Cross-site scripting (XSS) attacks and mitigation: A survey

The advent of online social networks (OSN) has transformed a common passive reader into a content contributor. It has allowed users to share information and exchange opinions, and also express themselves in online virtual communities to interact with other users of similar interests. However, OSN have turned the social sphere of users into the commercial sphere. This should create a privacy and security issue for OSN users. OSN service providers collect the private and sensitive data of their customers that can be misused by data collectors, third parties, or by unauthorized users. In this paper, common security and privacy issues are explained along with recommendations to OSN users to protect themselves from these issues whenever they use social media.

https://www.mdpi.com/1999-5903/10/12/114/pdf

Privacy and Security Issues in Online Social Networks

Nowadays, Cross Site Scripting (XSS) is one of the major threats to Web applications. Since it's known to the public, XSS vulnerability has been in the TOP 10 Web application vulnerabilities based on surveys published by the Open Web Applications Security Project (OWASP). How to effectively detect and defend XSS attacks are still one of the most important security issues. In this paper, we present a novel approach to detect XSS attacks based on deep learning (called DeepXSS). First of all, we used word2vec to extract the feature of XSS payloads which captures word order information and map each payload to a feature vector. And then, we trained and tested the detection model using Long Short Term Memory (LSTM) recurrent neural networks. Experimental results show that the proposed XSS detection model based on deep learning achieves a precision rate of 99.5% and a recall rate of 97.9% in real dataset, which means that the novel approach can effectively identify XSS attacks.

DeepXSS: Cross Site Scripting Detection Based on Deep Learning

A survey of detection methods for XSS attacks

Cross site scripting (XSS) is a type of scripting attack on web pages and account as one of the unsafe vulnerability existed in web applications. Once the vulnerability is oppressed, an intruder advances intended access of the authenticate user's web-browser and may perform session-hijacking, cookie-stealing, malicious redirection and malware-spreading. As prevention against such attacks, it is essential to implement security measures that certainly block the third party intrusion. Recently the most dangerous attacks are reflected and DOM based cross-site scripting attacks because in both cases attacker attack using server side scripting and do forgery over the network, it is hard to detect and therefore it must be prevented. Vulnerabilities of websites are exploited over the network through web request using GET and POST method. In this paper, we are focusing on injection, detection, and prevention of stored based XSS reflected XSS and DOM based XSS.

XSS vulnerability assessment and prevention in web application

In present-day time, securing the web application against hacking is a big challenge. One of the common types of hacking technique to attack the web application is Cross-Site Scripting (XSS). Cross-Site Scripting (XSS) vulnerabilities are being exploited by the attackers to steal web browser’s resources such as cookies, credentials etc. by injecting the malicious JavaScript code on the victim’s web applications. Since Web browsers support the execution of commands embedded in Web pages to enable dynamic Web pages attackers can make use of this feature to enforce the execution of malicious code in a user’s Web browser. The analysis of detection and prevention of Cross-Site Scripting (XSS) help to avoid this type of attack. We describe a technique to detect and prevent this ki nd of manipulation and hence eliminate Cross-Site Scripting attack.

http://www.sersc.org/journals/IJSIA/vol9_no3_2015/14.pdf

A Survey on Detection and Prevention of Cross-Site Scripting Attack

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A Survey on Detection and Prevention of Cross-Site Scripting Attack