An automated black box approach for web vulnerability identification and attack scenario generation

Akrout, R.; Alata, Éric; Kaâniche, Mohamed; Nicomette, Vincent

doi:10.1186/1678-4804-20-4

Cited by 25 publications

(15 citation statements)

References 6 publications

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“…The methodology presented in the article is not intended to be applied in documents explaining methodologies (e.g. Akrout et al [1]). The methodology is related to the documentation of their pragmatic application at the operators level, with intra-organization scope.…”

Section: Discussionmentioning

confidence: 99%

Semantics for incident identification and resolution reports

et al. 2016

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In order to achieve a safe and systematic treatment of security protocols, organizations release a number of technical briefings describing how to detect and manage security incidents. A critical issue is that this document set may suffer from semantic deficiencies, mainly due to ambiguity or different granularity levels of description and analysis. An approach to face this problem is the use of semantic methodologies in order to provide better Knowledge Externalization from incident protocols management. In this article, we propose a method based on semantic techniques for both, analyzing and specifying (meta)security requirements on protocols used for solving security incidents. This would allow specialist getting better documentation on their intangible knowledge about them.

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Section: Discussionmentioning

confidence: 99%

Semantics for incident identification and resolution reports

et al. 2016

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“…Online detection is also called real-time detection, which means a scanner will be immediately alerted and processed once its scanning behavior is detected. In addition, some literatures focus on the design and implementation of scanners [3][4][5][6][7][8], which are helpful to understand the mechanism of these scanners and to extract the fingerprint and behavioral characteristics of scanners.…”

Section: Related Workmentioning

confidence: 99%

Web Scanner Detection Based on Behavioral Differences

Lin

Wang

et al. 2019

Communications in Computer and Information Science

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Web scanners will not only take up the bandwidth of the server, but also collect sensitive information of websites and probe vulnerabilities of the system, which seriously threaten the security of websites. Accurate detection of Web scanners can effectively mitigate this kind of thread. Existing scanner detection methods extract features from log and differentiate between scanners and legal users with machine learning. However, these methods are unable to block scanning due to lack of behavior information of clients. To solve this problem, a Web scanner detection method based on behavioral differences is proposed. It collects request information and behavior information of clients by three modules named Passive Detection, Active Injection and Active Detection. Then, six kinds of features including fingerprint of scanners and execution ability of JavaScript code are extracted to detect whether a client is a scanner. This method makes full use of the behavior characteristics of clients and the behavioral differences between scanners and legal users. The experimental results showed the method is efficient and fast in scanner detection.

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“…Based on clustering techniques, a methodology has been developed aiming to identify web application vulnerability. They developed Wasapy vulnerability scanner and compared the result with W3af 1.1, Skipfish 1.9.6b, and Wapiti 2.2.1 focusing on code injection type vulnerability [9]. A method proposed to create test input using attack pattern with applying permutation and combination algorithm for several SQL injection [23].A sample prototype implementation with Open Web Application Security (OWASP) enterprise security application API based on Rapid Application Development (RAD) methodology to minimize web application flaws and prevent from critical malicious attacks [24].…”

Section: Literature Reviewmentioning

confidence: 99%

SAISAN: An Automated Local File Inclusion Vulnerability Detection Model

Hassan¹,

Bhuyian²,

Sohel³

et al. 2018

IJET

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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.

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An automated black box approach for web vulnerability identification and attack scenario generation

Cited by 25 publications

References 6 publications

Semantics for incident identification and resolution reports

Semantics for incident identification and resolution reports

Web Scanner Detection Based on Behavioral Differences

SAISAN: An Automated Local File Inclusion Vulnerability Detection Model

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