• Convergence Security Journal
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• v.13 no.6
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• pp.83-89
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• 2013

When applying web hacking techniques and methods it needs to configure the integrated step-by-step and run an information security. Web hackings rely upon only one way to respond to any security holes that can cause a lot. In this study the diagnostic process of cross-site scripting attacks and web hacking response procedures are designed. Response system is a framework for configuring and running a step-by-step information security. Step response model of the structure of the system design phase, measures, operational step, the steps in the method used. It is designed to secure efficiency of design phase of the system development life cycle, and combines the way in secure coding. In the use user's step, the security implementation tasks to organize the details. The methodology to be applied to the practice field if necessary, a comprehensive approach in the field can be used as a model methodology.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.125-142
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• 2021

Reports of rampant cross-site scripting (XSS) vulnerabilities raise growing concerns on the effectiveness of current Static Analysis Security Testing (SAST) tools as an internet security device. Attentive to these concerns, this study aims to examine seven open-source SAST tools in order to account for their capabilities in detecting XSS vulnerabilities in PHP applications and to determine their performance in terms of effectiveness and analysis runtime. The representative tools - categorized as either text-based or graph-based analysis tools - were all test-run using real-world PHP applications with known XSS vulnerabilities. The collected vulnerability detection reports of each tool were analyzed with the aid of PhpStorm's data flow analyzer. It is observed that the detection rates of the tools calculated from the total vulnerabilities in the applications can be as high as 0.968 and as low as 0.006. Furthermore, the tools took an average of less than a minute to complete an analysis. Notably, their runtime is independent of their analysis type.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.22-29
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• 2021

For web application vulnerability diagnosis, from the development stage to the operation stage, it is possible to stably operate the web only when there is a policy that is commonly applied to each task through diagnosis of vulnerabilities, removal of vulnerabilities, and rapid recovery from web page damage. KISA presents 28 evaluation items for technical vulnerability analysis of major information and communication infrastructure. In this paper, we diagnose the vulnerabilities in the automobile goods shopping mall website and suggest security measures according to the vulnerabilities. As a result of diagnosing 28 items, major vulnerabilities were found in three items: cross-site scripting, cross-site request tampering, and insufficient session expiration. Cookie values were exposed on the bulletin board, and personal information was exposed in the parameter values related to passwords when personal information was edited. Also, since the session end time is not set, it was confirmed that session reuse is always possible. By suggesting security measures according to these vulnerabilities, the discovered security threats were eliminated, and it was possible to prevent breaches in web applications and secure the stability of web services.

• Journal of Digital Convergence
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• v.13 no.2
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• pp.177-183
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• 2015

Cross Site Scripting enables hackers to steal other user's information (such as cookie, session etc.) or to do abnormal functions automatically using vulnerability of web application. This attack patterns of Cross Site Scripting(XSS) can be divided into two types. One is Reflect XSS which can be executed in one request for HTTP and its reply, and the other is Stored XSS which attacks those many victim users whoever access to the page which accepted the payload transmitted. To correspond to these XSS attacks, some measures have been suggested. They are data validation for user input, output validation during HTML encoding procedures, and removal of possible risk injection point to prevent from trying to insert malicious code into web application. In this paper, the methods and procedures for these two types are explained and a penetration testing is done. With these suggestions, the attack by XSS could be understood and prepared by its countermeasures.

• ETRI Journal
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• v.38 no.2
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• pp.376-386
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• 2016

Currently, web applications are gaining in prevalence. In a web application, an input may not be appropriately validated, making the web application susceptible to cross-site scripting (XSS), which poses serious security problems for Internet users and websites to whom such trusted web pages belong. A taint inference is a type of information flow analysis technique that is useful in detecting XSS on the client side. However, in existing techniques, two current practical issues have yet to be handled properly. One is URL rewriting, which transforms a standard URL into a clearer and more manageable form. Another is HTML sanitization, which filters an input against blacklists or whitelists of HTML tags or attributes. In this paper, we make an analogy between the taint inference problem and the molecule sequence alignment problem in bioinformatics, and transfer two techniques related to the latter over to the former to solve the aforementioned yet-to-be-handled-properly practical issues. In particular, in our method, URL rewriting is addressed using local sequence alignment and HTML sanitization is modeled by introducing a removal gap penalty. Empirical results demonstrate the effectiveness and efficiency of our method.

