.
Figure 2.12 Confusion matrix for the BIG 2015 dataset.
Figure 2.13 Confusion matrix for the MaleVis dataset.
Chapter 3
Figure 3.1 Wireless environment.
Figure 3.2 Applications of wireless networks.
Figure 3.3 Types of passive attacks.
Figure 3.4 Types of active attacks.
Figure 3.5 Application of wireless network towards healthcare.
Figure 3.6 Ransomware attack in healthcare.
Figure 3.7 Steps to do after ransomware attack in wireless networks.
Figure 3.8 Steps to avoid risks in wireless environment.
Chapter 4
Figure 4.1 Operation of denial of service.
Figure 4.2 Working model - distributed denial of service.
Figure 4.3 Proposed work.
Figure 4.4 Feature selection.
Figure 4.5 Before mitigation.
Figure 4.6 After mitigation.
Figure 4.7 Time bins for normal traffic processing.
Figure 4.8 Time bins for mitigation process.
Figure 4.9 CPU usage on attack detection.
Chapter 5
Figure 5.1 System architecture.
Figure 5.2 Proposed encryption and decryption process.
Figure 5.3 Encryption and decryption approach.
Figure 5.4 Computational time for the input data.
Figure 5.5 Key generation time for the input data.
Figure 5.6 Encryption time for the input data.
Figure 5.7 Decryption time for the input data.
Chapter 6
Figure 6.1 Blockchain development stages.
Figure 6.2 Blockchain elements.
Figure 6.3 Types of blockchain.
Figure 6.4 Blockchain type pattern representation.
Figure 6.5 Blockchain structure.
Figure 6.6 Hashing time in ms vs. file size in character.
Chapter 7
Figure 7.1 Various forms of cyberattack.
Figure 7.2 Relation between the terms of XAI.
Figure 7.3 XAI categorizations.
Figure 7.4 XAI framework.
Figure 7.5 Evolution of SHAP models.
Figure 7.6 Skater algorithms.
Figure 7.7 XAI in cybersecurity applications.
Chapter 8
Figure 8.1 Phishing attack.
Figure 8.2 Phishing attack survey from 2021-2022.
Figure 8.3 Optimizer workflow.
Figure 8.4 Flowchart of the proposed work.
Figure 8.5 Architecture of RNN.
Figure 8.6 Performance of RNN with other approaches.
Figure 8.7 Performance of RNN with other approaches.
Chapter 9
Figure 9.1 A secured smart society framework having intelligent devices in t...
Figure 9.2 Trust-based mechanism in IoT devices [27].
Figure 9.3 Flowchart of proposed mechanism [26].
Figure 9.4 Alteration rate.
Figure 9.5 Accuracy rate.
Figure 9.6 Delay.
Chapter 10
Figure 10.1 Physical layer in artificial intelligence [2].
Figure 10.2 Network architecture for better security [2].
Chapter 11
Figure 11.1 A hybrid IIoT architecture [9].
Figure 11.2 The architecture of trust-based and blockchain network [26].
Figure 11.3 A hybrid IIoT architecture [26].
Figure 11.4 Flowchart of proposed model.
Figure 11.5 Accuracy.
Figure 11.6 Data alteration rate.
Figure 11.7 Blockchain of legitimate devices.
Guide
Cover Page
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Begin Reading
About the Editors
Index
Also of Interest
Wiley End User License Agreement
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Advances in Antenna, Microwave, and Communication Engineering
Series Editor: Manoj Gupta, PhD, Pradeep Kumar, PhD
Scope: Antenna and microwave, as well as digital communication, engineering has been increasingly adopted in many diverse applications such as radio astronomy, long-distance communications, space navigation, radar systems, medical equipment’s as well as missile electronic systems. As a result of the accelerating rate of growth of communication, microwave and antenna technology in research and industry sectors; students, teachers and practicing engineers in these area are faced with the need to understand various theoretical and experimental aspects of design and analysis of microwave circuits, antennas and simulation techniques, communication systems as well as their applications. Antennas, Microwave and Communication Engineering are actually a very lively and multidisciplinary one, mixing the deepest electromagnetic theoretical aspects, mathematical signal and data processing methods, physics of devices and physics of fields, software developments and technological fabrication aspects, and the large number of possible applications generates multiple outcomes. Hence the aim of this book series is to provide a multi-discipline forum for engineers and scientists, students, researchers, industry professionals in the fields of Antenna, Microwave, Communication and Electromagnetic Engineering to focus on advances and applications.
