• Journal of IKEEE
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• v.16 no.4
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• pp.389-394
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• 2012

Most current systems have ignored security vulnerability concerned with boot firmware. It is highly likely that boot firmware may cause serious system errors, such as hardware manipulations by malicious programs or code, the operating system corruption caused by malicious code and software piracy under a condition of no consideration of security mechanism because boot firmware has an authority over external devices as well as hardware controls. This paper proposed a structural security mechanism based on software equipped with encrypted bootstrap patterns different from pre-existing bootstrap methods in terms of securely loading an operating system, searching for malicious codes and preventing software piracy so as to provide reliability of boot firmware. Moreover, through experiments, it proved its superiority in detection capability and overhead ranging between 1.5 % ~ 3 % lower than other software security mechanisms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.360-361
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• 2017

The infrastructure of the country and society is connected to cyberspace. Malicious codes that steal financial information from websites such as plastic surgeons, dentists, and hospitals that are confirmed as IP in Daegu South Korea area are spreading In particular, financial information is an important privacy target. Takeover of financial information leads to personal financial loss. In this paper, we analyze the recent pharming malicious code that takes financial information. Attack files with social engineering methods are spread as executables in the banner, disguised as downloaders. When the user selects the banner, the attack file infects the PC with malicious code to the user. The infected PC takes users to the farming site and seizes financial information and personal security card information. The fraudulent financial information causes a financial loss to the user. The research in this paper will contribute to secure financial security.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.6
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• pp.89-98
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• 2007

We have come a long way in the information age. Thanks to the advancement of such technologies as the internet, we have discovered new ways to convey information on a broader scope. However, negative aspects exist as is with anything else. These may include invasion of privacy over the web, or identity theft over the internet. What is more alarming is that malwares so called 'maliciouscodes' are rapidly spreading. Its intent is very destructive which can result in hacking, phishing and as aforementioned, one of the most disturbing problems on the net, invasion of privacy. This thesis describes the technology of how you can effectively analyze and detect these kind of malicious codes. We propose sequencial hybrid analysis for API calls that are hooked inside user-mode and kernel-level of Windows. This research explains how we can cope with malicious code more efficiently by abstracting malicious function signature and hiding attribute.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.31-40
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• 2024

Recently, static analysis-based signature and pattern detection technologies have limitations due to the advanced IT technologies. Moreover, It is a compatibility problem of multiple architectures and an inherent problem of signature and pattern detection. Malicious codes use obfuscation and packing techniques to hide their identity, and they also avoid existing static analysis-based signature and pattern detection techniques such as code rearrangement, register modification, and branching statement addition. In this paper, We propose an LLVM IR image-based automated static analysis of malicious code technology using machine learning to solve the problems mentioned above. Whether binary is obfuscated or packed, it's decompiled into LLVM IR, which is an intermediate representation dedicated to static analysis and optimization. "Therefore, the LLVM IR code is converted into an image before being fed to the CNN-based transfer learning algorithm ResNet50v2 supported by Keras". As a result, we present a model for image-based detection of malicious code.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4308-4325
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• 2021

The importance and necessity of artificial intelligence, particularly machine learning, has recently been emphasized. In fact, artificial intelligence, such as intelligent surveillance cameras and other security systems, is used to solve various problems or provide convenience, providing solutions to problems that humans traditionally had to manually deal with one at a time. Among them, information security is one of the domains where the use of artificial intelligence is especially needed because the frequency of occurrence and processing capacity of dangerous codes exceeds the capabilities of humans. Therefore, this study intends to examine the definition of artificial intelligence and machine learning, its execution method, process, learning algorithm, and cases of utilization in various domains, particularly the cases and contents of artificial intelligence technology used in the field of information security. Based on this, this study proposes a method to apply machine learning technology to the method of classifying and detecting malware that has rapidly increased in recent years. The proposed methodology converts software programs containing malicious codes into images and creates training data suitable for machine learning by preparing data and augmenting the dataset. The model trained using the images created in this manner is expected to be effective in classifying and detecting malware.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.1
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• pp.187-196
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• 2016

