• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.843-850
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• 2003

Script viruses are easy to make a variation because they can be built easily and be spread in text format. Thus signature-based method has a limitation in detecting script viruses. In a consequence, many researches suggest simple heuristic methods, but high false-positive error is always being an obstacle. In order to overcome this problem, our previous study concentrated on analyzing data flow of codes and has low-false positive error, but still could not detect a polymorphic virus because polymorphic virus loads self body and changes it before make a descendent. We suggest a heuristic detection method which expands the detection range of previous method to include polymorphic script viruses. Expanded data flow analysis heuristic has an expanded grammar to detect Polymorphic copy Propagation. Finally, we will show the experimental result for the effectiveness of suggested method.

• Journal of KIISE:Information Networking
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• v.29 no.5
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• pp.473-481
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• 2002

Decryption of encrypted malicious scripts is essential in order to analyze the scripts and to determine whether they are malicious. An effective decryption technique is one that is designed to consider the characteristics of the script languages rather than the specific encryption patterns. However, currently X-raying and emulation are not the proper techniques for the script because they were designed to decrypt binary malicious codes. In addition to that, heuristic techniques are unable to decrypt unknown script codes that use unknown encryption techniques. In this paper, we propose a new technique that will be able to decrypt malicious scripts based on analytical approach. we describe its implementation.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.1-9
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• 2023

Malware files containing concealed malicious scripts have recently been identified within MS Office documents frequently. In response, this paper describes the design and implementation of a system that automatically detects malicious digital files using machine learning techniques. The system is proficient in identifying malicious scripts within MS Office files that exploit the OLE VBA macro functionality, detecting malicious scripts embedded within the CDH/LFH/ECDR internal field values through OOXML structure analysis, and recognizing abnormal CDH/LFH information introduced within the OOXML structure, which is not conventionally referenced. Furthermore, this paper presents a mechanism for utilizing the VirusTotal malicious script detection feature to autonomously determine instances of malicious tampering within MS Office files. This leads to the design and implementation of a machine learning-based integrated software. Experimental results confirm the software's capacity to autonomously assess MS Office file's integrity and provide enhanced detection performance for arbitrary MS Office files when employing the optimal machine learning model.

• Convergence Security Journal
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• v.10 no.1
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• pp.19-27
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• 2010

Nowadays, the network's speed and internet technology are progressing rapidly but mill wares are occurring frequently through the Window's weak point. Since the malwares have various infection types and characteristics, it is hard to detect them by the virus vaccine and to cure them. This paper analyzes the type and characteristics of the mal ware and proposes a script technology that can find the location of the mal ware by the program and respond rapidly to the new kind of malwares.

• Journal of KIISE:Information Networking
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• v.29 no.6
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• pp.663-673
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• 2002

Server-side anti-viruses are useful to protect their domains, because they can detect malicious codes at the gateway of their domains. In prevailing local network, all clients cannot be perfectly controlled by domain administrators, so server-side inspection, for example in e-mail server, is used as an efficient technique of detecting mobile malicious codes. However, current server-side anti-virus systems perform only signature-based detection for known malicious codes, simple filtering, and file name modification. One of the main reasons that they don't have detection features, for unknown malicious codes, is that activity monitoring technique is unavailable for server machines. In this paper, we propose a detection technique that is executed at the server, but it can monitor activities at the clients without any anti-virus features. we describe its implementation.

• Korean Journal Plant Pathology
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• v.10 no.3
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• pp.228-234
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• 1994

Genomic RNA was extracted from Cy strain of odontoglossum ringspot tobamovirus (ORSV-Cy) isolated from infected leaves of tobacco cv. Samsun. Size of the genomic RNA was about 6.6 kb in length. The genomic RNA was fractionated using Sephadex G-50 column chromatography into 2 fractions. They were polyadenylated at their 3'-end using E. coli poly(A) polymerase. Polyadenylated viral RNA was recovered by oligo (dT) primer adapter containing NotI restriction site and Moloney murine leukemia virus SuperScript reverse transcriptase (RNase H-). Second-strand cDNA was synthesized by using E. coli DNA ligase, E. coli DNA polymerase I and E. coli RNase H. Recombinant plasmids containing cDNAs for ORSV-Cy RNA ranged from about 800 bp to 3,000 bp. Among the selected 238 recombinants, pORCY-124 clone was the largest one covering 3'-terminal half of the viral RNA. This clone contained two restriction sites for EcoRI and XbaI and one site for AccI, AvaI, BglII, BstXI, HindIII, PstI, and TthIII 1. respectively. The clone contained partial viral replicase, a full-length movement protein and a complete coat protein genes followed by a 3' untranslated region of 414 nucleotides based on restriction mapping and nucleotide sequencing analyses. Clones pORCY-028, -068, -072, -187 and -224 were overlapped with the pORCY-124. Clones pORCY-014 and -095 covered 5' half upstream from the middle region of the viral RNA, which was estimated based on restriction mapping and partial sequence analysis. Constructed cDNA library covered more than 90% of the viral genome.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10b
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• pp.1013-1016
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• 2001

