Mal2d: 2d Based Deep Learning Model for Malware Detection Using Black and White Binary Image

Cho, Minkyoung; Kim, Jik-Soo; Shin, Jong‐Ho; Shin, Ik‐Soo

doi:10.1587/transinf.2019edl8146

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Image-based malware detection methods leverage static analysis techniques to convert malware binaries into pix-based images, which owns the ability to mitigate code obfuscation and encoding issues [35][36][37][38][39][40]. These images can be fed into mature image classification techniques for identifying malicious samples.…”

Section: Image-based Malware Detection Methodsmentioning

confidence: 99%

BejaGNN: behavior-based Java malware detection via graph neural network

Feng

Yang

et al. 2023

J Supercomput

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As a popular platform-independent language, Java is widely used in enterprise applications. In the past few years, language vulnerabilities exploited by Java malware have become increasingly prevalent, which cause threats for multi-platform. Security researchers continuously propose various approaches for fighting against Java malware programs. The low code path coverage and poor execution efficiency of dynamic analysis limit the large-scale application of dynamic Java malware detection methods. Therefore, researchers turn to extracting abundant static features to implement efficient malware detection. In this paper, we explore the direction of capturing malware semantic information by using graph learning algorithms and present BejaGNN (Behavior-based Java malware detection via Graph Neural Network), a novel behavior-based Java malware detection method using static analysis, word embedding technique, and graph neural network. Specifically, BejaGNN leverages static analysis techniques to extract ICFGs (Inter-procedural Control Flow Graph) from Java program files and then prunes these ICFGs to remove noisy instructions. Then, word embedding techniques are adopted to learn semantic representations for Java bytecode instructions. Finally, BejaGNN builds a graph neural network classifier to determine the maliciousness of Java programs. Experimental results on a public Java bytecode benchmark demonstrate that BejaGNN achieves high F 1 98.8% and is superior to existing Java malware detection approaches, which verifies the promise of graph neural network in Java malware detection.

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Section: Image-based Malware Detection Methodsmentioning

confidence: 99%

BejaGNN: behavior-based Java malware detection via graph neural network

Feng

Yang

et al. 2023

J Supercomput

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“…Other papers that focus on Windows-based malware detection include [53][54][55][56][57][58][59][60][61][62][63][64].…”

Section: Windows Malware Detectionmentioning

confidence: 99%

“…CNN [47,112,52,62,36,68,89,77,35,46,56,80,84,70,92,27,51,121,87,34,54,50,76,95,29,100,82,83,114,42,57,79,40,74,63,73,66,26,39,30,119,101,110,61,32,65,120 quality of dataset should be carefully considered when creating and developing predictive models and tools for malware detection [148]. Such datasets are created by the research community to serve as a source of research for empirical analysis and extracting new insights about apps.…”

Section: Algorithmsmentioning

confidence: 99%

Deep Learning Methods for Malware and Intrusion Detection: A Systematic Literature Review

Ali

Iqbal

et al. 2022

Security and Communication Networks

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Android and Windows are the predominant operating systems used in mobile environment and personal computers and it is expected that their use will rise during the next decade. Malware is one of the main threats faced by these platforms as well as Internet of Things (IoT) environment and the web. With time, these threats are becoming more and more sophisticated and detecting them using traditional machine learning techniques is a hard task. Several research studies have shown that deep learning methods achieve better accuracy comparatively and can learn to efficiently detect and classify new malware samples. In this paper, we present a systematic literature review of the recent studies that focused on intrusion and malware detection and their classification in various environments using deep learning techniques. We searched five well-known digital libraries and collected a total of 107 papers that were published in scholarly journals or preprints. We carefully read the selected literature and critically analyze it to find out which types of threats and what platform the researchers are targeting and how accurately the deep learning-based systems can detect new security threats. This survey will have a positive impact on the learning capabilities of beginners who are interested in starting their research in the area of malware detection using deep learning methods. From the detailed critical analysis, it is identified that CNN, LSTM, DBN, and autoencoders are the most frequently used deep learning methods that have effectively been used in various application scenarios.

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“…Image-based malware classification algorithms can overcome code obfuscation or encoding difficulties, comparable to and more advanced than static analysis approaches [8]. These image-based methods employ static analysis techniques to turn malware samples into image representations [10]. Conti et al [11]conducted the first research on the visualisation of binary data as images.…”

Section: B Visualization Techniquementioning

confidence: 99%

Deep Image: A precious image based deep learning method for online malware detection in IoT Environment

Ghahramani¹,

Taheri²,

Shojafar³

et al. 2022

Preprint

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The volume of malware and the number of attacks in IoT devices are rising everyday, which encourages security professionals to continually enhance their malware analysis tools. Researchers in the field of cyber security have extensively explored the usage of sophisticated analytics and the efficiency of malware detection. With the introduction of new malware kinds and attack routes, security experts confront considerable challenges in developing efficient malware detection and analysis solutions. In this paper, a different view of malware analysis is considered and the risk level of each sample feature is computed, and based on that the risk level of that sample is calculated. In this way, a criterion is introduced that is used together with accuracy and FPR criteria for malware analysis in IoT environment. In this paper, three malware detection methods based on visualization techniques called the clustering approach, the probabilistic approach, and the deep learning approach are proposed. Then, in addition to the usual machine learning criteria namely accuracy and FPR, a proposed criterion based on the risk of samples has also been used for comparison, with the results showing that the deep learning approach performed better in detecting malware.

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Mal2d: 2d Based Deep Learning Model for Malware Detection Using Black and White Binary Image

Cited by 6 publications

References 10 publications

BejaGNN: behavior-based Java malware detection via graph neural network

BejaGNN: behavior-based Java malware detection via graph neural network

Deep Learning Methods for Malware and Intrusion Detection: A Systematic Literature Review

Deep Image: A precious image based deep learning method for online malware detection in IoT Environment

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