3,177 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
Static analysis for facilitating secure and reliable software
Software security and reliability are aspects of major concern for software development enterprises that wish to deliver dependable software to their customers. Several static analysis-based approaches for facilitating the development of secure and reliable software have been proposed over the years. The purpose of the present thesis is to investigate these approaches and to extend their state of the art by addressing existing open issues that have not been sufficiently addressed yet. To this end, an empirical study was initially conducted with the purpose to investigate the ability of software metrics (e.g., complexity metrics) to discriminate between different types of vulnerabilities, and to examine whether potential interdependencies exist between different vulnerability types. The results of the analysis revealed that software metrics can be used only as weak indicators of specific security issues, while important interdependencies may exist between different types of vulnerabilities. The study also verified the capacity of software metrics (including previously uninvestigated metrics) to indicate the existence of vulnerabilities in general. Subsequently, a hierarchical security assessment model able to quantify the internal security level of software products, based on static analysis alerts and software metrics is proposed. The model is practical, since it is fully-automated and operationalized in the form of individual tools, while it is also sufficiently reliable since it was built based on data and well-accepted sources of information. An extensive evaluation of the model on a large volume of empirical data revealed that it is able to reliably assess software security both at product- and at class-level of granularity, with sufficient discretion power, while it may be also used for vulnerability prediction. The experimental results also provide further support regarding the ability of static analysis alerts and software metrics to indicate the existence of software vulnerabilities. Finally, a mathematical model for calculating the optimum checkpoint interval, i.e., the checkpoint interval that minimizes the execution time of software programs that adopt the application-level checkpoint and restart (ALCR) mechanism was proposed. The optimum checkpoint interval was found to depend on the failure rate of the application, the execution cost for establishing a checkpoint, and the execution cost for restarting a program after failure. Emphasis was given on programs with loops, while the results were illustrated through several numerical examples.Open Acces
Automatic Software Repair: a Bibliography
This article presents a survey on automatic software repair. Automatic
software repair consists of automatically finding a solution to software bugs
without human intervention. This article considers all kinds of repairs. First,
it discusses behavioral repair where test suites, contracts, models, and
crashing inputs are taken as oracle. Second, it discusses state repair, also
known as runtime repair or runtime recovery, with techniques such as checkpoint
and restart, reconfiguration, and invariant restoration. The uniqueness of this
article is that it spans the research communities that contribute to this body
of knowledge: software engineering, dependability, operating systems,
programming languages, and security. It provides a novel and structured
overview of the diversity of bug oracles and repair operators used in the
literature
PreciseBugCollector: Extensible, Executable and Precise Bug-fix Collection
Bug datasets are vital for enabling deep learning techniques to address
software maintenance tasks related to bugs. However, existing bug datasets
suffer from precise and scale limitations: they are either small-scale but
precise with manual validation or large-scale but imprecise with simple commit
message processing. In this paper, we introduce PreciseBugCollector, a precise,
multi-language bug collection approach that overcomes these two limitations.
PreciseBugCollector is based on two novel components: a) A bug tracker to map
the codebase repositories with external bug repositories to trace bug type
information, and b) A bug injector to generate project-specific bugs by
injecting noise into the correct codebases and then executing them against
their test suites to obtain test failure messages.
We implement PreciseBugCollector against three sources: 1) A bug tracker that
links to the national vulnerability data set (NVD) to collect general-wise
vulnerabilities, 2) A bug tracker that links to OSS-Fuzz to collect
general-wise bugs, and 3) A bug injector based on 16 injection rules to
generate project-wise bugs. To date, PreciseBugCollector comprises 1057818 bugs
extracted from 2968 open-source projects. Of these, 12602 bugs are sourced from
bug repositories (NVD and OSS-Fuzz), while the remaining 1045216
project-specific bugs are generated by the bug injector. Considering the
challenge objectives, we argue that a bug injection approach is highly valuable
for the industrial setting, since project-specific bugs align with domain
knowledge, share the same codebase, and adhere to the coding style employed in
industrial projects.Comment: Accepted at the industry challenge track of ASE 202
Enhancing web application security through automated penetration testing with multiple vulnerability scanners.
Penetration testers have increasingly adopted multiple penetration testing scanners to ensure the robustness of web applications. However, a notable limitation of many scanning techniques is their susceptibility to producing false positives. This paper presents a novel framework designed to automate the operation of multiple Web Application Vulnerability Scanners (WAVS) within a single platform. The framework generates a combined vulnerabilities report using two algorithms: an automation algorithm and a novel combination algorithm that produces comprehensive lists of detected vulnerabilities. The framework leverages the capabilities of two web vulnerability scanners, Arachni and OWASP ZAP. The study begins with an extensive review of the existing scientific literature, focusing on open-source WAVS and exploring the OWASP 2021 guidelines. Following this, the framework development phase addresses the challenge of varying results obtained from different WAVS. This framework’s core objective is to combine the results of multiple WAVS into a consolidated vulnerability report, ultimately improving detection rates and overall security. The study demonstrates that the combined outcomes produced by the proposed framework exhibit greater accuracy compared to individual scanning results obtained from Arachni and OWASP ZAP. In summary, the study reveals that the Union List outperforms individual scanners, particularly regarding recall and F-measure. Consequently, adopting multiple vulnerability scanners is recommended as an effective strategy to bolster vulnerability detection in web applications
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