Informatics Publishing Limited

N. Birla , M. Jagadish , E. Venkatesan

Abstract

Intrusion Detection Systems (IDS) plays a significant role in computer security. In network surroundings IDS find the activities that have an effect on Confidentiality, Integrity and accessibility on network knowledge. Currently, most computer systems use user IDs and passwords because the login patterns to certify users. However, many of us share their login patterns with coworkers and request these coworkers to help co-tasks, thereby creating the pattern united of the weakest points of computer security. Insider attackers, the valid users of a system who attack the system internally, are exhausting to find since most intrusion detection systems and firewalls establish and isolate malicious behaviors launched from the external world of the system solely. Additionally, some studies claimed that analyzing system calls (SCs) generated by commands will establish these commands, with that to accurately find attacks, with attack patterns are the options of an attack. Therefore, during this paper, a security system, named the interior Intrusion Detection and Protection System (IIDPS), is projected to find Insider attacks at SC level by victimization data processing and rhetorical techniques. The IIDPS creates users’ personal profiles to stay track of users’ usage habits as their rhetorical options and determines whether or not a sound login user is that the account holder or not by scrutiny his/her current laptop usage behaviors with the patterns collected within the account holder’s personal profile.

Keywords

Data Mining, Identifying Users, Intrusion Detection, System Call (SC).

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E. Venkatesan ¹, T. Velmurugan ¹

Affiliations
1 PG and Research Department of Computer Science, D.G. Vaishnav College, Chennai-600106, Tamil Nadu, IN

Abstract

Backgrounds/Objectives: Data Mining (DM) techniques are extremely utilized for the extraction of useful information which is available in data warehouses and other database repositories. In medical diagnose, the role of DM approach rises quick recognition of disease over symptoms. To classify the medical data, a number of DM techniques are used by researchers. One of such techniques is classification. The classification algorithms predict the hidden information in the medical domain. The breast cancer is the very dangerous disease for women in developed countries like India. Most of the women death happens in the world, they are affected by the breast cancer. Methods/Statistical Analysis: The role of classification is importantin the real world applications in every field. Classification is used to classify the elements permitting to the features of the elements through the predefined set of classes. This research work analyses the breast cancer data using classification algorithms namely j48, Classification and Regression Trees (CART), Alternating Decision Tree (AD Tree) and Best First Tree (BF Tree). Findings: To find the performance of classification algorithms, this work uses cancer data as input. Particularly, this work is carried out to compare the four decision tree algorithms in the prediction of the performance accuracy in breast cancer data. All the algorithms are applied for breast cancer data to classify the data set for classification and prediction. Among these four methods, this work concludes the best algorithm for the chosen input data on decision tree supervised learning algorithms to predict the best classifier. Applications/Improvements: The breast cancer data is analyzed by taking the images using the same algorithms in future. Also, the microcalcifications of the breast cancer imagery are to be investigated in the same work.

Keywords

CART Algorithm, Classification Algorithms, Decision Trees, J48 Algorithm

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E. Venkatesan ¹, T. Velmurugan ¹

Affiliations
1 PG and Research Department of Computer Science, D.G. Vaishnav College, Chennai-600106, IN

Abstract

Objectives/Backgrounds: The breast cancer has increased significantly in the last few years. It is one type of cancer and is the second deadliest disease in the world wide. Recently, cancer is diagnosed by various test such as mammography, ultrasound, etc. Mammography is used to breast imaging to help in detecting breast cancer. Methods/Statistical Analysis: The Mammogram images are taken for the analysis to find the tumor affected regions by data mining techniques in this research work. This work uses the Median filter method for noise removal and Gaussian filter for image enhancement of preprocessing the images. The k-Means algorithm, which is easily detected and extracts tumor area by means of intensity values by segmenting the mammography images. Two types of mammography images; normal and abnormal are given as input to the algorithms. After clustering the images by k-Means algorithm, the results found are classified by J48, JRIP, Support Vector Machines (SVM), Naïve Bayes and CART algorithms to verify the accuracy of the results based on its pixel values. Findings: The performance of taken classification algorithms is compared and find out the best classifier in terms of its accuracy, sensitivity and specificity. Improvements: In the future, the other classifiers and feature selection algorithms are applied to extract the mammography images. Also, it gives more than fifty images for analysis.

Keywords

Classification Accuracy, Classification Algorithms, k-Means Algorithm, Mammogram Images.

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V. Anandkumar ¹, E. Venkatesan ¹, Baldev Raj ¹, C. Karthikeyan ², R. S. Babu ²

Affiliations
1 Indira Gandhi Centre for Atomic Research, Kalpakkam, IN
2 The Welding Research Institute, BHEL, Tiruchirapalli, IN

Abstract

Steam generators are critical components for the Prototype fast Breeder Reactor being designed at the Indira Gandhi Centre for Atomic Research, Kalpakkam. A leak in the steam generators can have serious consequences leading to rupture of steam generator tubes. Consequently, by introducing a known leak in the experimental loop, data is required to be generated which can predict the response of the material to the leak as a function of leak rate, time, temperature, steam pressure and the size of the microhole as a result of sodium water reaction.

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