A novel secure routing scheme using probabilistic modelling for better resistivity against lethal attacks

Neeli, Jyoti; Cauvery, N. K.

doi:10.11591/ijece.v10i5.pp4956-4964

Cited by 1 publication

(1 citation statement)

References 38 publications

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“…Additionally, the system uses an Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  analytical secure routing modeling that continuously checks for harmful activity on surrounding nodes and helps source nodes decide whether to use safe routing. On the other hand, a mathematical model-based approach has been developed to detect the malevolent intent of mobile agents in the event that they go rogue, in order to stop DoS assaults in vehicular ad hoc network (VANET) [30]- [34]. Considering this literature, many ensemble approaches have been suggested in recent works to address the problems with single classifiers [35]- [37].…”

Section: Figure 2 Overview Of Grey Hole Attack [19]mentioning

confidence: 99%

Malicious attacks modelling: a prevention approach for ad hoc network security

Talib

Alothman

Mohammed

2023

IJEECS

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As a result of the expansions that have taken place in the field of networking and the increase in the number of users of networks, there have recently been breakthroughs made in the techniques and methods used for network security. In this paper, a virtual private network (VPN) is proposed as a means of providing the necessary level of security for particular connections that span across vast networks. After the network performance metrics such as time delay and throughput have been accomplished, the suggested VPN is recommended for the purpose of assuring network security. In addition, artificial intelligence attack predictors and virtual private networks have been implemented with the purpose of preventing harmful activity within such connections. Using a wide variety of machine learning methods like Random Forests and Nave Bays, malicious assaults of any kind can be identified and thwarted in their tracks. Another technique for anticipating attacks is the use of an artificial neural network, which is a type of system that engages in deep learning and learns the behaviors of attacks while it is being trained so that it can then predict attacks. The results of this study demonstrate that the use of machine learning and artificial intelligence techniques can significantly improve the security and performance of virtual private networks and can effectively identify and prevent malicious attacks on networks.

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