• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.202-209
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• 2023

Android malware is now on the rise, because of the rising interest in the Android operating system. Machine learning models may be used to classify unknown Android malware utilizing characteristics gathered from the dynamic and static analysis of an Android applications. Anti-virus software simply searches for the signs of the virus instance in a specific programme to detect it while scanning. Anti-virus software that competes with it keeps these in large databases and examines each file for all existing virus and malware signatures. The proposed model aims to provide a machine learning method that depend on the malware detection method for Android inability to detect malware apps and improve phone users' security and privacy. This system tracks numerous permission-based characteristics and events collected from Android apps and analyses them using a classifier model to determine whether the program is good ware or malware. This method used the machine learning techniques KNN-SVM, DBN, and GRU in which help to find the accuracy which gives the different values like KNN gives 87.20 percents accuracy, SVM gives 91.40 accuracy, Naive Bayes gives 85.10 and DBN-GRU Gives 97.90. Furthermore, in this paper, we simply employ standard machine learning techniques; but, in future work, we will attempt to improve those machine learning algorithms in order to develop a better detection algorithm.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.644-660
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• 2016

The real-time detection of malware remains an open issue, since most of the existing approaches for malware categorization focus on improving the accuracy rather than the detection time. Therefore, finding a proper balance between these two characteristics is very important, especially for such sensitive systems. In this paper, we present a fast portable executable (PE) malware detection system, which is based on the analysis of the set of Application Programming Interfaces (APIs) called by a program and some technical PE features (TPFs). We used an efficient feature selection method, which first selects the most relevant APIs and TPFs using the chi-square ($KHI^2$) measure, and then the Phi (${\varphi}$) coefficient was used to classify the features in different subsets, based on their relevance. We evaluated our method using different classifiers trained on different combinations of feature subsets. We obtained very satisfying results with more than 98% accuracy. Our system is adequate for real-time detection since it is able to categorize a file (Malware or Benign) in 0.09 seconds.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.59-66
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• 2017

Hackers have obfuscated their malware to avoid being analyzed. Recently, obfuscation tools translate original codes into bytecodes to use virtualized-obfuscation, so that bytecodes are executed by virtual machines. In such cases, malware analysts fail to know about the malware before execution of the codes. We found that program slicing is one of promising program analysis techniques to solve this problem. The main concepts of program slice include slicing criteria given by analysts and sliced statements according to the slicing criteria. This paper proposes a deobfuscation method based on program slicing technique.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.131-138
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• 2020

Malware is generally recognized as a computer program that penetrates another computer system and causes malicious behavior intended by the developer. In cyberspace, it is also used as a cyber weapon to attack adversary. The most important factor that a malware must have as a cyber weapon is that it must achieve its intended purpose before being detected by the other's detection system. It requires a lot of time and expertise to create a single malware to avoid the other's detection system. We propose the framework that automatically generates variant malware when a binary code type malware is input using the DCM technique. In this framework, the sample malware was automatically converted into variant malware, and it was confirmed that this variant malware was not detected in the signature-based malware detection system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.153-154
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• 2018

전 세계 모바일 게임 소비 시장의 증가와 사용자들이 지속적으로 증가하는 반면 랜섬웨어와 같은 악성 프로그램들이 악의적인 목적을 위하여 모바일게임 시장에 피해를 주는 사례들도 지속적으로 증가하는 것도 사실이다. 본 논문에서는 모바일 게임을 이용한 악성코드 위협으로부터 보호하기 위하여 4차 산업의 가장 핵심 기술인 인공지능의 학습기술에 악성코드 분석기술을 연계시켜 새로운 모바일 악성코드 탐지와 속도를 향상시키는 기술의 필요성을 제시한다.

• Convergence Security Journal
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• v.19 no.3
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• pp.37-41
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• 2019

Recently, due to the appearance of various types of malware, the existing static analysis exposes many limitations. Static analysis means analyzing the structure of a code or program with source code or object code without actually executing the (malicious) code. On the other hand, dynamic analysis in the field of information security generally refers to a form that directly executes and analyzes (malware) code, and compares and examines and analyzes the state before and after execution of (malware) code to grasp the execution flow of the program. However, dynamic analysis required analyzing huge amounts of data and logs, and it was difficult to actually store all execution flows. In this paper, we propose and implement a preprocessor architecture of a system that performs malware detection and real-time multi-dynamic analysis based on 2nd generation PT in Windows environment (Windows 10 R5 and above).

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.3
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• pp.159-168
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• 2017

Smartphone malware has increased because Smartphone users has increased and smartphones are widely used in everyday life. Since 2012, Android has been the most mobile operating system. Owing to the open nature of Android, countless malware are in Android markets that seriously threaten Android security. Most of Android malware detection program does not detect malware to which bypass techniques apply and also does not detect unknown malware. In this paper, we propose lightweight method for detection of Android malware using static analysis and deep learning techniques. For experiments we crawl 7,000 apps from the Google Play Store and collect 6,120 malwares. The result show that proposed method can achieve 98.05% detection accuracy. Also, proposed method can detect about unknown malware families with good performance. On smartphones, the method requires 10 seconds for an analysis on average.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.172-180
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• 2022

Crypto-mining malware (known as crypto-jacking) is a novel cyber-attack that exploits the victim's computing resources such as CPU and GPU to generate illegal cryptocurrency. The attacker get benefit from crypto-jacking by using someone else's mining hardware and their electricity power. This research focused on the possibility of detecting the potential crypto-mining malware in an environment by analyzing both static and dynamic approaches of deep learning. The Program Executable (PE) files were utilized with deep learning methods which are Long Short-Term Memory (LSTM). The finding revealed that LTSM outperformed both SVM and RF in static and dynamic approaches with percentage of 98% and 96%, respectively. Future studies will focus on detecting the malware using larger dataset to have more accurate and realistic results.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.851-865
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• 2021

Enterprise networks in the PyeongChang Winter Olympics were hacked in February 2018. According to a domestic security company's analysis report, attackers destroyed approximately 300 hosts with the aim of interfering with the Olympics. Enterprise have no choice but to rely on digital vaccines since it is overwhelming to analyze all programs executed in the host used by ordinary users. However, traditional vaccines cannot protect the host against variant or new malware because they cannot detect intrusions without signatures for malwares. To overcome this limitation of signature-based detection, there has been much research conducted on the behavior analysis of malwares. However, since most of them rely on a sandbox where only analysis target program is running, we cannot detect malwares intruding the host where many normal programs are running. Therefore, this study proposes a method to detect malware variants in the host through logs rather than the sandbox. The proposed method extracts common behaviors from variants group and finds characteristic behaviors optimized for querying. Through experimentation on 1,584,363 logs, generated by executing 6,430 malware samples, we prove that there exist the common behaviors that variants share and we demonstrate that these behaviors can be used to detect variants.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.709-720
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• 2013

Virtualization obfuscation makes hard to analyze the code by applying virtualization to code section. Protected code by common used virtualization obfuscation technique has become known that it doesn't have restored point and also it is hard to analyze. However, it is abused to protect malware recently. So, It is been hard to analyze and take action for malware. Therefore, this paper's purpose is analyze and take action for protected malware by virtualization obfuscation technique through implement tool which can extract virtualization structure automatically and trace execution process. Hence, basic structure and operation process of virtualization obfuscation technique will be handled and analysis result of protected malware by virtualization obfuscation utilized Equation Reasoning System, one kind of program analysis. Also, we implement automatic analysis tool, extract virtualization structure from protected executable file by virtualization obfuscation technique and deduct program's execution sequence.