Applying Convolutional Neural Network for Malware Detection

Chen, Chia-Mei; Wang, Shihao; Wen, Dan-Wei; Lai, Gu-Hsin; Sun, Ming-Kung

doi:10.1109/icawst.2019.8923568

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…Chen et al [94], proposed a CNN algorithm for feature extraction because of the big volume and complexity of feature malware attacks. The challenges of malware attack detection are in the diversity and complexity of the types and structures of features that include source code, binary files, and other behaviors.…”

Section: Literature Reviewmentioning

confidence: 99%

Cyber security: performance analysis and challenges for cyber attacks detection

Salih

Abdulrazzaq

2023

IJEECS

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Nowadays, with the occurrence of new attacks and raised challenges have been facing the security of computer systems. Cyber security techniques have become essential for information technology services to detect and react against cyber-attacks. The strategy of cyber security enables visibility of various types of attacks and vulnerabilities throughout computer networks, whilst also provides detecting cyber-attacks and effective ways of identifying and preventing them. This study mainly focuses on the performance analysis and challenges faced by cyber security using the latest techniques. It also provides a review of the attack detection process including the robust effectiveness of intelligent techniques. Finally, summarize and discuss some methods to increase attack detection performance utilizing deep learning (DL) architectures.

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Section: Literature Reviewmentioning

confidence: 99%

Cyber security: performance analysis and challenges for cyber attacks detection

Salih

Abdulrazzaq

2023

IJEECS

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“…Recently, there has been lot of research involving malware detection using deep learning [40,41], recurrent neural networks (RNNs) [42,43,44], convolutional neural networks (CNNs) [45,46,47,48,49] and hybrid models [50,51,52]. The works that use CNNs, typically converts malware binaries to digital images and pass them into a CNN in order to detect malware.…”

Section: Related Workmentioning

confidence: 99%

Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning

Mohammed¹,

Nataraj

Chikkagoudar

et al. 2021

Preprint

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We propose a novel method to detect and visualize malware through image classification. The executable binaries are represented as grayscale images obtained from the count of N-grams (N=2) of bytes in the Discrete Cosine Transform (DCT) domain and a neural network is trained for malware detection. A shallow neural network is trained for classification, and its accuracy is compared with deep-network architectures such as ResNet that are trained using transfer learning. Neither dis-assembly nor behavioral analysis of malware is required for these methods. Motivated by the visual similarity of these images for different malware families, we compare our deep neural network models with standard image features like GIST descriptors to evaluate the performance. A joint feature measure is proposed to combine different features using error analysis to get an accurate ensemble model for improved classification performance.A new dataset called MaleX 1 which contains around 1 million malware and benign Windows executable samples is created for large-scale malware detection and classification experiments. Experimental results are quite promising with 96% binary classification accuracy on MaleX. The proposed model is also able to generalize well on larger unseen malware samples and the results compare favorably with state-of-the-art static analysis-based malware detection algorithms.

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