You shall not pass: Mitigating SQL Injection Attacks on Legacy Web Applications

SQL injection (SQLi) attacks pose a significant threat to the security of web applications. Existing approaches do not support object-oriented programming that renders these approaches unable to protect the real-world web apps such as Wordpress, Joomla, or Drupal against SQLi attacks. We propose a novel hybrid static-dynamic analysis for PHP web applications that limits each PHP function for accessing the database. Our tool, SQLBlock, reduces the attack surface of the vulnerable PHP functions in a web application to a set of query descriptors that demonstrate the benign functionality of the PHP function. We implement SQLBlock as a plugin for MySQL and PHP. Our approach does not require any modification to the web app. W evaluate SQLBlock on 11 SQLi vulnerabilities in Wordpress, Joomla, Drupal, Magento, and their plugins. We demonstrate that SQLBlock successfully prevents all 11 SQLi exploits with negligible performance overhead (i.e., a maximum of 3% on a heavily-loaded web server)Comment: Accepted in ASIACCS 202

A Framework for Hybrid Intrusion Detection Systems

Web application security is a definite threat to the world’s information technology infrastructure. The Open Web Application Security Project (OWASP), generally defines web application security violations as unauthorized or unintentional exposure, disclosure, or loss of personal information. These breaches occur without the company’s knowledge and it often takes a while before the web application attack is revealed to the public, specifically because the security violations are fixed. Due to the need to protect their reputation, organizations have begun researching solutions to these problems. The most widely accepted solution is the use of an Intrusion Detection System (IDS). Such systems currently rely on either signatures of the attack used for the data breach or changes in the behavior patterns of the system to identify an intruder. These systems, either signature-based or anomaly-based, are readily understood by attackers. Issues arise when attacks are not noticed by an existing IDS because the attack does not fit the pre-defined attack signatures the IDS is implemented to discover. Despite current IDSs capabilities, little research has identified a method to detect all potential attacks on a system. This thesis intends to address this problem. A particular emphasis will be placed on detecting advanced attacks, such as those that take place at the application layer. These types of attacks are able to bypass existing IDSs, increase the potential for a web application security breach to occur and not be detected. In particular, the attacks under study are all web application layer attacks. Those included in this thesis are SQL injection, cross-site scripting, directory traversal and remote file inclusion. This work identifies common and existing data breach detection methods as well as the necessary improvements for IDS models. Ultimately, the proposed approach combines an anomaly detection technique measured by cross entropy and a signature-based attack detection framework utilizing genetic algorithm. The proposed hybrid model for data breach detection benefits organizations by increasing security measures and allowing attacks to be identified in less time and more efficiently

Automatic Creation of SQL Injection and Cross-Site Scripting Attacks

We present a technique for finding security vulnerabilitiesin Web applications. SQL Injection (SQLI) and cross-sitescripting (XSS) attacks are widespread forms of attackin which the attacker crafts the input to the application toaccess or modify user data and execute malicious code. Inthe most serious attacks (called second-order, or persistent,XSS), an attacker can corrupt a database so as to causesubsequent users to execute malicious code.This paper presents an automatic technique for creatinginputs that expose SQLI and XSS vulnerabilities. The techniquegenerates sample inputs, symbolically tracks taintsthrough execution (including through database accesses),and mutates the inputs to produce concrete exploits. Oursis the first analysis of which we are aware that preciselyaddresses second-order XSS attacks.Our technique creates real attack vectors, has few falsepositives, incurs no runtime overhead for the deployed application,works without requiring modification of applicationcode, and handles dynamic programming-languageconstructs. We implemented the technique for PHP, in a toolArdilla. We evaluated Ardilla on five PHP applicationsand found 68 previously unknown vulnerabilities (23 SQLI,33 first-order XSS, and 12 second-order XSS)

SQLSCAN: A Framework to Check Web Application Vulnerability

Security vulnerabilities in web applications that are being found today are much higher than in any operating systems. So it clearly means that threats intended at web applications are utilizing vulnerabilities at the application. Simultaneously, amount and impact of security vulnerabilities on web applications has increases as well. Almost in all online transactions user access is authorized before providing access to database of application. But organized injection could provide entry to unauthorized users and it almost achieved via SQL injection and Cross-site scripting (XSS). In this article we provide a web vulnerability scanning and analyzing tool of various kinds of SQL injection and Cross Site Scripting (XSS) attacks named as SQLSCAN. Our proposed method will work with web application developed on any technology like PHP, JAVA, ASP .NET. We evaluate our proposed scanner by experiments to calculate its performance. We also evaluate the performance of SQLSCAN with performance of parallel tools in the literature. Keywords: Web Application security, Attack, Injection, SQL, XSS, Vulnerability, Scanner.

A survey on the application of deep learning for code injection detection

Abstract Code injection is one of the top cyber security attack vectors in the modern world. To overcome the limitations of conventional signature-based detection techniques, and to complement them when appropriate, multiple machine learning approaches have been proposed. While analysing these approaches, the surveys focus predominantly on the general intrusion detection, which can be further applied to specific vulnerabilities. In addition, among the machine learning steps, data preprocessing, being highly critical in the data analysis process, appears to be the least researched in the context of Network Intrusion Detection, namely in code injection. The goal of this survey is to fill in the gap through analysing and classifying the existing machine learning techniques applied to the code injection attack detection, with special attention to Deep Learning. Our analysis reveals that the way the input data is preprocessed considerably impacts the performance and attack detection rate. The proposed full preprocessing cycle demonstrates how various machine-learning-based approaches for detection of code injection attacks take advantage of different input data preprocessing techniques. The most used machine learning methods and preprocessing stages have been also identified

Survey on detecting and preventing web application broken access control attacks

Web applications are an essential component of the current wide range of digital services proposition including financial and governmental services as well as social networking and communications. Broken access control vulnerabilities pose a huge risk to that echo system because they allow the attacker to circumvent the allocated permissions and rights and perform actions that he is not authorized to perform. This paper gives a broad survey of the current research progress on approaches used to detect access control vulnerabilities exploitations and attacks in web application components. It categorizes these approaches based on their key techniques and compares the different detection methods in addition to evaluating their strengths and weaknesses. We also spotted and elaborated on some exciting research gaps found in the current literature, Finally, the paper summarizes the general detection approaches and suggests potential research directions for the future

Securing Web Applications from malware attacks using hybrid feature extraction

In this technological era, many of the applications are taking the utilization of services of internet in order to cater to the needs of its users. With the rise in number of internet users, there's a substantial inflation within the internet attacks. Because of this hike, Web Services give rise to new security threats. One among the major concerns is the susceptibility of the internet services for cross site scripting (XSS). More than three fourths of the malicious attacks are contributed by XSS. This article primarily focuses on detection and exploiting XSS vulnerabilities. Generally, improper sanitization of input results in these type of susceptibilities. This article primarily focuses on fuzzing, and brute forcing parameters for XSS vulnerability. In addition, we've mentioned the planned framework for contradicting XSS vulnerability