최근 인터넷의 전자 메일 서비스 사용의 증가로 인해 스크립트형 웜 바이러스에 대한 피해가 확산되고 있다. 전자 메일을 통하여 유포되는 스크립트형 웜 바이러스는 지속적으로 새로운 형태로 변이되어 나타나고 있지만, 이에 대한 예방 방법은 새로운 패턴이 분석된 후 이를 토대로 탐지하기 때문에 적극적인 대응을 하지 못하는 실정이다. 이에 본 논문에서는 스크립트형 웜 바이러스의 행위를 추출하여 일정한 패턴을 정의한 후 이를 기반으로 스크립트형 웜 바이러스 탐지 시스템을 설계하고, 기존의 패턴에 유전자 알고리즘을 적용하여 알려지지 않은 새로운 패턴을 생성한 후 바이러스 탐지에 활용할 수 있는 방안을 연구한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.407-409
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• 2003

매크로 바이러스를 비롯한 악성 스크립트 바이러스는 이진 코드와는 달리 텍스트 형식으로 코드가 저장되기 때문에 많은 수의 변종이 가능하고 다형성을 지닌 형태로의 제작이 쉬워 새로운 형태의 출현이 빈번하다［1］. 이에 따라 시그니처 기반의 감지 기법을 탈피한 다양한 기법들이 제안되고 있으나 세밀한 수준의 분석으로 인한 시간 지연과 높은 긍정 오류의 문제로 현실적으로 적용되지 못하는 실정이다. 이를 개선하여 비교적 짧은 시간에 정적 분석을 끝내고 코드 삽입 기법을 병행하여 긍정 오류 문제를 해결한 기법이 제안 되었다［2］. 그러나 이 기법에서 사용하는 정적 분석은 다형성 스크립트 바이러스에 대하여 고려하고 있지 않다. 본 논문에서는 제안된 정적 분석 기법을 확장 하여 다형성 스크립트 바이러스를 탐지할 수 있는 기법을 제시 한다.

• The KIPS Transactions:PartC
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• v.9C no.5
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• pp.765-774
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• 2002

Analyzing the code using static heuristics is a widely used technique for detecting unknown malicious codes. It decides the maliciousness of a code by searching for some fragments that had been frequently found in known malicious codes. However, in script codes, it tries to search for sequences of method calls, not code fragments, because finding such fragments is much difficult. This technique makes many false alarms because such method calls can be also used in normal scripts. Thus, static heuristics for scripts are used only to detect malicious behavior consisting of specific method calls which is seldom used in normal scripts. In this paper. we suggest a static analysis that can detect malicious behavior more accurately, by concerning not only the method calls but also parameters and return values. The result of experiments show that malicious behaviors, which were difficult to detect by previous works, due to high false positive, will be detected by our method.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.422-435
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• 2016

Despite the convenience brought by the advances in web and Internet technology, users are increasingly being exposed to the danger of various types of cyber attacks. In particular, recent studies have shown that today's cyber attacks usually occur on the web via malware distribution and the stealing of personal information. A drive-by download is a kind of web-based attack for malware distribution. Researchers have proposed various methods for detecting a drive-by download attack effectively. However, existing methods have limitations against recent evasion techniques, including JavaScript obfuscation, hiding, and dynamic code evaluation. In this paper, we propose an emulation-based malicious webpage detection method. Based on our study on the limitations of the existing methods and the state-of-the-art evasion techniques, we will introduce four features that can detect malware distribution networks and we applied them to the proposed method. Our performance evaluation using a URL scan engine provided by VirusTotal shows that the proposed method detects malicious webpages more precisely than existing solutions.