Nimmala Mangathayaru

An improved k-Means Clustering algorithm for Intrusion Detection using Gaussian function

In this paper the major objective is to design and analyze the suitability of Gaussian similarity measure for intrusion detection. The objective is to use this as a distance measure to find the distance between any two data samples of training set such as DARPA Data Set, KDD Data Set. This major objective is to use this measure as a distance metric when applying k-means algorithm. The novelty of this approach is making use of the proposed distance function as part of k-means algorithm so as to obtain disjoint clusters. This is followed by a case study, which demonstrates the process of Intrusion Detection. The proposed similarity has fixed upper and lower bounds.

Intrusion Detection Using Text Processing Techniques: A Recent Survey

Intrusion Detection is one of the major threats for any organization of any size. The approach of intrusion detection using text processing has been one of the research interests among researchers working in the area of the network and information security. In this approach for intrusion detection, the system calls serve as the source for mining and predicting any chance of intrusion. When an application runs, there might be several system calls which are initiated in the background. These system calls form the basis and the deciding factor for intrusion detection. We perform an extensive survey on Intrusion detection using text mining techniques and validate the suitability of various kernel measures published in the literature. We finally come out with the research directions for intrusion detection which have not been discussed in detail in the literature. We hope this survey will be useful for researchers working in the direction of intrusion detection using text mining techniques.

Design of novel fuzzy distribution function for dimensionality reduction and intrusion detection

Reducing the processing complexity is main challenge when dealing with intrusion detection systems. The processing complexity is reduced and efficiency is increased if we can reduce the number of dimensions so that only the minimum number of dimensions is retained. This work mainly targets on achieving dimensional reduction for intrusion detection using a novel membership function. The membership function is used to cluster the features in iterative incremental manner and obtains a reduced dimensional representation which retains the original distribution of process data. A case study is discussed to explore working of proposed model.

An approach for Intrusion Detection using Text Mining Techniques

The problem of clustering is NP-Complete. The existing clustering algorithm in literature is the approximate algorithms, which cluster the underlying data differently for different datasets. The K-Means Clustering algorithm is suitable for frequency but not for binary form. When an application runs several system calls are implicitly invoked in the background. Based on these system calls we can predict the normal or abnormal behavior of applications. This can be done by classification. In this paper we tried to perform classification of processes running into normal and abnormal states by using system call behavior. We reduce the system call feature vector by choosing k-means algorithm which uses the proposed measure for dimensionality reduction. We give the design of the proposed measure. The proposed measure has upper and lower bounds which are finite.

An Approach to Find Missing Values in Medical Datasets

Mining medical datasets is a challenging problem before data mining researchers as these datasets have several hidden challenges compared to conventional datasets. Starting from the collection of samples through field experiments and clinical trials to performing classification, there are numerous challenges at every stage in the mining process. The preprocessing phase in the mining process itself is a challenging issue when, we work on medical datasets. One of the prime challenges in mining medical datasets is handling missing values which is part of preprocessing phase. In this paper, we address the issue of handling missing values in medical dataset consisting of categorical attribute values. The main contribution of this research is to use the proposed imputation measure to estimate and fix the missing values. We discuss a case study to demonstrate the working of proposed measure.

An approach for intrusion detection using fuzzy feature clustering

This work discusses the approach for intrusion detection and classification by devising a membership function, inspired from [43] and is used in this work to carry the dimensionality reduction of processes present in the training set. The reduced process representation is then used to perform classification and prediction for detecting intrusion. The reduced representation of processes retains the system call distribution same as the initial process representation.

Text mining based approach for intrusion detection

Intrusion detection is classified as NP-Hard in the literature even today. Also supervised learning also termed classification, when performed on high dimensional documents has problem from the noise or outliers, which make the text classification inaccurate and leads to reduced accuracy by classifiers. We discuss the feature reduction methods which we adopted to achieve dimensionality reduction. In the Feature Extraction process, the high dimensional text documents are projected onto their corresponding low dimensional representation in feature space through using algebraic rules and transformations. The objective is to find optimal transformation matrix corresponding the input high dimensional document feature matrix. This objective is achieved in this thesis by using the concept of feature clustering and through clustering the features into a optimal set of clusters by designing a novel fuzzy membership function. The membership function designed retains the original distribution of words in the documents which is the importance of this approach.

Clustering and Classification of Effective Diabetes Diagnosis: Computational Intelligence Techniques Using PCA with kNN

The fourth leading disease in the world today is Diabetes and there are number of challenges to predict and identify the disease. Data mining proposes effective approaches to identify the diabetic patients. This paper proposes clustering and classification of effective diabetes diagnosis based on computational intelligence techniques using PCA with kNN. Diabetes disease data is used to identify feature of clusters. Diabetes disease diagnosis proposes novel distribution function applied to classify each patient. This proposed procedure defines clusters and similarity measure based on classifying with each cluster using computational intelligence techniques. PCA using diabetes disease data for dimensionality reduction. Novel similarity measure is proposed in kNN for classification. Accuracy measures are computed for each patient.

Dependency Parser for Telugu Language

In Telugu language sentence if we change the word order its meaning was not changed whereas in English if we change the word order the meaning was changed. So Telugu is morphologically rich so it is very difficult to develop syntactic parsers for these types of languages. To construct a dependency parser for such type of languages it will require a good morphology based Parts of Speech (POS) tagger. We used good POS tagger it has a good performance on Telugu sentences. The present work describes the steps to developing the dependency parser for the Telugu language. Bottom up approach was applied to a sentence for developing a dependency parser. Using this approach most of words were correctly assigned to its karakas and sentences were parsed correctly. The syntactic parser has many applications they are question-answering system, information retrieval, information extraction and language translation.

Exploring Research Issues in Mining Medical Datasets

Mining medical datasets is a challenging problem before data mining researchers as these datasets have several hidden challenges compared to conventional datasets. Starting from the collection of samples through field experiments and clinical trials to performing classification, there are numerous challenges at every stage in the mining process. The preprocessing phase in the mining process itself is a challenging issue when, we work on medical datasets. The main contribution of this research includes the detailed survey carried out and brings out the discussion that is not initiated in research papers published in the fields of medical and health informatics. We made a sincere effort towards making this possible and aim to bring out the various research issues associated with the disease prediction from the perspective of data mining. We also discuss the nature of medical disease datasets before switching our attention towards prediction or classification.