Jadeite: A novel image-behavior-based approach for Java malware detection using deep learning

Obaidat, Islam; Sridhar, Meera; Khue, Pham Minh; Phung, Phu H.

doi:10.1016/j.cose.2021.102547

Cited by 24 publications

(18 citation statements)

References 22 publications

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“…Both signature-based and behavior-based techniques are used for malware detection. The signature-based approach is useful for identifying known threats without producing an excessive number of false alarms [8], but it needs regular manual updates of the database containing rules and signatures. According to [9], a malware detection strategy in the Internet of Things environment based on a similarity hashing algorithm has been presented.…”

Section: A Malware Analysismentioning

confidence: 99%

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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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Section: A Malware Analysismentioning

confidence: 99%

“…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].…”

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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“…Next, a CNN classifier is used to classify the grayscale images mapped from the two-dimensional arrays. Jadeite [16] proposes to generate grayscale images from ICFGs of Java bytecode files, which considers the program's semantics information and improves the detection performance. Nevertheless, the image processing-based approach, or even on top of static analysis results with semantic information, makes malware detection a completer black box problem, which limits the exploration of attack laws and increases the difficulty in providing interpretable results.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, dynamic analysis techniques can hardly cover all execution paths and cause more time and resource consumption for the execution environment, which limits the practicality of these methods. Recently, researchers have explored image-based malware detection approaches which leverage the booms of ML algorithms in computer vision [14][15][16]. These approaches usually covert raw malware binaries into pixel-based images via various transformation methods.…”

Section: Introductionmentioning

confidence: 99%

“…Most of these approaches treat machine code as the pixel values and convert the binary files into grayscale images [14]. Jadeite [16] adopts an image transformation method on ICFG of Java bytecode, which improves the accuracy of imagebased malware detection methods. Nevertheless, these image transformation methods, directly using image processing techniques, do not carry the semantics within the malware binaries, which limits the ability of the ML algorithm and the exploration of malware attack evolution law.…”

Section: Introductionmentioning

confidence: 99%

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BejaGNN: Behavior-based Java Malware Detection via Graph Neural Network

Feng

Yang

et al. 2022

Preprint

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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. However, the presence of complex hidden techniques, such as code obfuscation, makes identifying complicated Java malware become challenging. Therefore, there is an urgent need to develop new approaches for resisting hidden techniques. In this paper, we present BejaGNN, 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 from Java program files and then prunes these ICFGs to remove noisy instructions. Then, work 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 F1 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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Penguin Search Optimization with Deep Learning Based Cybersecurity Malware Spectrogram Image Classification

Jeyalakshmi,

Santhiya,

Jegatha

2024

Communications in Computer and Information Science

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Jadeite: A novel image-behavior-based approach for Java malware detection using deep learning

Cited by 24 publications

References 22 publications

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

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

BejaGNN: Behavior-based Java Malware Detection via Graph Neural Network

Penguin Search Optimization with Deep Learning Based Cybersecurity Malware Spectrogram Image Classification

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