Visible to the public A Multi-layered Outlier Detection Model for Resource Constraint Hierarchical MANET

TitleA Multi-layered Outlier Detection Model for Resource Constraint Hierarchical MANET
Publication TypeConference Paper
Year of Publication2018
AuthorsKumar, A., Aggarwal, A., Yadav, D.
Conference Name2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)
Date Publishednov
ISBN Number978-1-5386-5002-8
Keywordsad hoc communication MANET, attack detection, composability, Data analysis, Data collection, decentralized network, density-based clustering, distributed network, Dynamic Network, fixed infrastructure, hierarchical categorized data, hierarchical similarity metric, high security level, inliers, layered architecture, MANET Attack Detection, Metrics, mobile ad hoc networks, multilayered outlier detection model, network performance, novel multilayered outlier detection algorithm, outlier minimum improvements, Outliers, pubcrawl, QoS, quality of service, quality-of-service parameters, Resiliency, resource constraint hierarchical MANET, security dimensions

For sharing resources using ad hoc communication MANET are quite effective and scalable medium. MANET is a distributed, decentralized, dynamic network with no fixed infrastructure, which are self- organized and self-managed. Achieving high security level is a major challenge in case of MANET. Layered architecture is one of the ways for handling security challenges, which enables collection and analysis of data from different security dimensions. This work proposes a novel multi-layered outlier detection algorithm using hierarchical similarity metric with hierarchical categorized data. Network performance with and without the presence of outlier is evaluated for different quality-of-service parameters like percentage of APDR and AT for small (100 to 200 nodes), medium (200 to 1000 nodes) and large (1000 to 3000 nodes) scale networks. For a network with and without outliers minimum improvements observed are 9.1 % and 0.61 % for APDR and AT respectively while the maximum improvements of 22.1 % and 104.1 %.

Citation Keykumar_multi-layered_2018