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Wireless Communication for Cybersecurity
Edited by
S. SountharrajanR. MaheswarGeetanjali Rathee
and
M. Akila
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 9781119910435
Front cover images supplied by Pixabay.comCover design by Russell Richardson
Preface
Wireless communication has become essential for everyday life all over the world, in almost every country. Irrespective of place or situation, people depend on wireless communication to fulfil their necessities. It is nearly impossible to remember a world before wireless communication became a critical entity in billions of lives. Rapid advancement in wireless communications and related technologies has led to advances in this domain, which is the use of newer technologies like 6G, IoT, radar, etc. Not only are these technologies expanding, but the impact of wireless communication is also changing and becoming an inevitable part of our lives.
With use comes responsibility with a lot of disadvantages for any newer technology. The growing risks in terms of security, authentications, user privacy, and encryptions are some major areas of concern. We have seen significant development in blockchain technology along with development in a wireless network that has proved extremely useful in solving many security issues. An efficient secure cyber-physical system can be constructed using these technologies. This book covers all kinds of situations regarding the digital health and processes of intrusion detection in wireless networks. It allows the readers to reach their solutions using various predictive algorithm-based approaches and some curated real-time protective examples that are defined. The chapters also comprehensively state the challenges in privacy and security levels for various algorithms and various techniques and tools are proposed for each challenge.
It focuses on exposing readers to advances in data security and privacy of wider domains. Security vulnerabilities are overcome using the techniques as proposed in the chapters. The book aims to address all viable solutions to the various problems faced in the newer techniques of wireless communications, improving the accuracies and reliability over the possible vulnerabilities and security threats to wireless communications. This book is useful for the researchers, academicians, R&D organizations, and healthcare professionals working in the area of antenna, 5G/6G communication, wireless communication, digital hospital, and intelligent medicine.
The key features of the book are:
Serves as a strong technological convergence solution for wireless communications in the cyber security domain
Enlightens the foundation of wireless communication networks embedding with cyber-physical systems and foundational topics of blockchain
Exploring the practical issues in the automation domain
Highlights the AI powered analytics to analyse the characteristics of wireless user behaviour security models
Key insights about blockchain joining forces with wireless communication security to set up flawless cyber-physical systems
Dr. S. SountharrajanDr. R. MaheswarDr. Geetanjali RatheeDr. M. AkilaSeptember 2023
1BBUCAF: A Biometric-Based User Clustering Authentication Framework in Wireless Sensor Network
Rinesh, S.1*, Thamaraiselvi, K.2, Mahdi Ismael Omar1 and Abdulfetah Abdulahi Ahmed1
1 Department of Computer Science, Jigjiga University, Jijiga, Ethiopia
2 Department of Computer Science, Malla Reddy College of Engineering, Hyderabad, Telangana, India
Abstract
Wireless Sensor Networks (WSN) have made much progress in the last few years, so data transmission must be more secure. Cryptographic keys keep information private, authenticate people, and keep data safe. Several research projects were done to interact with important management issues in WSNs. Prime statistics are used to make collective keys. It would then be able to accurately check the security of nodes. A new network way is modeled for sending data between nodes without restriction. A strong authentication system is needed to maintain network safety and allow people to use a network service freely. But the limited supplies of sensor nodes make it tough to authenticate people. To overcome the security-based issues, a biometric-based user clustering authentication framework (BBUCAF) has been introduced to increase the level of security and the network’s speed among the nodes. A biometric-based model is created by taking features from the fingerprint. Securely, feature vectors create a private key for the user. Such a key is sent to every sensor node. Then, private keys between sensor nodes are made by combining a randomly generated count and the user’s key, which is sent to each sensor node. C- means Clustering is used to group nodes based on their range and unique identification. A collective key is made here using a fuzzy registration component that considers prime numbers. Fuzzy membership and biometric-based secret keys send data between groups and sensor nodes. Each cluster has group keys that differ from one cluster to the next. The network’s speed improves the network’s effectiveness by cutting down on network traffic, protecting against DoS attacks, and extending the battery capacity of a node’s battery with less energy usage.
