• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.45-55
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• 2020

Due to the development of information and communication technology, the number of new / variant malicious codes is increasing rapidly every year, and various types of malicious codes are spreading due to the development of Internet of things and cloud computing technology. In this paper, we propose a malware analysis method based on string information that can be used regardless of operating system environment and represents library call information related to malicious behavior. Attackers can easily create malware using existing code or by using automated authoring tools, and the generated malware operates in a similar way to existing malware. Since most of the strings that can be extracted from malicious code are composed of information closely related to malicious behavior, it is processed by weighting data features using text mining based method to extract them as effective features for malware analysis. Based on the processed data, a model is constructed using various machine learning algorithms to perform experiments on detection of malicious status and classification of malicious groups. Data has been compared and verified against all files used on Windows and Linux operating systems. The accuracy of malicious detection is about 93.5%, the accuracy of group classification is about 90%. The proposed technique has a wide range of applications because it is relatively simple, fast, and operating system independent as a single model because it is not necessary to build a model for each group when classifying malicious groups. In addition, since the string information is extracted through static analysis, it can be processed faster than the analysis method that directly executes the code.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.69-76
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• 2013

In the recent years, cyber attacks by malicious codes called malware has become a social problem. With the explosive appearance and increase of new malware, innumerable disasters caused by metaphoric malware using the existing malicious codes have been reported. To secure more effective detection of malicious codes, in other words, to make a more accurate judgment as to whether suspicious files are malicious or not, this study introduces the malware analysis system, which is based on a profiling technique using the Finite Automata. This new analysis system enables realtime automatic detection of malware with its optimized partial execution method. In this paper, the functions used within a file are expressed by finite automata to find their correlation, and a realtime malware analysis system enabling us to give an immediate judgment as to whether a file is contaminated by malware is suggested.

• Smart Media Journal
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• v.8 no.1
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• pp.19-26
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• 2019

At the opening ceremony of 2018 Winter Olympics in PyeongChang, an unknown cyber-attack occurred. The malicious code used in the attack is based on in-memory malware, which differs from other malicious code in its concealed location and is spreading rapidly to be found in more than 140 banks, telecommunications and government agencies. In-memory malware accounts for more than 15% of all malicious codes, and it does not store its own information in a non-volatile storage device such as a disk but resides in a RAM, a volatile storage device and penetrates into well-known processes (explorer.exe, iexplore.exe, javaw.exe). Such characteristics make it difficult to analyze it. The most recently released in-memory malicious code bypasses the endpoint protection and detection tools and hides from the user recognition. In this paper, we propose a method to efficiently extract the payload by unpacking injection through IDA Pro debugger for Dorkbot and Erger, which are in-memory malicious codes.

• Smart Media Journal
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• v.11 no.2
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• pp.18-24
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• 2022

Problems caused by malicious comments occur on many social media. In particular, YouTube, which has a strong character as a medium, is getting more and more harmful from malicious comments due to its easy accessibility using mobile devices. In this paper, we designed and implemented a YouTube malicious comment detection system to identify malicious comments in YouTube contents through LSTM-based natural language processing and to visually display the percentage of malicious comments, such commentors' nicknames and their frequency, and we evaluated the performance of the system. By using a dataset of about 50,000 comments, malicious comments could be detected with an accuracy of about 92%. Therefore, it is expected that this system can solve the social problems caused by malicious comments that many YouTubers faced by automatically generating malicious comments statistics.

• Informatization Policy
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• v.24 no.4
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• pp.68-78
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• 2017

Malicious codes are currently becoming more complex and diversified, causing various problems spanning from simple information exposure to financial or psychologically critical damages. Even though many researches have studied using reverse engineering to detect these malicious codes, malicious code developers also utilize bypassing techniques against the code analysis to cause obscurity in code understanding. Furthermore, rootkit techniques are evolving to utilize such bypassing techniques, making it even more difficult to detect infection. Therefore, in this paper, we design the analysis process as a more agile countermeasure to malicious codes that bypass analysis techniques. The proposed analysis process is expected to be able to detect these malicious codes more efficiently.

