Cherukuri Aswani Kumar

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Due to increase in intrusion incidents over internet, many network intrusion detection systems are developed to prevent network attacks. Data mining, pattern recognition and classification methods are used to classify network events as a normal or anomalous one. This paper is aimed at evaluating different tree based classification algorithms that classify(More)
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(More)
Intrusion detection systems (IDS) play a major role in detecting the attacks that occur in the computer or networks. Anomaly intrusion detection models detect new attacks by observing the deviation from profile. However there are many problems in the traditional IDS such as high false alarm rate, low detection capability against new network attacks and(More)
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(More)
Domains such as text, images etc contain large amounts of redundancies and ambiguities among the attributes which result in considerable noise effects (i.e. the data is high dimension). Retrieving the data from high dimensional datasets is a big challenge. Dimensionality reduction techniques have been a successful avenue for automatically extracting the(More)
Rule acquisition is one of the main purposes in the analysis of decision formal contexts. Up to now, there have existed several types of rules (e.g., the decision rules and the granular rules) in decision formal contexts. This study firstly proposes a new algorithm with less time complexity for deriving the non-redundant decision rules from a decision(More)
Anomaly detection is one of the important challenges of network security associated today. We present a novel hybrid technique called G-LDA to identify the anomalies in network traffic. We propose a hybrid technique integrating Latent Dirichlet Allocation and genetic algorithm namely the G-LDA process. Furthermore, feature selection plays an important role(More)