Showing papers on "File inclusion vulnerability published in 2018"
TL;DR: A tool called GMSA, developed to detect a variety of CIAs, which is more comprehensive than other research techniques that are restricted to only two major types of CIA, namely, SQL injection and XSS attacks.
Abstract: Code injection attacks (CIAs) exploit security vulnerabilities and computer bugs that are caused by processing invalid codes. CIA is a problem which hackers attempt to introduce to any new method, their objective being to bypass the protection system. In this paper, we present a tool called GMSA, developed to detect a variety of CIAs, for example, cross-site scripting (XSS) attack, SQL injection attack, shell injection attack (command injection attack), and file inclusion attack. The latter consists of local file inclusion and remote file inclusion. Our empirical analysis reveals that compared with existing research, gathering multiple signatures approach (GMSA) executes a precision performance (accuracy of the proposed algorithm is 99.45%). The false positive rate (FPR) of GMSA is 0.59%, which is low compared with what other research has reported. The low FPR is the most important factor. Ideally, the defense algorithm should balance between the FPR and true positive rate (TPR) because with existing methodologies, security experts can defend against a broad range of CIAs with uncomplicated security software. Typical protection methods yield a high FPR. Our method results in high TPR while minimizing the resources needed to address the false positive. GMSA can detect four types of CIA. This is more comprehensive than other research techniques that are restricted to only two major types of CIA, namely, SQL injection and XSS attacks.
16 citations
TL;DR: An automated LFI vulnerability detection model, SAISAN for web applications is proposed and implemented through a tool and received 88% accuracy from the tool comparing with the manual penetration testing method.
Abstract: Communicating and delivering services to the consumers through web applications are now become very popular due to its user friendly interface, global accessibility, and easy manageability. Careless design and development of web applications are the key reasons for security breaches which are very alarming for the users as well as the web administrators. Currently, Local File Inclusion (LFI) vulnerability is found present commonly in several web applications that lead to remote code execution in host server and initiates sensitive information disclosure. Detection of LFI vulnerability is getting very critical concern for the web owner to take effective measures to mitigate the risk. After reviewing literatures, we found insignificant researches conducted on automated detection of LFI vulnerability. This paper has proposed an automated LFI vulnerability detection model, SAISAN for web applications and implemented it through a tool. 265 web applications of four different sectors has been examined and received 88% accuracy from the tool comparing with the manual penetration testing method.
13 citations
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TL;DR: In this article, the authors have surveyed literatures to study the general mechanics of VAPT process and gather tools which can be useful during the VAPTs process, such as SQL injection, cross-site scripting, local file inclusion and remote file inclusion.
Abstract: By taking advantage of vulnerability, Cyber criminals is easily able to steal confidential data of the ICT, results in heavy loss. Vulnerability Assessment and penetration testing is a special approach to eliminate various security threats from the web application. By focusing high risk vulnerability such as SQL Injection, Cross Site Scripting, Local File Inclusion and Remote File Inclusion, in this paper, we have surveyed literatures to study the general mechanics of VAPT process and gather tools which can be useful during VAPT process.
3 citations
01 Dec 2018
TL;DR: A Finite State Machine (FSM) attacking model is developed, which analyzes a set of vulnerabilities towards the road to finding connections among various vulnerabilities such as cross-site scripting, local file inclusion, remotefile inclusion, buffer overflow CSRF, etc.
Abstract: Recent emergence of new vulnerabilities is an epoch-making problem in the complex world of website security. Most of the websites are failing to keep updating to tackle their websites from these new vulnerabilities leaving without realizing the weakness of the websites. As a result, when cyber-criminals scour such vulnerable old version websites, the scanner will represent a set of vulnerabilities. Once found, these vulnerabilities are then exploited to steal data, distribute malicious content, or inject defacement and spam content into the vulnerable websites. Furthermore, a combination of different vulnerabilities is able to cause more damages than anticipation. Therefore, in this paper, we endeavor to find connections among various vulnerabilities such as cross-site scripting, local file inclusion, remote file inclusion, buffer overflow CSRF, etc. To do so, we develop a Finite State Machine (FSM) attacking model, which analyzes a set of vulnerabilities towards the road to finding connections. We demonstrate the efficacy of our model by applying it to the set of vulnerabilities found on two live websites.