K. Murugan ¹, T. Ravichandran ²

Affiliations
1 Karpagam University, Coimbatore, Tamil Nadu-641021, IN
2 Hindusthan Institute of Technology, Coimbatore, Tamil Nadu-641032, IN

Abstract

Now a day's interaction with computer become essential, efficient processing, storing and retrieving of data from database will play very important role in the database application. To access the database the user should have a strong knowledge in SQL commands and procedures. The conventional database will give only current data but not past or future data. In this paper we propose an Intelligent Query Processing (IQP) for temporal databases. This will facilitate the novice user to interact temporal database in their native language (English), without using any SQL command or procedures. The main purpose of IQP is for an English sentence to be interpreted by the computer and appropriate action taken. Asking questions to databases in natural language is a very convenient and easy method of data access. The temporal data will support for past, present and future data. In temporal data we used third axis as time interval, which support both transaction time as well valid time. The valid time is the actual or real world time at which the data is valid. This paper proposes the architecture for translating English Query into SQL using efficient context free grammar. This system has been implemented using Java that can be used in any operating system and has been tested with data from the industry domain.

Keywords

Intelligent Query Processing, Temporal Database Context Free Grammar

Full Text

   

K. Murugan ¹, P. Suresh ²

Affiliations
1 Department of Computer Science, Government College for Women, Kolar – 563101, Karnataka, IN
2 Department of Computer Science, Salem Sowdeswari College, Salem – 636010, Tamil Nadu, IN

Abstract

Objectives: An ensemble of Ada Booster with SVM (EAB-SVM) classifier technique is proposed for detecting the network intrusions and monitoring the activities of the node as well as classifying it as either normal or anomalous. Methods/ Statistical Analysis: The optimal feature selection method is applied in EAB-SVM to select the relevant features to classify and detect the intrusion in wireless ad-hoc network. After that, an ensemble of Ada booster with SVM (EAB-SVM) classifier is used for classifying the intrusion through updating the weight of samples. Finally, the objective function of the EAB-SVM classifier is used to distinguish the anomalous and normal node behavior accurately. This in turn improves the anomaly intrusion detection accuracy. Findings: The EAB-SVM technique comprises of two model namely optimal feature selection model and intrusion detection model. The EAB-SVM technique employed Optimal Feature Selection to choose the features and to reduce the data space dimension. This in turn assists to increase the packet delivery ratio. After selecting the optimal features, the intrusion detection and classification is performed using AdaBoost with SVM classifier for intrusion detection. The AdaBoost with SVM classifier discover the node behaviors as normal or anomalous and thereby achieves reliable packet delivery in wireless ad-hoc network. The performance of EAB-SVM technique is evaluated in terms of packet delivery ratio, classification time, false positive rate and anomaly intrusion detection accuracy and compared with existing two methods. Application/Improvements: The simulation results shows that the EAB-SVM technique achieved the better performance in terms of packet delivery ratio, classification time, false positive rate and anomaly intrusion detection accuracy compared to state-of-the-art methods.

Keywords

Ada Booster, Anomalous Node, Anomaly-Based Intrusion Detection Features, Normal Node, SVM Classifier, Wireless Ad Hoc Network.

Full Text