A secured MANET using position‐based opportunistic routing and SEMI MARKOV process

Maragatharajan, M.; Subramanian, C. Bala; Balakannan, S. P.

doi:10.1002/cpe.5047

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…The scheme determines the effective path based on energy level but the involvement of attacks is not contemplated, which results in loss of data packets. Proceeding more, Maragatharajan et al [6] recommend a Position-based opportunistic (POR) and greedy routing scheme for reliable data delivery. The work adopts a dual-step process to find the best routing path.…”

Section: Related Workmentioning

confidence: 99%

Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

Joshi

Sharma

2022

Wireless Pers Commun

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The exposure of IoT nodes to the internet makes them vulnerable to malicious attacks and failures. These failures affect the survivability, integrity, and connectivity of the network. Thus the detection and elimination of attacks in a timely manner become an important factor to maintain the network connectivity. Trust-based techniques are used in understanding the behavior of nodes in the network. Several researchers have proposed conventional trust models that are power-hungry and demand large storage space. Succeeding this Hidden Markov Models have also been developed to calculate trust but the survivability of network achieved from them is low. To improve the survivability selfish and malicious nodes present in the network are required to be treated separately. Hence, an improved Hidden Markov Trust (HMT) Model is developed in this paper which accurately detects the selfish and malicious nodes that illegally intercept the network. An algorithm is generalized for learning the behavior of nodes using the HMT model with the expected output. The evaluated node's likelihood functions differentiate the selfish node from the malicious node and provide independent timely treatment to both types of nodes. Further, comparative analysis for attacks such as black-hole, grey-hole, and sink-hole has been done and performance parameters have been extended to survivability-rate, power-consumption, delay, and false-alarm-rate, for different networks sizes and vulnerability. Simulation result provides a 10% higher PDR, 29% lower overhead, and 15% higher detection rate when compared to FUCEM, FTCSPM, and OADM trust models presented in the literature.

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Section: Related Workmentioning

confidence: 99%

Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

Joshi

Sharma

2022

Wireless Pers Commun

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“…Data sharing suffers from attacks especially due to having self‐organizing environment of the network infrastructure. Among these attacks, routing attacks have well‐known central focus although they might cause the most destructive damage in MANET . In Reference , encryption is applied on the data by using the symmetric cryptographic technique, which uses same key for encryption and decryption.…”

Section: Introductionmentioning

confidence: 99%

Secure and efficient data transfer using spreading and assimilation in MANET

et al. 2020

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Mobile ad hoc Network (MANET) is a cluster of moveable devices connected through a wireless medium to design network with rapidly changing topologies due to mobility. MANETs are applicable in variety of innovative application scenarios where smart devices exchange data among each other. In this case, security of data is the major concern to provide dependable solution to users. This article presents a secure mechanism for data transfer where sender splits the data into fragments and receiver gets the actual data by assimilating the data fragments. We have presented an Enhanced Secured Lempel-Ziv-Welch (ES-LZW) algorithm that provides cryptographic operations for secure data transfer. In proposed model, we have utilized the disjoint paths to transfer the data fragments from sender side and assimilate these fragments at receiver to get the original data. The messages containing data fragments are compressed and encrypted as well. Our scheme ensures confidentiality, integrity, efficient memory utilization, and resilience against node compromising attacks. We have validated our work through extensive simulations in NS-2.35 using TCL and C language. Results prove that our scheme reduces memory consumption along with less encryption and decryption cost as compared to blowfish especially when plaintext has more repetitive data. We have also analyzed the impact of creating data fragments, fraction of communication compromised, and probability to compromise the data fragments by subverting intermediaries.

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Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

Joshi

Sharma

2021

Preprint

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The exposure of IoT nodes to the internet makes them vulnerable to malicious attacks and failures. These failures affect the survivability, integrity, and connectivity of the network. Thus the detection and elimination of attacks in a timely manner become an important factor to maintain the network connectivity. Trust-based techniques are used in understanding the behavior of nodes in the network. Several researchers have proposed conventional trust models that are power-hungry and demand large storage space. Succeeding this Hidden Markov Models have also been developed to calculate trust but the survivability of network achieved from them is low. To improve the survivability selfish and malicious nodes present in the network are required to be treated separately. Hence, an improved Hidden Markov Trust (HMT) Model is developed in this paper which accurately detects the selfish and malicious nodes that illegally intercept the network. An algorithm is generalized for learning the behavior of nodes using the HMT model with the expected output. The evaluated node’s likelihood functions differentiate the selfish node from the malicious node and provide independent timely treatment to both types of nodes. Further, comparative analysis for attacks such as black-hole, grey-hole, and sink-hole has been done and performance parameters have been extended to survivability-rate, power-consumption, delay, and false-alarm-rate, for different networks sizes and vulnerability. Simulation result provides a 10% higher PDR, 29% lower overhead, and 15% higher detection rate when compared to FUCEM, FTCSPM, and OADM trust models presented in the literature.

show abstract

A secured MANET using position‐based opportunistic routing and SEMI MARKOV process

Cited by 3 publications

References 12 publications

Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

Secure and efficient data transfer using spreading and assimilation in MANET

Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

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