Gunupudi Rajesh Kumar

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 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.

Optimising business intelligence results through strategic application of software process model

According to Gartner, 60% of organisations are still unable to make fruitful decisions due to various factors like, data quality issues, lack of careful consideration of components involved, skill shortage. In this paper, we consider the software process, which is one of the key factors and show how a software process model may be applied to business intelligence (BI) process by defining the entire BI process as a two stage software component process model which internally involves other components. For this purpose, we have defined two software process component models for the entire BI process. We also emphasise all key considerations that need to be followed to achieve successful BI results. We throw light on how the sequential iteration phase can be applied at each component level. This model serves as a prototype for the future BI projects which can benefit organisations and top decision makers in making quality decisions.

Strategic Application of Software Process Model to Optimize Business Intelligence Results

Organizations across various sectors such as banking, automotive, energy and utilities or any other industry are strongly dependent on the analytics platform to arrive on their business decisions. Analytics has evolved as a major area and has been significantly helping organizations from years to get profitable business results. Organizations are now employing Business Intelligence tools and techniques to analyze their large complex volumes of data. Even though analytic tools and techniques are employed still many organizations are failing to come up with the right business decisions. This is because either they do not have the proper process model or they do not use a systematic process model for decision making. According to Gartner, on average sixty percent of the organizations are still not able to make fruitful decisions due to various factors like, data quality issues, lack of careful consideration of components involved, skill shortage to name a few. In this paper, we consider the software process, which is one of the key factors and show how a software process model may be applied to Business Intelligence process by defining the entire BI process as a two stage software component process model which internally involves other components. For this purpose, we have defined two software process component models for the entire BI process. We also emphasize all the key considerations that need to be followed to achieve successful BI results. We throw light on how the sequential iteration phase can be applied at each component level. This model serves as a prototype for the future BI projects which can benefit organizations and top decision makers in making quality decisions.