• Journal of Multimedia Information System
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• v.8 no.4
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• pp.259-266
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• 2021

Security vulnerabilities have been reported in major design software systems such as Adobe Photoshop and Illustrator, which are recognized as de facto standard design tools in most of the design industries. Companies need to evaluate and manage their risk levels posed by those vulnerabilities, so that they could mitigate the potential security bridges in advance. In general, security vulnerabilities are discovered throughout their life cycles repeatedly if software systems are continually used. Hence, in this study, we empirically analyze risk levels for the three major graphical design software systems, namely Photoshop, Illustrator and GIMP with respect to a software vulnerability discovery model. The analysis reveals that the Alhazmi-Malaiya Logistic model tends to describe the vulnerability discovery patterns significantly. This indicates that the vulnerability discovery model makes it possible to predict vulnerability discovery in advance for the software systems. Also, we found that none of the examined vulnerabilities requires even a single authentication step for successful attacks, which suggests that adding an authentication process in software systems dramatically reduce the probability of exploitations. The analysis also discloses that, for all the three software systems, the predictions with evenly distributed and daily based datasets perform better than the estimations with the datasets of vulnerability reporting dates only. The observed outcome from the analysis allows software development managers to prepare proactively for a hostile environment by deploying necessary resources before the expected time of vulnerability discovery. In addition, it can periodically remind designers who use the software systems to be aware of security risk, related to their digital work environments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.921-932
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• 2015

We proposed a new scoring system on software vulnerabilities, which calculates quantitatively the severity of software vulnerabilities. The proposed scoring system consists of metrics for vulnerability severity and scoring equations; the metrics are designed to measure the severity of a software vulnerability considering the prevalence of the vulnerability, the risk level of the vulnerability, the domestic market share of the software and the frequency of the software. We applied the proposed scoring system to domestically reported software vulnerabilities, and discussed the effectiveness of the scoring system, comparing it with CVSS and CWSS. We also suggested the prospective utilization areas of the proposed scoring system.

• Journal of Korea Multimedia Society
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• v.18 no.2
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• pp.199-206
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• 2015

Since security vulnerabilities newly discovered in a popular Web browser immediately put a number of users at risk, urgent attention from developers is required to address those vulnerabilities. Analysis of characteristics in the Web browser vulnerabilities can be used to assess security risks and to determine the resources needed to develop patches quickly to handle vulnerabilities discovered. So far, being a new research area, the quantitative aspects of the Web browser vulnerabilities and risk assessments have not been fully investigated. However, due to the importance of Web browser software systems, further detailed studies are required related to the Web browser risk assessment, using rigorous analysis of actual data which can assist decision makers to maximize the returns on their security related efforts. In this paper, quantitative software vulnerability analysis has been presented for major Web browsers with respect to the Common Vulnerability Scoring System. Further, vulnerability discovery trends in the Web browsers are also investigated. The results show that, almost all the time, vulnerabilities are compromised from remote networks with no authentication required systems. It is also found that a vulnerability discovery model which was originally introduced for operating systems is also applicable to the Web browsers.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.77-83
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• 2021

As Open Source Software(OSS) recently invigorates, many companies actively use the OSSes in their business software. With such OSS invigoration, our web application is developed in order to provide the safety in using the OSSes, and update the information on the new vulnerabilities and the patches at all times by crawling the web pages of the relevant OSS home pages and the managing organizations of the vulnerabilities. By providing the updated information, our application helps the OSS users and developers to be aware of such security issues, and gives them to work in the safer environment from security risks. In addition, our application can be used as a security platform to greatly contribute to preventing potential security incidents not only for companies but also for individual developers.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.48-60
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• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.354-357
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• 2021

Software vulnerabilities are becoming more and more increasing, their role is to harm the computer systems of companies, governmental organizations and agencies. The main objective of this paper is to propose a method that will cluster future software vulnerabilities that may spread. This method is developed by combining the Multiple Correspondence Analysis (MCA), the Elbow procedure and the Kmeans Algorithm. A simulation was done on a dataset of 15713 observations. This simulation allowed us to identify families of future vulnerabilities. This model was evaluated using the silhouette index.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1449-1461
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• 2018

As the number of software vulnerabilities grows year by year, attacks on software are also taking place a lot. As a result, the security administrator must identify and patch vulnerabilities in the software. However, it is important to prioritize the patches because patches for all vulnerabilities are realistically hard. In this paper, we propose a scoring system that expands the scale of risk assessment metric by taking into consideration attack patterns or weaknesses cause vulnerabilities with the vulnerability information provided by the NIST(National Institute of Standards and Technology). The proposed scoring system is expanded based on the CWSS and uses only public vulnerability information to utilize easily for any company. In this paper, we applied the automated scoring system to software vulnerabilities, and showed the expanded metrics with consideration for influence of attack pattern and weakness are meaningful.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.635-642
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• 2018

In the Era of the Fourth Industrial Revolution, we live in huge amounts of software. However, as software increases, software vulnerabilities are also increasing. Therefore, it is important to detect and remove software vulnerabilities. Currently, many researches have been studied to predict and detect software security problems, but it takes a long time to detect and does not have high prediction accuracy. Therefore, in this paper, we describe a method for efficiently predicting software vulnerabilities using machine learning algorithms. In addition, various machine learning algorithms are compared through experiments. Experimental results show that the k-nearest neighbors prediction model has the highest prediction rate.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.961-974
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• 2015

With the advancement of SIEM from ESM, it allows deep correlated analysis using huge amount of data. By collecting software's vulnerabilities from assessment with certain classification measures (e.g., CWE), it can improve detection rate effectively, and respond to software's vulnerabilities by analyzing big data. In the phase of monitoring and vulnerability diagnosis Process, it not only detects predefined threats, but also vulnerabilities of software in each resources could promptly be applied by sharing CCE, CPE, CVE and CVSS information. This abstract proposes a model for effective detection and response of software vulnerabilities and describes effective outcomes of the model application.