Machine Learning Based Security Solutions in MANETs: State of the art approaches

Popli, Renu; Sethi, Monika; Kansal, Isha; Garg, Atul; Goyal, Nitin

doi:10.1088/1742-6596/1950/1/012070

Cited by 15 publications

(8 citation statements)

References 10 publications

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“…In [13], authors proposed a model for Secure Optimization Routing Algorithm for Mobile Ad Hoc Networks. In [14], authors discussed AI, ML and Genetic Algorithms based methods for routing process.…”

Section: ░ 2 Related Workmentioning

confidence: 99%

Intensive Analysis of Routing Algorithm Detection over Mobile Ad hoc Network Using Machine Learning

Rajesh

Srinivas

Lakshmanarao

2022

IJEER

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As we are in the Digital Era, in our daily life activities we use different types of communication devices like Mobile Phone, Laptops, Smart watches, etc. and In Common these Mobile Devices allows users to access Information and Services through wireless mode. Basically, there are two types of wireless Networks they are Infrastructure and Infrastructure less Networks. This Infrastructure Network is known as Cellular Networks, they are stationary components, and they used to connect the Mobile Nodes within the Network. But this Infrastructure less Network is also known as Mobile Ad hoc Network (MANET). MANET consist of no fixed Routers and unlimited Nodes. They are Dynamic in Nature. They can move at any direction and at any Movement. It forms a temporary Network for Data Transmission Purpose from Source and Destination. There can be any number of Nodes in Between Source to Destination. Here this Transmission of data is taken place with an Optimal Path in the MANET. This MANET is preferred to be used during Natural Calamities and for Military Operations. Here the node will be created first for communication in this proposed research work. Then the Machine Learning Algorithm will be applied after the node creation. There will be n numbers of nodes, one is master and the n-1 are treated as slaves. The Master node will be concluded by using Highest Network range. The charge will be checked and maintained by Machine Learning Algorithm. Again, Master will update the information. Here ML Algorithm will be act as data centers. The Master node will be changed as per their Capacities based on ML algorithm. In this research work we will discuss about the process of Routing, which will be carried by DSR Algorithm which is more responsive when compared with AODV and DSDV.

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Section: ░ 2 Related Workmentioning

confidence: 99%

Intensive Analysis of Routing Algorithm Detection over Mobile Ad hoc Network Using Machine Learning

Rajesh

Srinivas

Lakshmanarao

2022

IJEER

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“…Where R2 indicate a random number which is a range between 0 and 1, A is estimated by using the following expression (6).…”

Section: Step1: Initializationmentioning

confidence: 99%

“…Intruders can affect the network's operation via attacking any of the layers like the network layer, physical layer or MAC layer, making MANETs vulnerable in their functionality [5]. Because traditional data security techniques like encryption and authentication cannot give complete protection, Intrusion Detection and Prevention (IDP) technologies are widely utilized for safeguarding MANETs [6].…”

Section: Introductionmentioning

confidence: 99%

Design of Intrusion Detection and Prevention Model Using COOT Optimization and Hybrid LSTM-KNN Classifier for MANET

Madhu

2022

EAI Endorsed Scal Inf Syst

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INTRODUCTION: MANET is an emerging technology that has gained traction in a variety of applications due to its ability to analyze large amounts of data in a short period of time. Thus, these systems are facing a variety of security vulnerabilities and malware assaults. Therefore, it is essential to design an effective, proactive and accurate Intrusion Detection System (IDS) to mitigate these attacks present in the network. Most previous IDS faced challenges such as low detection accuracy, decreased efficiency in sensing novel forms of attacks, and a high false alarm rate. OBJECTIVES: To mitigate these concerns, the proposed model designed an efficient intrusion detection and prevention model using COOT optimization and a hybrid LSTM-KNN classifier for MANET to improve network security. METHODS: The proposed intrusion detection and prevention approach consist of four phases such as classifying normal node from attack node, predicting different types of attacks, finding the frequency of attack, and intrusion prevention mechanism. The initial phases are done through COOT optimization to find the optimal trust value for identifying attack nodes from normal nodes. In the second stage, a hybrid LSTM-KNN model is introduced for the detection of different kinds of attacks in the network. The third stage performs to classify the occurrence of attacks. RESULTS: The final stage is intended to limit the number of attack nodes present in the system. The proposed method's effectiveness is validated by some metrics, which achieved 96 per cent accuracy, 98 per cent specificity, and 35 seconds of execution time. CONCLUSION: This experimental analysis reveals that the proposed security approach effectively mitigates the malicious attack in MANET.

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“…Nowadays, the use of MANETs is highly appealing and widespread in a lot of applications such as space communication, disaster relief, mission-critical battlefield communication, road or accident guidance, trade fairs, sports stadiums, shopping malls, and avoiding vehicle crashes [8]. However, MANET properties make the network's environment vulnerable to various types of attacks, including wormholes, black holes, Grey Holes, Rushing, and flood-based attacks [2][3][4][9][10][11]. Many techniques and methods have been developed to deal with these attacks.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Intrusion Detection in Ad-hoc Networks: Wormhole and Blackhole Attacks Case

Aurelle Tchagna Kouanou,

Theophile Fozin Fonzin,

Franck Mani Zanga

et al. 2023

Cloud Computing and Data Science

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This paper addresses the security concerns associated with Mobile Ad-hoc Networks (MANET) and proposes a new method for detecting and preventing attacks using machine learning. The study involved the creation of a MANET with 26 nodes in NetSim (Network Simulator) software, followed by the implementation of wormhole and blackhole attacks. A dataset was generated from the network traffic obtained during the simulations, and a machine-learning model was designed to predict and detect these attacks. The model achieved high sensitivity, accuracy and f1 scores of 99%. The effectiveness of the model was tested by developing a real-time application. This method can be applied to any wireless network and is particularly relevant for companies that use Ad-hoc networks for communication.

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Machine Learning Based Security Solutions in MANETs: State of the art approaches

Cited by 15 publications

References 10 publications

Intensive Analysis of Routing Algorithm Detection over Mobile Ad hoc Network Using Machine Learning

Intensive Analysis of Routing Algorithm Detection over Mobile Ad hoc Network Using Machine Learning

Design of Intrusion Detection and Prevention Model Using COOT Optimization and Hybrid LSTM-KNN Classifier for MANET

Machine Learning for Intrusion Detection in Ad-hoc Networks: Wormhole and Blackhole Attacks Case

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