• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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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.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.555-556
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• 2021

본 논문은 악성코드의 이미지화와 전이학습을 이용한 악성코드 분류 방안을 제안한다. 공개된 악성코드는 쉽게 재사용 또는 변형이 가능하다. 그런데 전통적인 악성코드 탐지 기법은 변형된 악성코드를 탐지하는데 취약하다. 동일한 부류에 속하는 악성코드들은 서로 유사한 이미지로 변환된다. 따라서 제안하는 기법은 악성코드를 이미지화하고 이미지 분류 분야에서 검증된 딥러닝 모델을 사용하여 악성코드의 부류를 분류한다. Malimg 데이터셋에 대해 VGG-16 모델을 이용하여 실험한 결과 98% 이상의 분류 정확도를 나타냈다.

• International journal of advanced smart convergence
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• 제12권1호
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• pp.59-63
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• 2023

We create a malware detector classification method with using the Sequential Probability Ratio Test (SPRT) in IoT. More specifically, we adapt the SPRT to classify malware detectors into two categories of basic and advanced in line with malware detection capability. We perform evaluation of our scheme through simulation. Our simulation results show that the number of advanced detectors is changed in line with threshold for fraction of advanced malware information, which is used to judge advanced detectors in the SPRT.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.49-62
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• 2023

There has been a rapid increase in the creation and alteration of new malware samples which is a huge financial risk for many organizations. There is a huge demand for improvement in classification and detection mechanisms available today, as some of the old strategies like classification using mac learning algorithms were proved to be useful but cannot perform well in the scalable auto feature extraction scenario. To overcome this there must be a mechanism to automatically analyze malware based on the automatic feature extraction process. For this purpose, the dynamic analysis of real malware executable files has been done to extract useful features like API call sequence and opcode sequence. The use of different hashing techniques has been analyzed to further generate images and convert them into image representable form which will allow us to use more advanced classification approaches to classify huge amounts of images using deep learning approaches. The use of deep learning algorithms like convolutional neural networks enables the classification of malware by converting it into images. These images when fed into the CNN after being converted into the grayscale image will perform comparatively well in case of dynamic changes in malware code as image samples will be changed by few pixels when classified based on a greyscale image. In this work, we used VGG-16 architecture of CNN for experimentation.

• 정보과학회 논문지
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• 제42권5호
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• pp.558-565
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• 2015

최근 새롭게 제작되는 악성코드 수의 증가와 악성코드 변종들의 다양성은 악성코드 분석가의 분석에 소요되는 시간과 노력에 많은 영향을 준다. 따라서 효과적인 악성코드 분류는 악성코드 분석가의 악성코드 분석에 소요되는 시간과 노력을 감소시키는 데 도움을 줄 뿐만 아니라, 악성코드 계보 연구 등 다양한 분야에 활용 가능하다. 본 논문에서는 악성코드 분류를 위해 중요 연산부호를 이용하는 방법을 제안한다. 중요 연산부호란 악성코드 분류에 높은 영향력을 가지는 연산부호들을 의미한다. 실험을 통해서 악성코드 분류에 높은 영향력을 가지는 상위 10개의 연산부호들을 중요 연산부호로 선정할 수 있음을 확인하였으며, 이를 이용할 경우 지도학습 알고리즘의 학습시간을 약 91% 단축시킬 수 있었다. 이는 향후 다량의 악성코드 분류 연구에 응용 가능할 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.4105-4121
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• 2021

Recently, malware classification based on Deep Neural Networks (DNN) has gained significant attention due to the rise in popularity of artificial intelligence (AI). DNN-based malware classifiers are a novel solution to combat never-before-seen malware families because this approach is able to classify malwares based on structural characteristics rather than requiring particular signatures like traditional malware classifiers. However, these DNN-based classifiers have been found to lack robustness against malwares that are carefully crafted to evade detection. These specially crafted pieces of malware are referred to as adversarial examples. We consider a clever adversary who has a thorough knowledge of DNN-based malware classifiers and will exploit it to generate a crafty malware to fool DNN-based classifiers. In this paper, we propose a DNN-based malware classifier that becomes resilient to these kinds of attacks by exploiting Generative Adversarial Network (GAN) based data augmentation. The experimental results show that the proposed scheme classifies malware, including AEs, with a false positive rate (FPR) of 3.0% and a balanced accuracy of 70.16%. These are respective 26.1% and 18.5% enhancements when compared to a traditional DNN-based classifier that does not exploit GAN.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.454-475
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• 2018

