Reverse Proxy Framework using Sanitization Technique for Intrusion Prevention in Database

With the increasing importance of the internet in our day to day life, data security in web application has become very crucial. Ever increasing on line and real time transaction services have led to manifold rise in the problems associated with the database security. Attacker uses illegal and unauthorized approaches to hijack the confidential information like username, password and other vital details. Hence the real time transaction requires security against web based attacks. SQL injection and cross site scripting attack are the most common application layer attack. The SQL injection attacker pass SQL statement through a web applications input fields, URL or hidden parameters and get access to the database or update it. The attacker take a benefit from user provided data in such a way that the users input is handled as a SQL code. Using this vulnerability an attacker can execute SQL commands directly on the database. SQL injection attacks are most serious threats which take users input and integrate it into SQL query. Reverse Proxy is a technique which is used to sanitize the users inputs that may transform into a database attack. In this technique a data redirector program redirects the users input to the proxy server before it is sent to the application server. At the proxy server, data cleaning algorithm is triggered using a sanitizing application. In this framework we include detection and sanitization of the tainted information being sent to the database and innovate a new prototype.Comment: 9 pages, 6 figures, 3 tables; CIIT 2013 International Conference, Mumba

The approaches to quantify web application security scanners quality: A review

The web application security scanner is a computer program that assessed web application security with penetration testing technique. The benefit of automated web application penetration testing is huge, which web application security scanner not only reduced the time, cost, and resource required for web application penetration testing but also eliminate test engineer reliance on human knowledge. Nevertheless, web application security scanners are possessing weaknesses of low test coverage, and the scanners are generating inaccurate test results. Consequently, experimentations are frequently held to quantitatively quantify web application security scanner's quality to investigate the web application security scanner's strengths and limitations. However, there is a discovery that neither a standard methodology nor criterion is available for quantifying the web application security scanner's quality. Hence, in this paper systematic review is conducted and analysed the methodology and criterion used for quantifying web application security scanners' quality. In this survey, the experiment methodologies and criterions that had been used to quantify web application security scanner's quality is classified and review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. The objectives are to provide practitioners with the understanding of methodologies and criterions that available for measuring web application security scanners' test coverage, attack coverage, and vulnerability detection rate, while provides the critical hint for development of the next testing framework, model, methodology, or criterions, to measure web application security scanner quality

A multi-agent scanner to detect stored-XSS vulnerabilities

Proceeding of: 2010 International Conference for Internet Technology and Secured Transactions (ICITST), 8 to 11 November 2010 London, England, United KingdomThe cross-site scripting (XSS) has become a common vulnerability of many web sites and web applications. XSS consists in the exploitation of input validation flaws, with the purpose of injecting arbitrary script code which is later executed at the web browser of the victim. One interesting possibility to prevent this type of vulnerability is the use of vulnerability scanners. However, current scanners are capable of detecting just one of the two main modalities of XSS attacks. This paper introduces a novel multi–agent system for the automated scanning of web sites to detect the presence of XSS vulnerabilities exploitable by an stored–XSS attack. The rate of detection of the system is evaluated in two different scenarios.This work has been partially supported by CDTI (Ministerio de Industria, Turismo y Comercio of Spain) in collaboration with Telefonica I+D, Project SEGUR@ with reference CENIT-2007 2004Publicad

Implementation of Customized UTP Algorithm for Attack Detection in Multitier Web Applications

Internet services and application have gained lots of importance in our daily life such as banking, travel and social networking. Personal information from any of the remote location can be communicated and managed with the help of Internet. Due to their omnipresent use for daily task, web applications have been target for attack. To deal with increasing demand and data complexity web services and applications have moved to a multitiered design. The idea is to detect attacks in multitier architecture to model the network behavior of user sessions across both the front-end web server and the back-end database. The attacks like SQL injection, cross site scripting attack, privilege escalation attack and direct DB attack can be monitored with both the web and subsequent database requestusing customized UTP algorithm, which an independent system cannot do

Shielding against Web Application Attacks - Detection Techniques and Classification

The field of IoT web applications is facing a range of security risks and system attacks due to the increasing complexity and size of home automation datasets. One of the primary concerns is the identification of Distributed Denial of Service (DDoS) attacks in home automation systems. Attackers can easily access various IoT web application assets by entering a home automation dataset or clicking a link, making them vulnerable to different types of web attacks. To address these challenges, the cloud has introduced the Edge of Things paradigm, which uses multiple concurrent deep models to enhance system stability and enable easy data revelation updates. Therefore, identifying malicious attacks is crucial for improving the reliability and security of IoT web applications. This paper uses a Machine Learning algorithm that can accurately identify web attacks using unique keywords. Smart home devices are classified into four classes based on their traffic predictability levels, and a neural system recognition model is proposed to classify these attacks with a high degree of accuracy, outperforming other classification models. The application of deep learning in identifying and classifying attacks has significant theoretical and scientific value for web security investigations. It also provides innovative ideas for intelligent security detection by classifying web visitors, making it possible to identify and prevent potential security threats