A Note on Implementing Recurrence Quantification Analysis for Network Anomaly Detection

This paper deal with the network anomaly detection, based on the analysis of non-stationary properties that occur in the aggregated IP traffic flows. We use recurrence quantification analysis (RQA), a mathematical nonlinear technique to achieve this task. The objective is to model the standard network traffic and report any deviation from it. We create a baseline from which we derive the RQA parameters. Using these parameters we explore the hidden recurrence patterns in the network traffic. Further, the detection is analysed using the support vector machine to classify the deviations from the regular traffic. Experiments are conducted on Vellore Institute of Technology University campus network traffic data to validate the model.Defence Science Journal, 2012, 62(2), pp.112-116, DOI:http://dx.doi.org/10.14429/dsj.62.117

Implementing Secure Group Communications using Key Graphs

While the technical issues of securing unicast communications for client-server computingare fairly well-understood, the technical issues of securing group communications are not. Theexisting approach to improve the scalability is to decompose a large group of clients into manysubgroups and employ a hierarchy of group security agents. In this paper, the secure groupcommunications using key graphs and the implementation of a different hierarchical approachto improve the scalability and secure group communication using key graphs has been presented

Knowledge discovery in data using formal concept analysis and random projections

In this paper our objective is to propose a random projections based formal concept analysis for knowledge discovery in data. We demonstrate the implementation of the proposed method on two real world healthcare datasets. Formal Concept Analysis (FCA) is a mathematical framework that offers a conceptual knowledge representation through hierarchical conceptual structures called concept lattices. However, during the design of a concept lattice, complexity plays a major role

A comprehensive overview on the foundations of formal concept analysis

The immersion of voluminous collection of data is inevitable almost everywhere. The invention of mathematical models to analyse the patterns and trends of the data is an emerging necessity to extract and predict useful information in any Knowledge Discovery from Data (KDD) process. The Formal Concept Analysis (FCA) is an efficient mathematical model used in the process of KDD which is specially designed to portray the structure of the data in a context and depict the underlying patterns and hierarchies in it. Due to the huge increase in the application of FCA in various fields, the number of research and review articles on FCA has raised to a large extent. This review differs from the existing ones in presenting the comprehensive survey on the fundamentals of FCA in a compact and crisp manner to benefit the beginners and its focuses on the scalability issues in FCA. Further, we present the generic anatomy of FCA apart from its origin and growth at a primary level

Modeling cognitive functionalities of prosthetic arm using conceptual spaces

Conceptual space framework is used for representing knowledge in cognitive systems. In this paper, we have adapted conceptual space framework for prosthetic arm considering its cognitive abilities such as receiving signals, recognizing and decoding the signal and responding with the corresponding action in order to develop a conceptual space of the prosthetic arm. Cognitive functionalities such as learning, memorizing and distinguishing configurations of prosthetic arm are achieved via its conceptual space. To our knowledge, this work is the first attempt to adapt the conceptual spaces to model cognitive functionalities of prosthetic arm. Adding to this, we have made use of different notion of concept that reflects the topological structure in concepts. To model the actions of the prosthetic arm functionalities, we have made use of force patterns to represent action. Similarly, to model the distinguishing ability, we make use of the relationship between the attributes conveyed by adapted different notion of concept

Latent semantic indexing using eigenvalue analysis for efficient information retrieval

Text retrieval using Latent Semantic Indexing (LSI) with truncated Singular Value Decomposition (SVD) has been intensively studied in recent years. However, the expensive complexity involved in computing truncated SVD constitutes a major drawback of the LSI method. In this paper, we demonstrate how matrix rank approximation can influence the effectiveness of information retrieval systems. Besides, we present an implementation of the LSI method based on an eigenvalue analysis for rank approximation without computing truncated SVD, along with its computational details. Significant improvements in computational time while maintaining retrieval accuracy are observed over the tested document collections