Android applications are inherently vulnerable to a repackaging attack such that malicious codes are easily inserted into an application and then resigned by the attacker. These days, it occurs often that such private or individual information is leaked. In principle, all Android applications are composed of user defined methods and APIs. As well as accessing to resources on platform, APIs play a role as a practical functional feature, and user defined methods play a role as a feature by using APIs. In this paper we propose a scheme to analyze sensitive APIs mostly used in malicious applications in terms of how malicious applications operate and which API they use. Based on the characteristics of target APIs, we accumulate the knowledge on such APIs using a machine learning scheme based on Naive Bayes algorithm. Resulting from the learned results, we are able to provide fine-grained numeric score on the degree of vulnerabilities of mobile applications. In doing so, we expect the proposed scheme will help mobile application developers identify the security level of applications in advance.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.180-187
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• 2020

Security threats are increasing with interest due to the mass spread of smart devices, and vulnerabilities in developed applications are being exposed while mobile malicious codes are spreading. The government and companies provide various applications for the public, and for reliability and security of applications, security checks are required during application development. In this paper, among the security threats that can occur in the mobile service environment, we set up the vulnerability analysis items to respond to security threats when developing Android-based applications. Based on the set analysis items, vulnerability analysis was performed by examining three applications of public institutions and private companies currently operating as mobile applications. As a result of application security checks used by three public institutions and companies, authority management and open module stability management were well managed. However, it was confirmed that many security vulnerabilities were found in input value verification, outside transmit data management, and data management. It is believed that it will contribute to improving the safety of mobile applications through the case of vulnerability analysis for Android application security.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.144-146
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• 2006

The Data Matrix of two-dimensional bar codes is a new technology capable of holding relatively large amounts of data compared to the conventional one-dimensional bar code which is just a key that can access detailed information to the host computer database. A secret key is used to prevent a watermark from malicious attacks. We encoded copyright information into a Data Matrix bar code for encoding process and it was spread a pseudo random pattern using owner key. We embedded a randomized watermark into the image using watermark's embedding position, pattern generated with a secret key. The experimental results have shown that the proposed scheme has good quality and is very robust to various attacks, such as JPEG compression and noise. Also the performance of the proposed scheme is verified by comparing the copyright information with the information which is extracted from a bar code scantier.

• International journal of advanced smart convergence
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• v.5 no.4
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• pp.54-56
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• 2016

Code-reuse attacks are very dangerous in various systems. This is because they do not inject malicious codes into target systems, but reuse the instruction sequences in executable files or libraries of target systems. Moreover, code-reuse attacks could be more harmful to IoT systems in the sense that it may not be easy to devise efficient and effective mechanism for code-reuse attack detection in resource-restricted IoT devices. In this paper, we propose a detection scheme with using Kullback-Leibler (KL) divergence to combat against code-reuse attacks in IoT. Specifically, we detect code-reuse attacks by calculating KL divergence between the probability distributions of the packets that generate from IoT devices and contain code region addresses in memory system and the probability distributions of the packets that come to IoT devices and contain code region addresses in memory system, checking if the computed KL divergence is abnormal.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.9
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• pp.423-428
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• 2006

In this paper we propose a new watermarking scheme using a data matrix and a cryptograph key. The data matrix of two-dimensional bar codes is a new technology capable of holding relatively large amounts of data compared to the conventional one-dimensional bar code. And a cryptograph key is used to prevent a watermark from malicious attacks. We encoded the copyright information into a data matrix bar code, and it was spread as a pseudo random pattern using the owner key. The experimental results show that the proposed scheme has good quality and is robust to various attacks, such as JPEG compression, filtering and resizing. Also the performance of the proposed scheme is verified by comparing the copyright information with the information which is extracted from the watermark.