EDAroid

Kim, Heejin; Kim, Kyuho; Hong, Jiman; Heo, Jae-Young; Kook, Joongjin

doi:10.1145/3400286.3418266

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…To evade the malware detection model, the attacker needs to extract features [6,7] and compute feature values that can evade detection without damaging the malware. This often requires knowledge of the structure inside the detector.…”

Section: Overviewmentioning

confidence: 99%

Code obfuscation based adversarial example generation method

Li¹

2023

International Conference on Internet of Things and Machine Learning (IoTML 2022)

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Malware is still a big threat to network security, and various types of malware detectors are needed. Deep learning-based classifiers have substantially improved their ability to identify malware samples. However, these detectors suffer from adversarial examples. The samples were made by adding small, carefully selected perturbations to the normal software. Any vulnerability in malware detectors can pose a significant threat to the platforms they defend. However, existing attack methods may not meet the inherent limitations of malware. This paper proposes a new method to generate malware adversarial samples. The original malware is mutated into new samples through semantic analysis of malware, transplantation of code in the program and addition of code at the end. And used to fool the detector. Experiments show that compared with the existing methods, our method has a significant effect on the efficiency of generating adversarial examples and the success rate of the attack.

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Section: Overviewmentioning

confidence: 99%

Code obfuscation based adversarial example generation method

Li¹

2023

International Conference on Internet of Things and Machine Learning (IoTML 2022)

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An Automated Vision-Based Deep Learning Model for Efficient Detection of Android Malware Attacks

2022

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Recently, cybersecurity experts and researchers have given special attention to developing cost-effective deep learning (DL)-based algorithms for Android malware detection (AMD) systems. However, the conventional AMD solutions necessitate extensive computations to achieve high accuracy in detecting Android malware apps. Consequently, there is a significant benefit in utilizing convolution neural networks (CNNs) in vision-based AMD applications to quickly and efficiently learn without prior stages of reverse engineering processes. Thus, this paper introduces an efficient and automated vision-based AMD model composed of 16 well-developed and fine-tuned CNN algorithms. This model precludes the need for a pre-designated features extraction process while generating accurate predictions of malware images with minimum cost and high detection speed. Such performance is achieved with colored or grayscale malware images, whether by using balanced or imbalanced datasets. Firstly, the bytecodes of the "classes.dex" files extracted from the Android benign and malware apps were converted to color and grayscale visual images before forwarding them to the developed CNN algorithms for classification. Then, the detection efficiency of the proposed AMD model was examined and evaluated using the imbalanced benchmark Leopard Android dataset that composes 14733 samples of malware apps and 2486 samples of benign apps. Finally, different experimental scenarios were conducted using balanced and imbalanced Android samples of color and grayscale images generated from the Leopard dataset; to extensively and sufficiently validate the detection and classification performance of the suggested model. A comprehensive assessment classification parameters in the evaluation experiments were applied to prove the high capability of the developed finetuned CNN algorithms in recognizing Android malware attacks with low computational overhead. As a result, the detection accuracy reached 99.40% for balanced samples and 98.05% for imbalanced samples. Furthermore, the proposed AMD model outperforms the existing approaches that utilize conventional vision-based algorithms and tested on the same benchmark Android dataset.

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EDAroid

Cited by 2 publications

References 14 publications

Code obfuscation based adversarial example generation method

Code obfuscation based adversarial example generation method

An Automated Vision-Based Deep Learning Model for Efficient Detection of Android Malware Attacks

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