Security Enhancement in MANETs Using Fuzzy-Based Trust Computation Against Black Hole Attacks

Tokekar, Vrinda; Shrivastava, Shailendra Kumar

doi:10.1007/978-981-10-5508-9_4

Cited by 27 publications

(18 citation statements)

References 12 publications

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“…In adhoc networks, Blackhole attack detection was performed by fuzzy based trust value computation [15]. Trust value of each node was computed in fuzzy approach based on following metrics: packet delivery ratio, percentage of energy exhaustion, percentage of buffer utilization, and number of connection requests given by a node.…”

Section: Survey On Attack Detection Methodsmentioning

confidence: 99%

Secure Group based Routing and Flawless Trust Formulation in MANET using Unsupervised Machine Learning Approach for IoT Applications

Ponguwala¹,

Rao²

2019

EAI Endorsed Transactions on Energy Web

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INTRODUCTION: Mobile Adhoc Network (MANET) is integrated with Internet of Things (IoT) in many application cases due to its flexibility and scalability. The dynamic nature of MANET introduces some security threats in IoT environment. In those threats, Blackhole attack and Grayhole attack are severe routing attacks that disrupts routing algorithm to crack transmission in the entire network. OBJECTIVES: Many security mechanisms are introduced in MANET based on trust computation schemes. However, computation of inaccurate trust value degrades the performance of mitigation schemes. Thus the major objective of this work is to design a novel security mechanism to protect the MANET-IoT from different adversaries. METHODS: in this paper we propose a novel group based routing algorithm with recommendation filtering supported by security monitors (SMs). Unsupervised machine learning algorithm is adapted for the purpose of recommendation filtering in the network. Initially the entire network is grouped by Secure Certificate based Group Formation (SCGF) algorithm. In each group, Recommendation Filtering by K-means algorithm (RF-K means) algorithm is employed to perform trust computation. For secure and optimal route selection, hybrid optimization algorithm that combines Genetic Algorithm and Fire Fly Algorithm (GA-FFA) is proposed. Data transmission is protected by novel Hash Message Authentication Code with Advanced Encryption Standard (HMAC-AES) algorithm in which hash function is integrated with cryptography function. RESULTS: The proposed work is validated in network simulator-3 environment and the obtained results show better performance in terms of packet delivery ratio (96.3%), throughput (135kbps), delay (3.26ms), detection rate (99%), and energy consumption (8.5%). CONCLUSION: The MANET-IoT network is secured by group formation and trust filtering approaches. Further, involvement of cryptography function ensures data security whereas hash function ensures data integrity..

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Section: Survey On Attack Detection Methodsmentioning

confidence: 99%

Secure Group based Routing and Flawless Trust Formulation in MANET using Unsupervised Machine Learning Approach for IoT Applications

Ponguwala¹,

Rao²

2019

EAI Endorsed Transactions on Energy Web

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“…In the QoS parameters some variations have been occurred that can be showed the performance of network, mainly decreased in the presence of a black hole attack. Jain et al [11] have explored a network using fuzzy based model. The model has been utilized for the detection of black hole attack through consuming AODV along with Fuzzy based approach.…”

Section: Detection and Prevention Of Manet Using Hybrid Svm With Annmentioning

confidence: 99%

Detection and Prevention of Manet using Hybrid SVM with Ann

Walia,

Malhotra

2019

IJEAT

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Mobile Ad hoc Networks (MANET) have been exceptionally vulnerable against attacks because of the dynamic and self-configurable nature of its system foundation. This kind of wireless network is appropriate for temporary communication linked due to its nature of less-foundation and there is no any control of centralized manner. Design a routing mechanism that are security aware with higher QoS parameter is very competetive and the major tasks involved in ad hoc types of network as per the limited power resources and their dynamic routing topology. This paper mainly focused on the design of a secure and trusts based on-demand routing mechanism using Ad-hoc on demand distance vector (AODV) protocol to compute trust-based produces path initialed from source up to destination that will fulfill minimum two end-to-end QoS parameters of network. So here, the generalized AODV routing protocol has been extended from traditional routing mechanism to analyze the performance of this model with combination of artificial intelligence concept. The proposed ad hoc based routing mechanism is used to found possible routes that are prevented through trust adjacent position of security validation protocols and enhanced link optimized route computes on the basis of Artificial Neural Network (ANN) as an artificial intelligence algorithm for well-organized communication in MANET. In addition, this research demonstrates the effectiveness of bio inspired Firefly Algorithm (FFA) as an optimization approach with the consideration of several performance QoS metrics of network. The results have been measured in terms of throughput and PDR with SVM and ANN approach. It has been observed that the throughput and PDR measured using ANN approach is better compared to SVM approach an average of 0.755 PDR value has been obtained using ANN approach.

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“…Each target can be reflected by some measured value, such as security, which refers to the prevention of unauthorized access to a system. Unauthorized access can be detected according to the number of illegal connections and scanning of important ports [42][43][44][45][46]. Reliability refers to the provision of a trusted service for a given duration and can be evaluated by average response time and average task success ratio.…”

Section: Introductionmentioning

confidence: 99%

Review of the D2D Trusted Cooperative Mechanism in Mobile Edge Computing

Yuan

Kang

et al. 2019

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Mobile edge computing (MEC) effectively integrates wireless network and Internet technologies and adds computing, storage, and processing functions to the edge of cellular networks. This new network architecture model can deliver services directly from the cloud to the very edge of the network while providing the best efficiency in mobile networks. However, due to the dynamic, open, and collaborative nature of MEC network environments, network security issues have become increasingly complex. Devices cannot easily ensure obtaining satisfactory and safe services because of the numerous, dynamic, and collaborative character of MEC devices and the lack of trust between devices. The trusted cooperative mechanism can help solve this problem. In this paper, we analyze the MEC network structure and device-to-device (D2D) trusted cooperative mechanism and their challenging issues and then discuss and compare different ways to establish the D2D trusted cooperative relationship in MEC, such as social trust, reputation, authentication techniques, and intrusion detection. All these ways focus on enhancing the efficiency, stability, and security of MEC services in presenting trustworthy services.

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Security Enhancement in MANETs Using Fuzzy-Based Trust Computation Against Black Hole Attacks

Cited by 27 publications

References 12 publications

Secure Group based Routing and Flawless Trust Formulation in MANET using Unsupervised Machine Learning Approach for IoT Applications

Secure Group based Routing and Flawless Trust Formulation in MANET using Unsupervised Machine Learning Approach for IoT Applications

Detection and Prevention of Manet using Hybrid SVM with Ann

Review of the D2D Trusted Cooperative Mechanism in Mobile Edge Computing

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