Immune System Inspired Strategies for Distributed Systems

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Soumya Banerjee

Technology

Distributed Systems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Properties of Distributed Systems ,[object Object],[object Object],[object Object]

Natural Immune System ,[object Object],[object Object],[object Object]

Problems Faced by the Immune System (IS) ,[object Object]

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Lymph Nodes (LN) ,[object Object],[object Object]

Decentralized Detection Network www.lymphadvice.com

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Peer-to-Peer Systems ,[object Object],[object Object],[object Object],[object Object]

Semantic Small World (SSW) P2P Overlay Network ,[object Object],[object Object],[object Object],[object Object],* M. Li et al. 2004

Semantic Small World (SSW) P2P Overlay Network adapted from M. Li et al. 2004

Bounds for Efficient Decentralized Search in SSW ,[object Object],[object Object],[object Object],[object Object],M. Li et al. 2004

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[Master Thesis Extended Abstract] With the advent of high-throughput sequencing methods, new ways of visualizing and analyzing increasingly amounts of data are needed. Although some software already exist, they do not scale well or require advanced skills to be useful in phylogenetics. The aim of this thesis was to implement three community finding algorithms – Louvain, Infomap and Layered Label Propagation (LLP); to benchmark them using two synthetic networks – Girvan-Newman (GN) and Lancichinetti-Fortunato-Radicchi (LFR); to test them in real networks, particularly, in one derived from a Staphylococcus aureus MLST dataset; to compare visualization frameworks – Cytoscape.js and D3.js, and, finally, to make it all available online (mscthesis.herokuapp.com). Louvain, Infomap and LLP were implemented in JavaScript. Unless otherwise stated, next conclusions are valid for GN and LFR. In terms of speed, Louvain outperformed all others. Considering accuracy, in networks with well-defined communities, Louvain was the most accurate. For higher mixing, LLP was the best. Contrarily to weakly mixed, it is advantageous to increase the resolution parameter in highly mixed GN. In LFR, higher resolution decreases the accuracy of detection, independently of the mixing parameter. The increase of the average node degree enhanced partitioning accuracy and suggested detection by chance was minimized. It is computationally more intensive to generate GN with higher mixing or average degree, using the algorithm developed in the thesis or the LFR implementation. In S. aureus network, Louvain was the fastest and the most accurate in detecting the clusters of seven groups of strains directly evolved from the common ancestor.

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The wormhole attack in Wireless sensor networks (WSNs) decreases the network performance by dropping the No. of Packets. Many techniques have been proposed to so far reduce the impact of the wormhole attack by detecting and preventing it. But, related work indicates that no technique is perfect for every kind of circumstances of WSNs. Among the existing techniques, Watchdog technique has better performance in preventing the wormhole attack. It utilizes the local knowledge of the next hop node and eavesdrops it. If it gets that spending time of the Packet is more than the given threshold, then it characterizes that node as wormhole attacker. However, this method has several shortcomings that it does not track the link transmission errors, which may be because of congestion in WSNs and also it not offers high mobility for maximum No. of nodes, which eventually decreases the WSNs performance. In order to handle this issue, a new multipoint relay based Watchdog monitoring and prevention technique is proposed in this paper. The proposed technique utilizes the dynamic threshold value to detect the wormhole attacker node, and then clustering and the Watchdog based optimistic path is selected for communicating the Packets. Thus, it reduces the overall Packet dropping, which improves the performance of the WSNs.

Modular RADAR: Immune System Inspired Strategies for Distributed Systems

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