• Review of KIISC
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• v.18 no.3
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• pp.1-16
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• 2008

2008년으로 컴퓨터 바이러스가 제작된 지 이미 20년이라는 세월을 넘기게 되었다. 이 긴 시간 속에서 컴퓨터 바이러스는 파일 감염을 목적으로 하는 바이러스(virus)로부터 네트워크를 통한 급속한 화산을 시도하는 웜(Worm) 그리고 데이터 유출과 파괴를 목적으로 하는 트로이목마(Trojan Horse)로 발달해왔다. 최근에는 컴퓨터 사용자의 정보를 무단으로 유출하기 위한 스파이웨어(Spyware)에 이르기까지 다양한 형태로 변화를 이룩해 왔다. 이러한 다양한 형태로의 변화가 진행되는 동안에도 컴퓨터 과학의 발달에 따른 새로운 기술들을 흡수하여 더욱더 정교하고 파괴적인 기능들로 발전을 이루게 되었다. 다양한 형태와 기술적 인 발전을 거듭한 악성 코드(Malicious Code)는 컴퓨터 운영 체제, 네트워크의 발달로 이룩된 컴퓨터 과학사와 함께 하였다고 볼 수 있다. 악성 코드의 발전은 해가 갈수록 수치적인 면에서는 증가 추세를 이루고 있으며 기술적인 면에서도 더욱더 위험성을 더해 가고 있으며 그 제작 목적 또한 전통적인 기술력 과시에서 금전적인 이익을 취하기 위한 도구로 전락하고 있다. 이렇게 제작 목적의 변질로 인해 악성 코드는 인터넷 공간에서 사이버 범죄를 발생시키는 원인 중 하나로 변모하게 되었다. 본 논문에서는 이러한 발전적인 형태를 띠고 있는 악성 코드에 대해서 최근 동향을 바탕으로 어떠한 악성코드와 스파이웨어의 형태가 발견되고 있는지 그리고 최근 발견되고 있는 악성코드에서 사용되는 소프트웨어 취약점들을 살펴보고자 한다. 그리고 이러한 악성코드의 형태에 따라 향후 발생할 수 있는 새로운 악성 코드의 위협 형태도 다루어 보고자 한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.3
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• pp.37-44
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• 2011

Recently malicious activities that is a DDoS, spam, propagation of malware, steeling person information, phishing on the Internet are related malicious botnet. To detect malicious botnet, Many researchers study a detection system for malicious botnet, but these applies specific protocol, action or attack based botnet. In this reason, we study a selection of measurement to detec malicious botnet in this paper. we collect a traffic of malicious botnet and analyze it for feature of network traffic. And we select a feature based measurement. we expect to help a detection of malicious botnet through this study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.522-528
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• 2020

Rapidly spreading malware, such as ransomware, trojans and Internet worms, have become one of the new major threats of the Internet recently. In order to resist against their malicious behaviors, it is essential to comprehend how malware propagate and how main factors affect spreads of them. In this paper, we aim to develop a spread prediction tool based on the modeling of malware epidemics. So we surveyed the related studies, and described the system design and implementation. In addition, we experimented on the spread of malware with major factors of malware using the developed spread prediction tool. If you make good use of the proposed prediction tool, it is possible to predict the malware spread at major factors and explore under various responses from a macro perspective with only basic knowledge of the recently wormable malware.

• Journal of IKEEE
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• v.25 no.4
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• pp.672-678
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• 2021

New malware continues to increase and become advanced by every year. Although various studies are going on executable files to diagnose malicious codes, it is difficult to detect attacks that internalize malicious code threats in emails by exploiting non-executable document files, malicious URLs, and malicious macros and JS in documents. In this paper, we introduce a method of analyzing malicious code for email security through proactive detection and blocking of malicious email attacks, and propose a method for determining whether a non-executable document file is malicious based on AI. Among various algorithms, an efficient machine learning modeling is choosed, and an ML workflow system to diagnose malicious code using Kubeflow is proposed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.439-446
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• 2022

Document-type malicious codes are being actively distributed using attachments on websites or e-mails. Document-type malicious code is relatively easy to bypass security programs because the executable file is not executed directly. Therefore, document-type malicious code should be detected and prevented in advance. To detect document-type malicious code, we identified the document structure and selected keywords suspected of being malicious. We then created a dataset by converting the stream data in the document to ASCII code values. We specified the location of malicious keywords in the document stream data, and classified the stream as malicious by recognizing the adjacent information of the malicious keywords. As a result of detecting malicious codes by applying the CNN model, we derived accuracies of 0.97 and 0.92 in stream units and file units, respectively.