• The KIPS Transactions:PartA
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• v.15A no.3
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• pp.181-188
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• 2008

Nowadays, most web sites are developed using dynamic web pages where web pages are generated and transmitted by web application programs. Therefore, the ratio of attacks injecting malevolent strings to vulnerable web applications is increasing. In this paper, we present a static program analyzer which analyzes whether a web application program has vulnerabilities to the SQL injection attack and the cross site scripting(XSS) attack. To analyze programs using abstract interpretation framework, we designed an abstract domain which models potential string set along with excluded strings and developed an abstract interpreter for the PHP language. Also, based on them, we implemented a static analyzer. According to our experiments, our analyzer has competitive analysis speed and accuracy compared with related research results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4008-4023
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• 2022

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.

• Convergence Security Journal
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• v.12 no.4
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• pp.71-76
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• 2012

As the use of Mashups, web3.0, JavaScript and AJAX(Asynchronous JavaScript XML) widely increases, the new security threats for web vulnerability also increases when the web application services are provided. In order to previously diagnose the vulnerability and prepare the threats, in this paper, the classification of security threats and requirements are presented, and the web vulnerability is analyzed for the domestic web sites using WVS(Web Vulnerability Scanner) automatic evaluation tool. From the results of vulnerability such as XSS(Cross Site Scripting) and SQL Injection, the total alerts are distributed from 0 to 31,177, mean of 411, and standard deviation of 2,563. The results also show that the web sites of 22.5% for total web sites has web vulnerability, and the previous defenses for the security threats are required.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.1014-1028
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• 2017

Social networking services (SNSs) such as Twitter, MySpace, and Facebook have become progressively significant with its billions of users. Still, alongside this increase is an increase in security threats such as cross-site scripting (XSS) threat. Recently, a few approaches have been proposed to detect an XSS attack on SNSs. Due to the certain recent features of SNSs webpages such as JavaScript and AJAX, however, the existing approaches are not efficient in combating XSS attack on SNSs. In this paper, we propose a machine learning-based approach to detecting XSS attack on SNSs. In our approach, the detection of XSS attack is performed based on three features: URLs, webpage, and SNSs. A dataset is prepared by collecting 1,000 SNSs webpages and extracting the features from these webpages. Ten different machine learning classifiers are used on a prepared dataset to classify webpages into two categories: XSS or non-XSS. To validate the efficiency of the proposed approach, we evaluated and compared it with other existing approaches. The evaluation results show that our approach attains better performance in the SNS environment, recording the highest accuracy of 0.972 and lowest false positive rate of 0.87.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3750-3770
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• 2021

This study investigates open-source dynamic XSS filters used as security devices in web applications to account for the effectiveness of filters in protecting against XSS attacks. The experiment involves twelve representative filters, which are examined individually by placing them into the final output function of a custom-built single-input-form web application. To assess the effectiveness of the filters in their tasks of sanitizing XSS payloads and in preserving benign payloads, a black-box testing method is applied using an automated XSS testing framework. The result in working with malicious and benign payloads shows an important trade-off in the filters' tasks. Because the filters that only check for dangerous or safe elements, they seem to neglect to validate their values. As some safe values are mistreated as dangerous elements, their benign payload function is lost in the way. For the filters to be more effective, it is suggested that they should be able to validate the respective values of malicious and benign payloads; thus, minimizing the trade-off. This particular assessment of XSS filters provides important insight regarding the filters that can be used to mitigate threats, including the possible configurations to improve them in handling both malicious and benign payloads.