Performance evaluation of deep neural network on malware detection: visual feature approach

Anandhi, V.; Vinod, P.; Menon, Varun G.; Aditya, K

doi:10.1007/s10586-022-03702-3

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Consequently, it is imperative to develop effective countermeasures and mitigation techniques to enhance model robustness against adversarial examples. In this context, referencing countermeasures proposed in previous works to combat adversarial attacks is pertinent [6,41]. These countermeasures encompass model encryption, a protective measure that safeguards the model's core and complicates attempts to manipulate model weights and parameters.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Robustness of Deep Learning Models against Adversarial Attacks: An Analysis with FGSM, PGD and CW

Villegas-Ch,

Jaramillo-Alcázar,

Luján-Mora

2024

BDCC

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This study evaluated the generation of adversarial examples and the subsequent robustness of an image classification model. The attacks were performed using the Fast Gradient Sign method, the Projected Gradient Descent method, and the Carlini and Wagner attack to perturb the original images and analyze their impact on the model’s classification accuracy. Additionally, image manipulation techniques were investigated as defensive measures against adversarial attacks. The results highlighted the model’s vulnerability to conflicting examples: the Fast Gradient Signed Method effectively altered the original classifications, while the Carlini and Wagner method proved less effective. Promising approaches such as noise reduction, image compression, and Gaussian blurring were presented as effective countermeasures. These findings underscore the importance of addressing the vulnerability of machine learning models and the need to develop robust defenses against adversarial examples. This article emphasizes the urgency of addressing the threat posed by harmful standards in machine learning models, highlighting the relevance of implementing effective countermeasures and image manipulation techniques to mitigate the effects of adversarial attacks. These efforts are crucial to safeguarding model integrity and trust in an environment marked by constantly evolving hostile threats. An average 25% decrease in accuracy was observed for the VGG16 model when exposed to the Fast Gradient Signed Method and Projected Gradient Descent attacks, and an even more significant 35% decrease with the Carlini and Wagner method.

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Section: Discussionmentioning

confidence: 99%

Evaluating the Robustness of Deep Learning Models against Adversarial Attacks: An Analysis with FGSM, PGD and CW

Villegas-Ch,

Jaramillo-Alcázar,

Luján-Mora

2024

BDCC

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“…The F1-score for the model in detection is 93.1%. Anandhi et al in year 2022 [25] use a model named DenseNet for malware detection by the network. The two datasets used for the experiment contain the Malimg and BIG2015.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Malware Attacks using Artiﬁcial Neural Network

Rana,

Minhaj Ahmad Khan

2023

VAWKUM trans. comput. sci.

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Malware attacks are increasing rapidly as the technology continues to become prevalent. These attacks have become extremely difficult to detect as they continuously change their mechanism for exploitation of vulnerabilities in software. The conventional approaches to malware detection become ineffective due to a large number of varying patterns and sequences, thereby requiring artificial intelligence-based approaches for the detection of malware attacks. In this paper, we propose an artificial neural network-based model for malware detection. Our proposed model is generic as it can be applied to multiple datasets. We have compared our model with different machine-learning approaches. The experimentation results show that the proposed model can outperform other well-known approach as it achieves 99.6\% , 98.9\% and 99.9\% accuracy on the Windows API call dataset, Top PE Imports Dataset and Malware Dataset, respectively.

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