This paper presents a novel Android malware classification model planned to classify and categorize Android malicious code at Drebin dataset. The amount of malicious mobile application targeting Android based smartphones has increased rapidly. In this paper, Restricted Boltzmann Machine and Deep Belief Network are used to classify malware into families of Android application. A texture-fingerprint based approach is proposed to extract or detect the feature of malware content. A malware has a unique "image texture" in feature spatial relations. The method uses information on texture image extracted from malicious or benign code, which are mapped to uncompressed gray-scale according to the texture image-based approach. By studying and extracting the implicit features of the API call from a large number of training samples, we get the original dynamic activity features sets. In order to improve the accuracy of classification algorithm on the features selection, on the basis of which, it combines the implicit features of the texture image and API call in malicious code, to train Restricted Boltzmann Machine and Back Propagation. In an evaluation with different malware and benign samples, the experimental results suggest that the usability of this method---using Deep Belief Network to classify Android malware by their texture images and API calls, it detects more than 94% of the malware with few false alarms. Which is higher than shallow machine learning algorithm clearly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1466-1488
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• 2022

Recently, the importance and necessity of artificial intelligence (AI), especially machine learning, has been emphasized. In fact, studies are actively underway to solve complex and challenging problems through the use of AI systems, such as intelligent CCTVs, intelligent AI security systems, and AI surgical robots. Information security that involves analysis and response to security vulnerabilities of software is no exception to this and is recognized as one of the fields wherein significant results are expected when AI is applied. This is because the frequency of malware incidents is gradually increasing, and the available security technologies are limited with regard to the use of software security experts or source code analysis tools. We conducted a study on MalDC, a technique that converts malware into images using machine learning, MalDC showed good performance and was able to analyze and classify different types of malware. MalDC applies a preprocessing step to minimize the noise generated in the image conversion process and employs an image augmentation technique to reinforce the insufficient dataset, thus improving the accuracy of the malware classification. To verify the feasibility of our method, we tested the malware classification technique used by MalDC on a dataset provided by Microsoft and malware data collected by the Korea Internet & Security Agency (KISA). Consequently, an accuracy of 97% was achieved.

• 정보보호학회논문지
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• 제28권5호
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• pp.1161-1167
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• 2018

기하급수적으로 증가하고 있는 변종 악성코드에 대응하기 위해 악성코드 분류 연구가 다양화되고 있다. 최근 연구에서는 기존 악성코드 분석 기술 (정적/동적)의 개별 사용 한계를 파악하고, 각 방식을 혼합한 하이브리드 분석으로 전환하는 추세이다. 나아가, 분류가 어려운 변종 악성코드를 더욱 정확하게 식별하기 위해 기계학습을 적용하기에 이르렀다. 하지만, 각 방식을 모두 활용했을 때 발생하는 정확성, 확장성 트레이드오프 문제는 여전히 해결되지 못했으며, 학계에서 중요한 연구 주제이다. 이에 따라, 본 연구에서는 기존 악성코드 분류 연구들의 문제점을 보완하기 위해 새로운 악성코드 분류 시스템을 연구 및 개발한다.

• 정보보호학회논문지
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• 제24권5호
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• pp.897-909
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• 2014

인터넷이 지속적으로 발달하면서 이에 따른 부작용으로 사이버 해킹 공격 또한 지능적인 공격으로 진화하고 있다. 해킹 공격의 도구로 사용되는 악성코드는 공격자들이 자동 제작 툴을 이용해 손쉽게 악성코드를 생성할 수 있기 때문에 악성코드의 수가 급증하고 있다. 그러나 수많은 악성코드를 모두 분석하기에는 많은 시간과 노력이 요구됨에 따라 신 변종 악성코드에 대한 별도의 분류가 필요한 상황이다. 이에 따라 신 변종 악성코드를 분류하는 다양한 연구들이 등장하고 있으며, 해당 연구들은 악성코드 분석을 통해 악성 행위를 나타내는 다양한 정보를 추출하고 이를 악성코드를 대표하는 특징으로 정의하여 악성코드를 분류한다. 그 중, 대부분이 API 함수와 API 함수로부터 추출한 특정 길이의 API 시퀀스를 이용하여 악성코드를 분류하고 있다. 그러나 API 시퀀스의 길이는 분류의 정확성에 영향을 미치기 때문에 적합한 API 시퀀스의 길이를 선택하는 것이 매우 중요하다. 따라서 본 논문은 특정 길이에 한정하지 않고, 다양한 길이의 API 시퀀스를 생성 및 조합하여 악성코드 분류의 정확성을 향상시키기 위한 최적의 API 시퀀스 및 조합을 찾는 방법론을 제안한다.