• Journal of Information Processing Systems
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• v.16 no.4
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• pp.882-895
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• 2020

The amount of malware increases exponentially every day and poses a threat to networks and operating systems. Most new malware is a variant of existing malware. It is difficult to deal with numerous malware variants since they bypass the existing signature-based malware detection method. Thus, research on automated methods of detecting and processing variant malware has been continuously conducted. This report proposes a method of extracting feature data from files and detecting malware using machine learning. Feature data were extracted from 7,000 malware and 3,000 benign files using static and dynamic malware analysis tools. A malware classification model was constructed using multiple DNN, XGBoost, and RandomForest layers and the performance was analyzed. The proposed method achieved up to 96.3% accuracy.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3756-3777
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• 2019

Recent internet development is helping malware researchers to generate malicious code variants through automated tools. Due to this reason, the number of malicious variants is increasing day by day. Consequently, the performance improvement in malware analysis is the critical requirement to stop the rapid expansion of malware. The existing research proved that the similarities among malware variants could be used for detection and family classification. In this paper, a Cross-Platform Malware Variant Classification System (CP-MVCS) proposed that converted malware binary into a grayscale image. Further, malicious features extracted from the grayscale image through Combined SIFT-GIST Malware (CSGM) description. Later, these features used to identify the relevant family of malware variant. CP-MVCS reduced computational time and improved classification accuracy by using CSGM feature description along machine learning classification. The experiment performed on four publically available datasets of Windows OS and Android OS. The experimental results showed that the computation time and malware classification accuracy of CP-MVCS was higher than traditional methods. The evaluation also showed that CP-MVCS was not only differentiated families of malware variants but also identified both malware and benign samples in mix fashion efficiently.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.357-367
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• 2020

According to Symantec's 2018 internet security threat report, The number of new mobile malware variants increased by 54 percent in 2017, as compared to 2016. And last year, there were an average of 24,000 malicious mobile applications blocked each day. Existing signature-based technologies of malware detection have limitations. So, malware detection technique through machine learning is being researched to detect malware variant. However, even in the case of applying machine learning, if the proper features of the malware are not properly selected, the machine learning cannot be shown correctly. We are focusing on feature selection method to find the features of malware variant in this research.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.1-9
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• 2021

Traditionally, most malicious codes have been analyzed using feature information extracted by domain experts. However, this feature-based analysis method depends on the analyst's capabilities and has limitations in detecting variant malicious codes that have modified existing malicious codes. In this study, we propose a ResNet-Variational AutoEncder-based variant malware classification method that can classify a family of variant malware without domain expert intervention. The Variational AutoEncoder network has the characteristics of creating new data within a normal distribution and understanding the characteristics of the data well in the learning process of training data provided as input values. In this study, important features of malicious code could be extracted by extracting latent variables in the learning process of Variational AutoEncoder. In addition, transfer learning was performed to better learn the characteristics of the training data and increase the efficiency of learning. The learning parameters of the ResNet-152 model pre-trained with the ImageNet Dataset were transferred to the learning parameters of the Encoder Network. The ResNet-Variational AutoEncoder that performed transfer learning showed higher performance than the existing Variational AutoEncoder and provided learning efficiency. Meanwhile, an ensemble model, Stacking Classifier, was used as a method for classifying variant malicious codes. As a result of learning the Stacking Classifier based on the characteristic data of the variant malware extracted by the Encoder Network of the ResNet-VAE model, an accuracy of 98.66% and an F1-Score of 98.68 were obtained.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1161-1167
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• 2018

In order to cope with dramatically increasing malware variant, malware classification research is getting diversified. Recent research tend to grasp individual limits of existing malware analysis technology (static/dynamic), and to change each method into "hybrid analysis", which is to mix different methods into one. Futhermore, it is applying machine learning to identify malware variant more accurately, which are difficult to classify. However, accuracy and scalability of trade-off problems that occur when using all kinds of methods are not yet to be solved, and it is still an important issue in the field of malware research. Therefore, to supplement and to solve the problems of the original malware classification research, we are focusing on developing a new malware classification system in this research.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.291-297
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• 2021

Recently, as the number of new and variant malicious codes has increased exponentially, malware warnings are being issued to PC and smartphone users. Malware is becoming more and more intelligent. Efforts to protect personal information are becoming more and more important as social issues are used to stimulate the interest of PC users and allow users to directly download malicious codes. In this way, it is difficult to prevent malicious code because malicious code infiltrates in various forms. As a countermeasure to solve these problems, many studies are being conducted to apply deep learning. In this paper, we investigate and analyze various deep learning methods to detect and classify malware.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.2
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• pp.321-330
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• 2015

There have been found many modified malwares which could avoid detection simply by replacing a sequence of characters or a part of code. Since the existing anti-virus program performs signature-based analysis, it is difficult to detect a malware which is slightly different from the well-known malware. This paper suggests a method of detecting modified malwares by extending a hash-value based code comparison. We generated hash values for individual functions and individual code blocks as well as the whole code, and thus use those values to find whether a pair of codes are similar in a certain degree. We also eliminated some numeric data such as constant and address before generating hash values to avoid incorrectness incurred from them. We found that the suggested method could effectively find inherent similarity between original malware and its derived ones.

• 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.