Fuzzy Based Intrusion Detection Systems in MANET

Balan, E. Vishnu; Kumar, Priyan Malarvizhi; Gokulnath, C.; Devi, G. Usha

doi:10.1016/j.procs.2015.04.071

Cited by 61 publications

(18 citation statements)

References 9 publications

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“…The idea is to offer an enhanced on-demand routing protocol more secure adaptively. Similar line of research work is also carried out by Balan et al [49]. The work carried out by Inaba et al [50] emphasizes on the improving the handover policy as well as call admission control by incorporating security approach.…”

Section: I) Fuzzy-logic Based Security Solutionmentioning

confidence: 70%

Insights on Security Improvements and Implications of Artificial Intelligence in MANET

T.M.¹,

Shivakumar²

2019

CAE

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Mobile Adhoc Network (MANET) is claimed to be an integral part of all futuristic network and communication system e.g. cloud, reconfigurable network, Internet-of-Things (IoT), interdomain routing, etc. However, the security problems of MANET is not yet been mitigated irrespective of archives of security approaches and solution. For a MANET system to be integrated with upcoming technologies it is required that their routing system should offer higher level of resistance against all potential threats as well as they should also offer a better flexibility to tailor the security scheme based on demand of the environment, where the mobile nodes are operating. It is also seen that Artificial Intelligence is another big boon which is currently being proliferating at faster speed and there are some segment of research where it is also claimed to contribute towards security system in MANET. Therefore, this paper offers insights to the current state of security system using conventional as well as using Artificial Intelligence to explore about its research gap.

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Section: I) Fuzzy-logic Based Security Solutionmentioning

confidence: 70%

Insights on Security Improvements and Implications of Artificial Intelligence in MANET

T.M.¹,

Shivakumar²

2019

CAE

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“…Every component of the model of rule induction is introduced in detail in Reference. 13 In the literature, 14 rule induction is carried out for the absence of feature values in the information system. In the literature, 15,16 the researchers used the result of attribute reduction to classify datasets with neural networks; the testing result indicated that with less study time the misclassification does not increase significantly, and they declared that the attribute reduction of rough set has the possibility of practical application.…”

Section: Related Workmentioning

confidence: 99%

Forest optimization algorithm‐based feature selection using classifier ensemble

Moorthy

Gandhi

2019

Computational Intelligence

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Features selection is the process of choosing the relevant subset of features from the high-dimensional dataset to enhance the performance of the classifier. Much research has been carried out in the present world for the process of feature selection. Algorithms such as Naïve Bayes (NB), decision tree, and genetic algorithm are applied to the high-dimensional dataset to select the relevant features and also to increase the computational speed. The proposed model presents a solution for selection of features using ensemble classifier algorithms. The proposed algorithm is the combination of minimum redundancy and maximum relevance (mRMR) and forest optimization algorithm (FOA). Ensemble-based algorithms such as support vector machine (SVM), K-nearest neighbor (KNN), and NB is further used to enhance the performance of the classifier algorithm. The mRMR-FOA is used to select the relevant features from the various datasets and 21% to 24% improvement is recorded in the feature selection. The ensemble classifier algorithms further improves the performance of the algorithm and provides accuracy of 96%.

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“…However, there comparatively expensive operations have led to a rise in clustering techniques to mitigate the calculations need when associating anomaly detection with big data [12], [13]. Using aggregated data, such as NetFlow can be used to reduce the calculations further [14].…”

Section: Related Workmentioning

confidence: 99%

NetFlow Anomaly Detection Though Parallel Cluster Density Analysis in Continuous Time-Series

Flanagan

Fallon

Connolly

et al. 2017

Lecture Notes in Computer Science

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The increase in malicious network based attacks has resulted in a growing interest in network anomaly detection. The ability to detect unauthorized or malicious activity on a network is of importance to any organization. With the increase in novel attacks, anomaly detection techniques can be more successful in detecting unknown malicious activity in comparison to traditional signature based methods. However, in a real-world environment, there are many variables that cannot be simulated. This paper proposes an architecture where parallel clustering algorithms work concurrently in order to detect abnormalities that may be lost while traversing over time-series windows. The presented results describe the NetFlow activity of the NPD Group, Inc. over a 24-hour period. The presented results contain real-world anomalies that were detected.

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Fuzzy Based Intrusion Detection Systems in MANET

Cited by 61 publications

References 9 publications

Insights on Security Improvements and Implications of Artificial Intelligence in MANET

Insights on Security Improvements and Implications of Artificial Intelligence in MANET

Forest optimization algorithm‐based feature selection using classifier ensemble

NetFlow Anomaly Detection Though Parallel Cluster Density Analysis in Continuous Time-Series

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