• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.3
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• pp.161-166
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• 2023

In recent years, new and variant hacking of binary codes has increased, and the limitations of techniques for detecting malicious codes in source programs and defending against attacks are often exposed. Advanced software security vulnerability detection technology using machine learning and deep learning technology for binary code and defense and response capabilities against attacks are required. In this paper, we propose a malware clustering method that groups malware based on the characteristics of the taint information after entering dynamic taint information by tracing the execution path of binary code. Malware vulnerability detection was applied to a three-layered Few-shot learning model, and F1-scores were calculated for each layer's CPU and GPU. We obtained 97~98% performance in the learning process and 80~81% detection performance in the test process.

• ETRI Journal
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• v.39 no.3
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• pp.406-416
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• 2017

Malware propagated via the World Wide Web is one of the most dangerous tools in the realm of cyber-attacks. Its methodologies are effective, relatively easy to use, and are developing constantly in an unexpected manner. As a result, rapidly detecting malware propagation websites from a myriad of webpages is a difficult task. In this paper, we present LoGos, an automated high-interaction dynamic analyzer optimized for a browser-based Windows virtual machine environment. LoGos utilizes Internet Explorer injection and API hooks, and scrutinizes malicious behaviors such as new network connections, unused open ports, registry modifications, and file creation. Based on the obtained results, LoGos can determine the maliciousness level. This model forms a very lightweight system. Thus, it is approximately 10 to 18 times faster than systems proposed in previous work. In addition, it provides high detection rates that are equal to those of state-of-the-art tools. LoGos is a closed tool that can detect an extensive array of malicious webpages. We prove the efficiency and effectiveness of the tool by analyzing almost 0.36 M domains and 3.2 M webpages on a daily basis.

• Convergence Security Journal
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• v.13 no.1
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• pp.79-86
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• 2013

The objective of DDoS Attacks is to simply disturb the services. In recent years, the DDoS attacks have been evolved into Multi-Vector Attacks which use diversified and mixed attacking techniques. Multi-Vector Attacks start from DDoS Attack and Malware Infection, obtain inside information, and make zombie PC to reuse for the next DDoS attacks. These forms of Multi-Vector Attacks are unable to be prevented by the existing security strategies for DDoS Attacks and Malware Infection. This paper presents an approach to effectively defend against diversified Multi-Vector attacks by using Reverse Proxy Group and PMS(Patch Management Server).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3005-3027
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• 2015

Spam has become one of the top threats of micro-blogging networks as the representations of rumor spreading, advertisement abusing and malware distribution. With the increasing popularity of micro-blogging, the problems will exacerbate. Prior detection tools are either designed for specific types of spams or not robust enough. Spammers may escape easily from being detected by adjusting their behaviors. In this paper, we present a novel model to quantitatively evaluate information diffusion in micro-blogging networks. Under this model, we found that spam posts differ wildly from the non-spam ones. First, the propagations of non-spam posts mostly result from their followers, but those of spam posts are mainly from strangers. Second, the non-spam posts relatively last longer than the spam posts. Besides, the non-spam posts always get their first reposts/comments much sooner than the spam posts. With the features defined in our model, we propose an RBF-based approach to detect spams. Different from the previous works, in which the features are extracted from individual profiles or contents, the diffusion features are not determined by any single user but the crowd. Thus, our method is more robust because any single user's behavior changes will not affect the effectiveness. Besides, although the spams vary in types and forms, they're propagated in the same way, so our method is effective for all types of spams. With the real data crawled from the leading micro-blogging services of China, we are able to evaluate the effectiveness of our model. The experiment results show that our model can achieve high accuracy both in precision and recall.

• Convergence Security Journal
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• v.19 no.4
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• pp.77-85
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• 2019

As the Internet continues to evolve, cyber attacks are becoming more precise and covert. Anonymous communication, which is used to protect personal privacy, is also being used for cyber attacks. Not only it hides the attacker's IP address but also encrypts traffic, which allows users to bypass the information protection system that most organizations and institutions are using to defend cyber attacks. For this reason, anonymous communication can be used as a means of attacking malicious code or for downloading additional malware. Therefore, this study aims to suggest a method to detect and block encrypted anonymous communication as quickly as possible through artificial intelligence. Furthermore, it will be applied to the defense to detect malicious communication and contribute to preventing the leakage of important data and cyber attacks.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.2
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• pp.94-99
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• 2016

Application layer attacks have for years posed an ever-serious threat to network security, since they always come after a technically legitimate connection has been established. In recent years, cyber criminals have turned to fully exploiting the web as a medium of communication to launch a variety of forbidden or illicit activities by spreading malicious automated software (auto-ware) such as adware, spyware, or bots. When this malicious auto-ware infects a network, it will act like a robot, mimic normal behavior of web access, and bypass the network firewall or intrusion detection system. Besides that, in a private and large network, with huge Hypertext Transfer Protocol (HTTP) traffic generated each day, communication behavior identification and classification of auto-ware is a challenge. In this paper, based on a previous study, analysis of auto-ware communication behavior, and with the addition of new features, a method for classification of HTTP auto-ware communication is proposed. For that, a Not Only Structured Query Language (NoSQL) database is applied to handle large volumes of unstructured HTTP requests captured every day. The method is tested with real HTTP traffic data collected through a proxy server of a private network, providing good results in the classification and detection of suspicious auto-ware web access.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1103-1107
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• 2016

Network assets have been protected from malware infection by checking the integrity of mobile devices through network access control systems, vaccines, or mobile device management. However, most of existing systems apply a uniform security policy to all users, and allow even infected mobile devices to log into the network inside for completion of the integrity checking, which makes it possible that the infected devices behave maliciously inside the network. Therefore, this paper proposes a network defense mechanism based on isolated networks. In the proposed mechanism, every mobile device go through the integrity check system implemented in an isolated network, and can get the network access only if it has been validated successfully.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.3
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• pp.559-565
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• 2015

To defend against information leakage or malware inflow by USB memory, security technologies such as copy protection and device control have being researched and developed. However, countermeasure are insufficient despite being recognized as a fatal security-hole for BadUSB presented at the Black Hat Security Conference 2014. To solve this problem, the countermeasures for BadUSB vulnerability are proposed.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.635-638
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• 2012

2011년 Stuxnet의 출현을 기점으로 사이버공격이 보다 정밀화 구체화 되고 있으며 사이버전의 주요 무기라 할 수 있는 악성코드들의 특정 국가산업 기관시스템에 대한 직접적이고 지속적인 공격 시도가 예상된다. 본 논문에서는 사이버전의 개념, 악성코드 관련 동향과 공격행위별 감염대상 등을 살펴보고, 국방정보통신망에서 사이버공격에 대비한 악성코드 감염 예방방안을 제안한다.

• Journal of KIISE
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• v.41 no.12
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• pp.1018-1025
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• 2014

An ROP attack creates gadget sequences which consist of existing code snippets in a program, and hijacks the control flow of a program by chaining and executing gadget sequences consecutively. Existing defense schemes have limitations in that they cause high execution overhead, an increase in the binary size overhead, and a low applicability. In this paper, we solve these problems by introducing zero-sum defender, which is a fast and space-efficient mitigation scheme against ROP attacks. We find a fundamental property of gadget execution in which control flow starts in the middle of a function without a call instruction and ends with a return instruction. So, we exploit this property by monitoring whether the execution is abused by ROP attacks. We achieve a very low runtime overhead with a very small increase in the binary size. In our experimental results, we verified that our defense scheme prevents real world ROP attacks, and we showed that there is only a 2% performance overhead and a 1% binary size increase overhead in several benchmarks.