Feature selection‐based android malware adversarial sample generation and detection method

Li, Xiangjun; Kong, Ke; Xu, Su Xiu; Qin, Pengtao; He, Daojing

doi:10.1049/ise2.12030

Cited by 13 publications

(4 citation statements)

References 42 publications

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“…A novel feature selection was used in detecting malwares, which relies on modi ed whale optimization algorithm [32]. Malware attacks are most common among android mobiles, a new technique based on frequency differential enhancement was used in detecting android malware [33]. As malware can change their appearance or behavior to avoid detection, a e cient method was used with the combination of structural and behavioral features for reducing dimensionality and improve the quality of features [34].…”

Section: Cic Dataset Autoencoder-decoders Binarymentioning

confidence: 99%

A Robust Method for Malware Analysis Using Stacking Classifiers and Dendrogram Visualization

Nallabala

2024

Preprint

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Malware analysis is a vital and challenging task in the ever-changing cyber threat landscape. Traditional signature-based methods cannot keep up with the fast-paced evolution of malware variants. This underscores the need for developing a more effective malware classification tool helping in exploring new research directions with solutions. In this research work firstly, we present a comprehensive assessment on malware classification using dendrogram clustering techniques. This work proposes, a robust approach to malware analysis by constructing dendrogram that groups malware samples which works based on their ancestral relationships. Semantic analysis of the code also provides deeper insights into the malware’s behavior and functionality, enabling more precise identification. These methods help to detect new variants as well as enhance the recognition of existing malware families. Our proposed model results shows that, the robustness in accuracy and False Positive Rate when compared with other existing models.

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Section: Cic Dataset Autoencoder-decoders Binarymentioning

confidence: 99%

A Robust Method for Malware Analysis Using Stacking Classifiers and Dendrogram Visualization

Nallabala

2024

Preprint

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“…In recent years, many methods have been proposed to improve the robustness of models. In general, there are three kinds of defense methods, including adversarial training [15,[23][24][25], adversarial detection [26][27][28], and input transformation defenses [29][30][31][32][33][34].…”

Section: Adversarial Defensesmentioning

confidence: 99%

Boosting the Transferability of Ensemble Adversarial Attack via Stochastic Average Variance Descent

Zhao,

Liu,

et al. 2024

IET Information Security

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Adversarial examples have the property of transferring across models, which has created a great threat for deep learning models. To reveal the shortcomings in the existing deep learning models, the method of the ensemble has been introduced to the generating of transferable adversarial examples. However, most of the model ensemble attacks directly combine the different models’ output but ignore the large differences in optimization direction of them, which severely limits the transfer attack ability. In this work, we propose a new kind of ensemble attack method called stochastic average ensemble attack. Unlike the existing approach of averaging the outputs of each model as an integrated output, we continuously optimize the ensemble gradient in an internal loop using the model history gradient and the average gradient of different models. In this way, the adversarial examples can be updated in a more appropriate direction and make the crafted adversarial examples more transferable. Experimental results on ImageNet show that our method generates highly transferable adversarial examples and outperforms existing methods.

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“…The adversarial sample generation algorithm is based on feature space [15] distribution and feature filtering.…”

Section: Xiangjun LI Et Almentioning

confidence: 99%

A Method for Generating Malware Countermeasure Samples Based on Pixel Attention Mechanism

2024

KSII TIIS

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With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

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Feature selection‐based android malware adversarial sample generation and detection method

Cited by 13 publications

References 42 publications

A Robust Method for Malware Analysis Using Stacking Classifiers and Dendrogram Visualization

A Robust Method for Malware Analysis Using Stacking Classifiers and Dendrogram Visualization

Boosting the Transferability of Ensemble Adversarial Attack via Stochastic Average Variance Descent

A Method for Generating Malware Countermeasure Samples Based on Pixel Attention Mechanism

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