• 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.

• 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.

• 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.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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1689-1705
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• 2023

To deal with the potential XSS vulnerabilities in the source code of the power communication network, an XSS vulnerability detection method combining the static analysis method with the dynamic testing method is proposed. The static analysis method aims to analyze the structure and content of the source code. We construct a set of feature expressions to match malignant content and set a "variable conversion" method to analyze the data flow of the code that implements interactive functions. The static analysis method explores the vulnerabilities existing in the source code structure and code content. Dynamic testing aims to simulate network attacks to reflect whether there are vulnerabilities in web pages. We construct many attack vectors and implemented the test in the Selenium tool. Due to the combination of the two analysis methods, XSS vulnerability discovery research could be conducted from two aspects: "white-box testing" and "black-box testing". Tests show that this method can effectively detect XSS vulnerabilities in the source code of the power communication network.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06d
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• pp.102-104
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• 2011

최근 스마트폰 사용자가 빠른 속도로 늘어나면서 'QR(Quick Response)코드가 새로운 마케팅 및 정보의 전달 수단으로 크게 각광받고 있다. 또한, QR코드는 인터넷 주소(URL), 사진 및 동영상 정보, 지도 정보, 명함 정보 등을 제공하는 매우 효율적인 수단으로 작용하고 있다. 하지만 스마트폰으로 무심코 인식한 QR 코드로 인해 악성코드에 감염될 가능성이 높아 사용자 주의가 필요하다. 로그인된 웹 사이트에서 QR 코드를 읽어 웹 브라우저로 접근할 때 XSS(Cross Site Scripting)을 통해 해당 웹사이트의 로그인 정보를 획득하거나 게시판 회원정보와 같은 데이터를 수정할 수도 있기 때문이다. 이에 본 논문에서는 QR 코드의 XSS 공격에 대한 대응방안을 상세히 기술하여 QR 코드를 사용하는 유저들에게 도움이 되고자 한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.669-677
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• 2020

As the number of Internet users exploded, attacks on the web increased. In addition, the attack patterns have been diversified to bypass existing defense techniques. Traditional web firewalls are difficult to detect attacks of unknown patterns.Therefore, the method of detecting abnormal behavior by artificial intelligence has been studied as an alternative. Specifically, attempts have been made to apply natural language processing techniques because the type of script or query being exploited consists of text. However, because there are many unknown words in scripts and queries, natural language processing requires a different approach. In this paper, we propose a new classification model which uses byte pair encoding (BPE) technology to learn the embedding vector, that is often used for web attack payloads, and uses an attention mechanism-based Bi-GRU neural network to extract a set of tokens that learn their order and importance. For major web attacks such as SQL injection, cross-site scripting, and command injection attacks, the accuracy of the proposed classification method is about 0.9990 and its accuracy outperforms the model suggested in the previous study.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.57-61
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• 2014

Zeroboard is a public bulletin board that can support PHP and MySQL. It has been used by many people because it is easy to use, but there is no more updates after Zeroboard4. So, there is a problem that its administrator will have nothing to do about it if zeroboard has a vulnerability. In this paper, we will discuss about CSRF(Cross Site request Forgery) which is developed and expanded by XSS(Cross Site Scripting). Also, we will find CSRF attacks and suggest an alternative method using VM-ware. The main features and contributions of the proposed method are as follows. First, make an environment construction using VM-ware and other tools. Second, analyze and prepare vulnerabilities using Proxy server. Performance evaluation will be conducted by applying possible countermeasure.

• Convergence Security Journal
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• v.18 no.3
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• pp.37-44
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• 2018

Today we live in a flood of web sites and access numerous websites through the Internet to obtain various information. However, unless the security of the Web site is secured, Web site security can not be secured from various malicious attacks. Hacking attacks, which exploit Web site security vulnerabilities for various reasons, such as financial and political purposes, are increasing. Various attack techniques such as SQL-injection, Cross-Site Scripting(XSS), and Drive-By-Download are being used, and the technology is also evolving. In order to defend against these various hacking attacks, it is necessary to remove the vulnerabilities from the development stage of the website, but it is not possible due to various problems such as time and cost. In order to compensate for this, it is important to identify vulnerabilities in Web sites through web vulnerability checking and take action. In this paper, we investigate web vulnerabilities and diagnostic techniques and try to understand the priorities of vulnerabilities in the development stage according to the actual status of each case through cases of actual web vulnerability diagnosis.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.199-202
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• 2023

Carriage return (CR) and line feed (LF), also known as CRLF injection is a type of vulnerability that allows a hacker to enter special characters into a web application, altering its operation or confusing the administrator. Log poisoning and HTTP response splitting are two prominent harmful uses of this technique. Additionally, CRLF injection can be used by an attacker to exploit other vulnerabilities, such as cross-site scripting (XSS). Email injection, also known as email header injection, is another way that can be used to modify the behavior of emails. The Open Web Application Security Project (OWASP) is an organization that studies vulnerabilities and ranks them based on their level of risk. According to OWASP, CRLF vulnerabilities are among the top 10 vulnerabilities and are a type of injection attack. Automated testing can help to quickly identify CRLF vulnerabilities, and is particularly useful for companies to test their applications before releasing them. However, CRLF vulnerabilities can also lead to the discovery of other high-risk vulnerabilities, and it fosters a better approach to mitigate CRLF vulnerabilities in the early stage and help secure applications against known vulnerabilities. Although there has been a significant amount of research on other types of injection attacks, such as Structure Query Language Injection (SQL Injection). There has been less research on CRLF vulnerabilities and how to detect them with automated testing. There is room for further research to be done on this subject matter in order to develop creative solutions to problems. It will also help to reduce false positive alerts by checking the header response of each request. Security automation is an important issue for companies trying to protect themselves against security threats. Automated alerts from security systems can provide a quicker and more accurate understanding of potential vulnerabilities and can help to reduce false positive alerts. Despite the extensive research on various types of vulnerabilities in web applications, CRLF vulnerabilities have only recently been included in the research. Utilizing automated testing as a recurring task can assist companies in receiving consistent updates about their systems and enhance their security.