Automatic Malicious Code Classification System through Static Analysis Using Machine Learning

Kim, Sung-Joong; Yeom, Seongkyu; Oh, Haengrok; Shin, Dongil; Shin, Dongkyoo

doi:10.3390/sym13010035

Cited by 11 publications

(11 citation statements)

References 10 publications

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“…In the learning process of the CAM method, the value that has the greatest influence on the normal/malignant classification is identified inversely to confirm the part of the input byte information that affects the classification. This process is illustrated in Figure 8 [15]. The validity of the proposed model (Malconv) was verified by comparing its accuracy with that of the learning model using byte block and file meta information as feature information by NVIDIA.…”

Section: Research On Nvidia's Dl-based Malware Detection Technologymentioning

confidence: 96%

“…As shown in Figure 7, NVIDIA conducted an experiment to detect malicious files by training a DL model with the byte information of files to detect malicious files in Windows executable files. As for the system configuration, the system for detecting malicious files consisted of preprocessing the byte information of executable files, training a DL model with the byte information, and classifying normal and malicious files [14,15]. To preprocess the input value for learning the DL model, the byte information (hexadecimal) of the executable file was converted into a form suitable for the input value of the learning algorithm.…”

Section: Research On Nvidia's Dl-based Malware Detection Technologymentioning

confidence: 99%

Section: Research On Nvidia's Dl-based Malware Detection Technologymentioning

confidence: 99%

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Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework

et al. 2022

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With advances in cyber threats and increased intelligence, incidents continue to occur related to new ways of using new technologies. In addition, as intelligent and advanced cyberattack technologies gradually increase, the limit of inefficient malicious code detection and analysis has been reached, and inaccurate detection rates for unknown malicious codes are increasing. Thus, this study used a machine learning algorithm to achieve a malicious file detection accuracy of more than 99%, along with a method for visualizing data for the detection of malicious files using the dynamic-analysis-based MITRE ATT&CK framework. The PE malware dataset was classified into Random Forest, Adaboost, and Gradient Boosting models. These models achieved accuracies of 99.3%, 98.4%, and 98.8%, respectively, and malicious file analysis results were derived through visualization by applying the MITRE ATT&CK matrix.

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Section: Research On Nvidia's Dl-based Malware Detection Technologymentioning

confidence: 96%

Section: Research On Nvidia's Dl-based Malware Detection Technologymentioning

confidence: 99%

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Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework

et al. 2022

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“…Source code can be retrieved from the APK file or Portable Executable (PE) file to perform the static analysis. In [70],…”

Section: Machinementioning

confidence: 99%

Android Source Code Vulnerability Detection: A Systematic Literature Review

et al. 2023

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The use of mobile devices is rising daily in this technological era. A continuous and increasing number of mobile applications are constantly offered on mobile marketplaces to fulfil the needs of smartphone users. Many Android applications do not address the security aspects appropriately. This is often due to a lack of automated mechanisms to identify, test, and fix source code vulnerabilities at the early stages of design and development. Therefore, the need to fix such issues at the initial stages rather than providing updates and patches to the published applications is widely recognized. Researchers have proposed several methods to improve the security of applications by detecting source code vulnerabilities and malicious codes. This Systematic Literature Review (SLR) focuses on Android application analysis and source code vulnerability detection methods and tools by critically evaluating 118 carefully selected technical studies published between 2016 and 2022. It highlights the advantages, disadvantages, applicability of the proposed techniques and potential improvements of those studies. Both Machine Learning (ML) based methods and conventional methods related to vulnerability detection are discussed while focusing more on ML-based methods since many recent studies conducted experiments with ML. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in secure mobile application development while minimizing the vulnerabilities by applying ML methods. Furthermore, researchers can use the discussions and findings of this SLR to identify potential future research and development directions.

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“…Thirty-two supervised ML algorithms were considered for most common vulnerabilities and identified that when the model used the J48 ML algorithm, 96% accuracy could be obtained in vulnerability detection. The model proposed in [123] discussed an automated mechanism to classify well-written and malicious code using a portable executable (PE) structure through static analysis and ML with an accuracy of 98.77%. The proposed methodology used RF, GB, DT, and CNN as ML models.…”

Section: Applying ML To Detect Source Code Vulnerabilitiesmentioning

confidence: 99%

Android Mobile Malware Detection Using Machine Learning: A Systematic Review

2021

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With the increasing use of mobile devices, malware attacks are rising, especially on Android phones, which account for 72.2% of the total market share. Hackers try to attack smartphones with various methods such as credential theft, surveillance, and malicious advertising. Among numerous countermeasures, machine learning (ML)-based methods have proven to be an effective means of detecting these attacks, as they are able to derive a classifier from a set of training examples, thus eliminating the need for an explicit definition of the signatures when developing malware detectors. This paper provides a systematic review of ML-based Android malware detection techniques. It critically evaluates 106 carefully selected articles and highlights their strengths and weaknesses as well as potential improvements. Finally, the ML-based methods for detecting source code vulnerabilities are discussed, because it might be more difficult to add security after the app is deployed. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in the field and to identify potential future research and development directions.

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Automatic Malicious Code Classification System through Static Analysis Using Machine Learning

Cited by 11 publications

References 10 publications

Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework

Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework

Android Source Code Vulnerability Detection: A Systematic Literature Review

Android Mobile Malware Detection Using Machine Learning: A Systematic Review

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