51 research outputs found
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
A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm
As the growing interest of web recommendation systems those are applied to
deliver customized data for their users, we started working on this system.
Generally the recommendation systems are divided into two major categories such
as collaborative recommendation system and content based recommendation system.
In case of collaborative recommen-dation systems, these try to seek out users
who share same tastes that of given user as well as recommends the websites
according to the liking given user. Whereas the content based recommendation
systems tries to recommend web sites similar to those web sites the user has
liked. In the recent research we found that the efficient technique based on
asso-ciation rule mining algorithm is proposed in order to solve the problem of
web page recommendation. Major problem of the same is that the web pages are
given equal importance. Here the importance of pages changes according to the
fre-quency of visiting the web page as well as amount of time user spends on
that page. Also recommendation of newly added web pages or the pages those are
not yet visited by users are not included in the recommendation set. To
over-come this problem, we have used the web usage log in the adaptive
association rule based web mining where the asso-ciation rules were applied to
personalization. This algorithm was purely based on the Apriori data mining
algorithm in order to generate the association rules. However this method also
suffers from some unavoidable drawbacks. In this paper we are presenting and
investigating the new approach based on weighted Association Rule Mining
Algorithm and text mining. This is improved algorithm which adds semantic
knowledge to the results, has more efficiency and hence gives better quality
and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table
- …