• The KIPS Transactions:PartC
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• v.16C no.5
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• pp.555-562
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• 2009

In malware accident investigation, the most important thing is detection of malicious code. Signature based anti-virus softwares have been used in most of the accident. Malware can easily avoid signature based detection by using packing or encryption method. Because of this, packed file detection is also important. Detection methods can be divided into signature based detection and entropy based detection. Signature based detection can not detect new packing. And entropy based detection has a problem with false positive. We provides detection method using entropy statistics of entry point section and 'write' properties of essential characteristic of packed file. And then, we show packing detection tool and evaluate its performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.4
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• pp.775-784
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• 2019

Malware infringement attacks are continuously increasing in various environments such as mobile, IOT, windows and mac due to the emergence of new and variant malware, and signature-based countermeasures have limitations in detection of malware. In addition, analytical performance is deteriorating due to obfuscation, packing, and anti-VM technique. In this paper, we propose a system that can detect malware based on machine learning by using similarity hashing-based pattern detection technique and static analysis after file classification according to packing. This enables more efficient detection because it utilizes both pattern-based detection, which is well-known malware detection, and machine learning-based detection technology, which is advantageous for detecting new and variant malware. The results of this study were obtained by detecting accuracy of 95.79% or more for benign sample files and malware sample files provided by the AI-based malware detection track of the Information Security R&D Data Challenge 2018 competition. In the future, it is expected that it will be possible to build a system that improves detection performance by applying a feature vector and a detection method to the characteristics of a packed file.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.902-904
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• 2022

스마트폰 사용자 수가 증가함에 따라 스미싱, 몸캠피싱, 메신저 피싱과 같은 정보통신망을 이용한 범죄가 큰 폭으로 증가하고 있다. 이러한 범죄 피해는 다양한 연령층에서 발생하고 있다. 본 논문에서는 국내 모바일 운영체제 점유율이 가장 높은 안드로이드 운영체제를 대상으로 하는 패킹된 악성 앱 언패킹을 수행하고 시그니처 기반 탐지 도구인 Yara 를 통해 악성 앱에 사용된 패커를 식별하는 통합 악성 앱 언패킹 시스템을 제공하여 악성 앱을 이용한 범죄 대응에 도움을 줄 수 있을 것으로 기대된다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1043-1053
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• 2023

Malware analysts work diligently to analyze and counteract malware, while developers persistently devise evasion tactics, notably through packing and obfuscation techniques. Although previous works have proposed general unpacking approaches, they inadequately address techniques like OEP obfuscation and API obfuscation employed by modern packers, leading to occasional failures during the unpacking process. This paper examines the OEP and API obfuscation techniques utilized by various packers and introduces a system designed to automatically de-obfuscate them. The system analyzes the memory of packed programs, detects trampoline codes, and identifies obfuscated information, for program reconstruction. Experimental results demonstrate the effectiveness of our system in de-obfuscating programs that have undergone OEP and API obfuscation techniques.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.2
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• pp.169-177
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• 2020

Malware analysis is one of the important concerns of computer security, and advances in analysis techniques have become important for computer security. In the past, the signature-based method was used to detect malware. However, as the percentage of packed malware increased, it became more difficult to detect using the conventional method. In this paper, we propose a method for identifying packers of packed programs using machine learning. The proposed method parses the packed program to extract specific PE information that can identify the packer and identifies the packer using the Adaptive Boosting algorithm among the machine learning models. To verify the accuracy of the proposed method, we collected and tested 391 programs packed with 12 types of packers and found that the packers were identified with an accuracy of about 99.2%. In addition, we presented the results of identification using PEiD, a signature-based PE identification tool, and existing machine learning method. The proposed method shows better performance in terms of accuracy and speed in identifying packers than existing methods.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.6
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• pp.155-164
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• 2018

Many malicious programs have been compressed or encrypted using various commercial packers to prevent reverse engineering, So malicious code analysts must decompress or decrypt them first. The OEP (Original Entry Point) is the address of the first instruction executed after returning the encrypted or compressed executable file back to the original binary state. Several unpackers, including PinDemonium, execute the packed file and keep tracks of the addresses until the OEP appears and find the OEP among the addresses. However, instead of finding exact one OEP, unpackers provide a relatively large set of OEP candidates and sometimes OEP is missing among candidates. In other words, existing unpackers have difficulty in finding the correct OEP. We have developed new tool which provides fewer OEP candidate sets by adding two methods based on the property of the OEP. In this paper, we propose two methods to provide fewer OEP candidate sets by using the property that the function call sequence and parameters are same between packed program and original program. First way is based on a function call. Programs written in the C/C++ language are compiled to translate languages into binary code. Compiler-specific system functions are added to the compiled program. After examining these functions, we have added a method that we suggest to PinDemonium to detect the unpacking work by matching the patterns of system functions that are called in packed programs and unpacked programs. Second way is based on parameters. The parameters include not only the user-entered inputs, but also the system inputs. We have added a method that we suggest to PinDemonium to find the OEP using the system parameters of a particular function in stack memory. OEP detection experiments were performed on sample programs packed by 16 commercial packers. We can reduce the OEP candidate by more than 40% on average compared to PinDemonium except 2 commercial packers which are can not be executed due to the anti-debugging technique.

• 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.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.115-118
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• 2020

본 논문에서는 IoT 취약점 탐지 시스템과 취약점 데이터베이스 구축 방안을 제안한다. 동적 웹페이지 제어기술 기반의 크롤링 기법으로 펌웨어를 수집한 후, 패킹된 펌웨어 파일을 Binwalk, FMK를 활용하여 추출하고 Qemu 에뮬레이팅 기반의 실제 서비스를 실행시키는 시스템을 구현하여 펌웨어 취약점을 탐지할 수 있는 환경을 구축한다. 구축된 시스템을 통하여 수집, 추출, 에뮬레이팅 과정에서 생성된 데이터와 연계되어 탐지된 취약점 정보를 저장할 수 있는 데이터베이스를 제안한다. 제안된 시스템과 데이터베이스를 통하여 IoT 기기 펌웨어의 취약점을 탐지하고 예방을 할 수 있을 것이라 기대한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.131-133
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• 2023

악성코드를 유포할 때 프로그램 코드만으로 악성코드의 유무를 확인할 수 없도록 조치하여 분석을 지연시키는 방식을 사용하는 방향으로 발전하고 있다. 악성코드를 실행하지 않고 코드와 구조만으로 분석하는 정적 분석으로는 악성코드를 판별할 수 없어 코드를 직접 실행해 분석하는 동적 분석을 이용해야 한다. 본 논문에서는 난독화된 비정상적인 코드를 직접 실행한 동적 분석데이터와 일반적이지 않은 섹션들의 정보를 추출한 정적 분석데이터를 이용해 동적-정적 분석 데이터와 딥러닝 모델을 통해 난독화 및 패킹된 악성코드를 탐지하는 기법을 제안한다.