International Journals

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/178

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    EFFICIENT PREDICTION OF CROSS-SITE SCRIPTING WEB PAGES USING EXTREME LEARNING MACHINE
    (International Journal of Computer Science & Engineering Technology, 2013-11) Krishnaveni S; Sathiyakumari K
    Malicious code is a way of attempting to acquire sensitive information by sending malicious code to the trustworthy entity in an electronic communication. JavaScript is the most frequently used command language in the web page environment. If the hackers misuse the JavaScript code there is a possibility of stealing the authentication and confidential information about an organization and user. The attack is based on the malicious JavaScript code inserted into pages by intruders or hackers. Various attacks like redirect, script injection and XSS which usually include to transmitting private data to attacker or redirecting the victim to web content controlled by hacker. A cross-site scripting vulnerability allows the introduction of malicious content on a web site that is then served to users. Therefore filtering malicious JavaScript code is necessary for any web application. The aim of this work is to analyze different malicious code attacks phenomenon, various types of malicious code attacks. The experimental results obtained on XSS classification in web pages using Extreme Learning Machine techniques. ELM approach can be found in its high sparseness, it can also be seen that ELM accomplishes better and more balanced classification for individual categories as well in very less training time comparative to other classification algorithms. The data are collected from the real web pages and various features are extracted to classify the malicious web page using supervised learning algorithms and the results demonstrate that the proposed features lead to highly accurate classification of malicious page.
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    MULTICLASS CLASSIFICATION OF XSS WEB PAGE ATTACK USING MACHINE LEARNING TECHNIQUES
    (Foundation of Computer Science, 2013-07) Krishnaveni S; Sathiyakumari K
    Web applications are most widely used technique for providing an access to online services. At the same time web applications are easiest way for vulnerable acts. When a security mechanism is failed then the user may download malicious code from a trusted web site. In this case, the malicious script is contracted to full access with all assets belonging to that legitimate web site. These types of attacks are called Cross-Site Scripting (XSS) attacks. Cross Site Scripting (XSS) attacks are the most common type of attack against web application, which allows hackers to inject the malicious script code for stealing the user‟s confidential information. Recent studies show that malicious code detection has become the most frequent vulnerability. In web browsers, the malicious script codes are executed and used to transfer the sensitive data to the third party (or hackers) domain. Currently, most research areas are attempted to prevent XSS on both the client and server side. In this paper, we present a machine learning technique to classify the malicious web pages. This work focus some of the possible ways to detect the XSS script on client side based on the features extracted from the web document content and the URL to scan the web pages for check the malicious scripts.
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    A SURVEY OF EXPERT FINDING IN ACADEMIC SOCIAL NETWORK
    (International Journal of Engineering Sciences & Research Technology, 2013-07) Krishnaveni S; Sathiyakumari K
    Social networks place an important role in sharing knowledge, retrieving information from various websites.Recent studies suggest that an increasing participation of people in online activities like content publishing, different kinds of relationships and interactions among people in online social network web sites. Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of application domains. This survey aims at providing a comprehensive overview of the research efforts made in the field of Profile Extraction from the Academic Social Network. In this paper tried to review some of the accomplished research of expert finding and profile extraction. The contribution of this paper is based on the extraction of social networks and a research framework for analyzing the experts in specified topics and co-author relationships in researcher network using various algorithms and tools.