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Author

S. Thamarai Selvi

Bio: S. Thamarai Selvi is an academic researcher from Anna University. The author has contributed to research in topics: Grid & Scheduling (computing). The author has an hindex of 14, co-authored 67 publications receiving 690 citations. Previous affiliations of S. Thamarai Selvi include Madras Institute of Technology & Maharaja Sayajirao University of Baroda.


Papers
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Proceedings ArticleDOI
01 Dec 2014
TL;DR: This work analyzes the SVM classifier and compares it with other classifiers for DDoS detection to show that SVM performs accurate classification than others.
Abstract: Software Defined Networking (SDN) provides separation of data plane and control plane. The controller has centralized control of the entire network. SDN offers the ability to program the network and allows dynamic creation of flow policies. The controller is vulnerable to Distributed Denial of Service (DDoS) attacks that leads to resource exhaustion which causes non-reachability of services given by the controller. The detection of DDoS requires adaptive and accurate classifier that does decision making from uncertain information. It is critical to detect the attack in the controller at earlier stage. SVM is widely used classifier with high accuracy and less false positive rate. We analyze the SVM classifier and compare it with other classifiers for DDoS detection. The experiments show that SVM performs accurate classification than others.

151 citations

Journal ArticleDOI
01 Jan 2007
TL;DR: Advanced classification techniques based on Least Squares Support Vector Machines (LS-SVM) are proposed and applied to brain image slices classification using features derived from slices and compared with other classifiers like SVM with linear and nonlinear RBF kernels, RBF classifier, Multi Layer Perceptron (MLP) classifier and K-NN classifier.
Abstract: This research paper proposes an intelligent classification technique to identify normal and abnormal slices of brain MRI data. The manual interpretation of tumor slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed which caters the need for classification of image slices after identifying abnormal MRI volume, for tumor identification. In this research work, advanced classification techniques based on Least Squares Support Vector Machines (LS-SVM) are proposed and applied to brain image slices classification using features derived from slices. This classifier using linear as well as nonlinear Radial Basis Function (RBF) kernels are compared with other classifiers like SVM with linear and nonlinear RBF kernels, RBF classifier, Multi Layer Perceptron (MLP) classifier and K-NN classifier. From this analysis, it is observed that the proposed method using LSSVM classifier outperformed all the other classifiers tested.

135 citations

Proceedings ArticleDOI
01 Dec 2009
TL;DR: A novel trust model to evaluate the grid and cloud resources by means of resource broker that evaluates the trust value of the resources based on the identity as well as behavioral trust is introduced.
Abstract: Trust plays an important role in all commercial grid and cloud environments. It is the estimation of competence of a resource provider in completing a task based on reliability, security, capability and availability in the context of distributed environment. It enables users to select the best resources in the heterogeneous grid and cloud infrastructure. This paper introduces a novel trust model to evaluate the grid and cloud resources by means of resource broker. The resource broker chooses appropriate grid/cloud resource in heterogeneous environment based on the requirements of user. Our proposed trust management system is implemented with Kerberos authentication and PERMIS (PrivilEge and Role Management Infrastructure Standard) authorization to enhance the trust of the broker itself. The proposed trust enhanced resource broker evaluates the trust value of the resources based on the identity as well as behavioral trust. The proposed model considers metrics suitable for both grid and cloud resources. The results of the experiments show that the proposed model selects the dependable and reliable resources in grid and cloud environment.

76 citations

Journal ArticleDOI
TL;DR: A five-fold DDoS Defense Mechanism using an Information Divergence scheme that detects the attacker and discards the adversary's packets for a fixed amount of time in an organized manner is proposed.

29 citations

Proceedings ArticleDOI
01 Jan 2017
TL;DR: The proposed model is found to be more efficient in categorizing the documents when compared with other text categorization models such as fuzzy relevance clustering, ML-KNN (Multi-label KNN) and Naïve-Bayes Algorithms.
Abstract: Millions of file uploads and downloads happen every minute resulting in big data creation and manual text categorization is not possible. Hence, there is a need for automatic categorization of documents that makes storage and retrieval more efficient. This research paper proposes a hybrid text categorization model that combines both Rocchio algorithm and Random Forest algorithm to perform Multi-label text categorization. Stop word remover and word stemmer has been used to overcome the limitations in Rocchio Algorithm. Random Forest model takes minimal categories as input to reduce its error rate. Experiments were done on standard text categorization datasets. Our proposed model is found to be more efficient in categorizing the documents when compared with other text categorization models such as fuzzy relevance clustering, ML-KNN (Multi-label KNN) and Naive-Bayes Algorithms.

25 citations


Cited by
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01 Apr 1997
TL;DR: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity.
Abstract: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

2,188 citations

Journal ArticleDOI
TL;DR: This survey report describes key literature surveys on machine learning (ML) and deep learning (DL) methods for network analysis of intrusion detection and provides a brief tutorial description of each ML/DL method.
Abstract: With the development of the Internet, cyber-attacks are changing rapidly and the cyber security situation is not optimistic. This survey report describes key literature surveys on machine learning (ML) and deep learning (DL) methods for network analysis of intrusion detection and provides a brief tutorial description of each ML/DL method. Papers representing each method were indexed, read, and summarized based on their temporal or thermal correlations. Because data are so important in ML/DL methods, we describe some of the commonly used network datasets used in ML/DL, discuss the challenges of using ML/DL for cybersecurity and provide suggestions for research directions.

676 citations

Journal ArticleDOI
TL;DR: A brief overview of text classification algorithms is discussed in this article, where different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods are discussed, and the limitations of each technique and their application in real-world problems are discussed.
Abstract: In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in real-world problems are discussed.

624 citations

Journal ArticleDOI
TL;DR: An overview of text classification algorithms is discussed, which covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods.
Abstract: In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in the real-world problem are discussed.

612 citations

Journal ArticleDOI
TL;DR: A hybrid intelligent machine learning technique for computer-aided detection system for automatic detection of brain tumor through magnetic resonance images is proposed and demonstrates its effectiveness compared with the other machine learning recently published techniques.
Abstract: Computer-aided detection/diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. The objective of this paper is to review the recent published segmentation and classification techniques and their state-of-the-art for the human brain magnetic resonance images (MRI). The review reveals the CAD systems of human brain MRI images are still an open problem. In the light of this review we proposed a hybrid intelligent machine learning technique for computer-aided detection system for automatic detection of brain tumor through magnetic resonance images. The proposed technique is based on the following computational methods; the feedback pulse-coupled neural network for image segmentation, the discrete wavelet transform for features extraction, the principal component analysis for reducing the dimensionality of the wavelet coefficients, and the feed forward back-propagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 101 images consisting of 14 normal and 87 abnormal (malignant and benign tumors) from a real human brain MRI dataset. The classification accuracy on both training and test images is 99% which was significantly good. Moreover, the proposed technique demonstrates its effectiveness compared with the other machine learning recently published techniques. The results revealed that the proposed hybrid approach is accurate and fast and robust. Finally, possible future directions are suggested.

482 citations