Keywords: Wireless sensor network, nodes, clustering, network traffic, authentication
1.1 Introduction to Wireless Sensor Network
Several sensors can be used together in a single WSN. Nodes in the network that sense their surroundings are known as sensor nodes [1]. A wide range of applications, such as structural health monitoring, environmental control, and combat observation, can benefit from such connections [2]. A node can perform computing, identify itself, and communicate with other devices [3]. Those nodes can be dispersed in a situation where they can identify each other and work together to accomplish the task in a large region [4, 5]. Sensor nodes in WSNs are used for specific tasks [6]. Small sensor nodes in the network model their surroundings’ information after spotting it [7]. Due to their wide range of applications, WSNs are becoming increasingly popular in education and the market [8]. WSNs are primarily designed to gather and send environmental information to a home or remote location via a network of sensing devices located in an isolated community [9]. The original data are then processed online or offline as per application standards for a full evaluation in a remote location [10]. If a patient is not in the hospital, for example, remote patient tracking is important for doctors.
These systems can benefit from numerous applications, including structural health monitoring, environmental control, and combat monitoring [11]. Most apps allow users to obtain data immediately from a gateway node because queries are handled on this node in most cases [12]. The information from a gateway node is very hard to receive on rare occasions. Therefore, sensor nodes collect information directly [13]. By sending the request to a sensor node, unauthorized users can quickly obtain sensitive information [14]. As a result of sensor nodes’ inability to verify query messages may leak sensitive data, and network resources, such as node power and bandwidth, could be wastefully depleted [15]. Any or all of the associated issues could impact the network’s lifespan and effectiveness, making the system inaccessible to genuine people [16]. Since network data and resources can be illegally accessed, authentication is necessary [17]. To achieve this, sensor nodes must validate users’ identities [18]. All of the following issues can be solved with user authentication, which enables authorized users to join a system [19]. As a result of the resource limits of WSNs’ small sensor devices, namely their power and storage, along with their processing and transmission capabilities, providing authentication in these networks is a very difficult issue [20]. Even though several standards have been presented, the authentication procedure is still vulnerable. In the end, a more robust and intelligent process is needed to assure the security of a WSN [21]. Maintaining a safe network requires a robust authentication system that allows users to access network services without restriction. Authentication is difficult due to the restricted supply of sensor nodes [22, 23]. To overcome all the above-mentioned security-based issues, BBUCAF has been developed. The main contribution of BBUCAF is
To build a biometric model, enhance the network’s security and performance using fingerprints’ unique characteristics.
The user’s private key is generated securely using feature vectors. Every sensor node receives a key. Then, each sensor node receives a random count, and the user’s private key is combined with each sensor node.
Numerous benefits of a faster network include reducing network traffic, preventing denial-of-service (DDoS) assaults, and increasing node battery life.
1.2 Background Study
Many researchers have carried out research works. Tsu-Yang Wu et al. [24] developed Three-Factor Authentication Protocol (TAP), in which the logical study and informal analysis confirm safety, Burross-Abadii-Needham (BAN) logic, and ProVerif tools. The evaluation of security and performance reveals that the method offers stronger security and reduced computational burden.
P.P. Devi et al. [25] proposed SDN-Enabled Hybrid Clone Node Detection Mechanisms (SDN-HCN). An SDN-based methodology performs a network path evaluation and time-based research methodologies to identify and reduce duplicate nodes produced by cloning attacks. To identify clone nodes in a wireless network, one must use the HCN technique. The simulation results reveal that several metrics are analyzed in the experiment.
M. Rakesh Kumar et al. [26] introduced a Secure Fuzzy Extractor-based Biometric Key Authentication (SFE-BKA) Scheme. The hash function is critical to the system’s security. In SFE-BKA, the hash parameter value is irrespective of hash functions in an attempt to improve information security. The proposed method is not affected by this variance in hashing in terms of latency or delay. The outcome of SFE-BKA yielded 40% less data loss, 20% less energy usage, and less latency than earlier encoding systems.
S. Ashraf et al. [27] developed a Depuration-based Efficient Coverage Mechanism (DECM). Two rounds of deployment are required to complete the process. When a node is to be moved to new locations, the Dissimilitude Enhancement Scheme (DES) is used to find it. The Depuration mechanism in the second cycle reduces the separation between prior and new places by controlling the needless migration of the sensor nodes. By analyzing the simulation findings and computing in 0.016 seconds, the DECM has attained more than 98% protection.
Fan Wu et al. [28] described the Authentication Protocol for Wireless Sensor Networks (AP-WSN). Proverif ’s formal verification shows that the new system retains its security features. AP-WSN is feasible and meets general demands in a way that counters various threats and meets security properties. The proposed approach outperforms previous schemes in terms of security and is suitable for use. The simulation findings indicate that the plan may be successfully implemented in an IoT system and have a practical use.
Diksha Rangwani et al. [29] discussed improved privacy-preserving remote user authentication (PP-RUA). The suggested system is formally analyzed using the probabilistic Random-Oracle-Model to show the resilience of the scheme. Further, the system is simulated using a well-accepted AVISPA tool to show its security strength. The performance assessment of the system demonstrates that along with its consistency in aspects of privacy, the suggested scheme is more effective in computing and networking overheads than other current schemes.
SungJin Yu et al. [30] discussed Secure and Lightweight Three-Factor-Based User Authentication (SLUA). Secure, untraceable, and mutually authenticated communications are possible with the SLUA. Informal and formal methods are used to assess the safety of SLUA, along with the logic of Burrows–Abadi–Needham (BAN), the Real-or-Random (ROR) model, and the AVISPA simulation. SLUA-performance of WSNs is compared to other existing systems. Security and efficiency are more protected and more efficient in the proposed SLUA than in the prior suggested technique.
More problems are associated with security-based problems in sensor networks, and such security issues are concentrated on the proposed BBUCAF, and the obtained experimental analysis is compared with [25], [26], [27].
1.3 A Biometric-Based User Clustering Authentication Framework
1.4 Experimental Analysis
Modeling a network with many sensors is used to evaluate the system’s privacy and efficiency. To measure the performance of various methods, a few sensor networks to overcome the attacks are used with high-speed performance with less traffic. The network’s performance is increased using less traffic among the networks and protection against attacks; the evaluation of the network in terms of traffic is measured in the form of latency. The performance of traffic among the network is shown in Figure 1.5.
Computational calculations show that the network’s activity is implemented in this simulation scenario. For example, parameters such as the percentage of assaults detected and the time it takes to respond to well-known sensor network attacks can be calculated.
Figure 1.5 Latency of BBUCAF.
Figure 1.6 Computational time of BBUCAF.
Figure 1.6 shows the outcomes of an analysis of various strategies concerning the operating duration of the system under different node counts. The suggested BBUCAF system takes less time to execute than alternative approaches. Biometric key authentication is used to secure more communication, resulting in a running time of a few milliseconds for different nodes.
Eu is used to calculating the total energy usage of the network as shown below.
The total energy consumption is obtained from Equation (1.7), shown in Figure 1.7; here, l represents the sensor networks that help send m messages between nodes. As a result, the efficiency of energy use is directly related to the amount of time spent concentrating, the size of the packet sent, and the amount of time spent decrypting and encrypting data.
With the delay, one may determine the typical end-to-end lag experienced by data packets as they travel across networks. The term “end-to-end delay” refers to the average amount of time it takes for a packet delivered from a resource to reach its target. Trails are used to calculate delay, as shown in Figure 1.8.
Figure 1.7 Energy usage of BBUCAF.
Figure 1.8 Delay rate of BBUCAF.
The delay for the entire network is obtained from Equation (1.8); here l represents different sensor networks, ml represent the total number of packets collected.
Figure 1.9 compares the outcomes of different strategies for attack detection based on the number of nodes. Compared to previous approaches, the attack detection rate of the proposed BBUCAF system is more advanced.
Figure 1.9 Attack detection rate of BBUCAF.
Figure 1.10 Computational time BBUCAF.
A biometric key authentication method is used to secure more interaction since the projected system has a lower attack detection performance than the present system.
To test the effectiveness of different methods, the following notations are used for the multiplication time with the hash function and completion time. The fuzzy extractor and the computation time relate to multiplying rates in the algebraic graph, and the hash function with the cost of a unique action may be disregarded bitwise. The BBUCAF uses the clustering node to decrease the disparity between the computational loads of the center and the nodes and can enhance the efficiency of the performance. The computational time of BBUCAF is shown in Figure 1.10. As a result, just the expenses of calculating irregular multiplier curve operations and a hash function should be considered for detecting the computational cost.
1.5 Conclusion
To deal with the security-related problems, Network security and speed have been improved with the introduction of a BBUCAF. The fingerprint is used to develop a Biometric-based model. The user’s private key is generated securely using feature vectors. Sensor nodes get a key in the form of an encrypted message. The user’s key is given to each sensor node, and a random number is used to create private keys between them. A fuzzy registration component that considers prime integers is used to create a collective key. Based on biometrics, data is sent between groups and sensor nodes using fuzzy membership and secret keys. The set of group keys used by each cluster is distinct from the sets used by other clusters. To increase the network’s effectiveness, the network’s speed must be increased to reduce network traffic with less energy usage and guard against DoS attacks.
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