Classifying malware using convolutional gated neural network

Kim, Chang Hoon; Kabanga, Espoir; Kang, Sin-Jae

doi:10.23919/icact.2018.8323640

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…Our proposed models are better than Gibert et al [27] in terms of accuracy, but F1 score of RCNF is very close to this model. Our proposed models are obviously better than models of Cao et al [26], Zhao et al [32], Kim et al [35] and Kim et al [36]. Jung et al [31] propose a reasonably smaller model than our models in terms of number of parameters, but our model has higher accuracy score than this model.…”

Section: Capsnetmentioning

confidence: 56%

Random CapsNet Forest Model for Imbalanced Malware Type Classification Task

Çayır¹,

Ünal²,

Dağ³

2019

Preprint

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Behavior of a malware varies with respect to malware types. Therefore, knowing type of a malware affects strategies of system protection softwares. Many malware type classification models empowered by machine and deep learning achieve superior accuracies to predict malware types. Machine learning based models need to do heavy feature engineering and feature engineering is dominantly effecting performance of models. On the other hand, deep learning based models require less feature engineering than machine learning based models. However, traditional deep learning architectures and components cause very complex and data sensitive models. Capsule network architecture minimizes this complexity and data sensitivity unlike classical convolutional neural network architectures. This paper proposes an ensemble capsule network model based on bootstrap aggregating technique. The proposed method are tested on two malware datasets, whose the-state-of-the-art results are well-known.

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Section: Capsnetmentioning

confidence: 56%

Random CapsNet Forest Model for Imbalanced Malware Type Classification Task

Çayır¹,

Ünal²,

Dağ³

2019

Preprint

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show abstract

“…LSTM was handling large scale branch sequencing. CNN combined with GRU was utilized for malware classification in [291]. This network has the ability to classify nine different malware families and has 92.6 percentage accuracy.…”

Section: ) Deep Neural Network (Dnn)mentioning

confidence: 99%

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

Ravi¹,

Alazab²,

Sriram³

et al. 2020

Preprint

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“…Ozkan et al [11] contributed the CNN features to overcome the malware detection problem. Kim et al [12] proposed a convolutional gated recurrent neural network model that is capable of classifying malware to their respective families. Although there are already some researches on malware detection that utilizes deep learning to detect malicious code, most of them are classification.…”

Section: Introductionmentioning

confidence: 99%

A Worm Detection System Based on Deep Learning

Zhou

Yang

et al. 2020

IEEE Access

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In today's cyber world, worms pose a great threat to the global network infrastructure. In this paper, we propose a worm detection system based on deep learning. It includes two main modules: one worm detection module based on a convolutional neural network (CNN) and one automatic worm signature generation module based on a deep neural network (DNN). In the CNN-based worm detection module, we propose three kinds of data preprocessing methods: frequency processing, frequency weighted processing, and difference processing, and use CNN to train the model for worm detection. In the DNN-based worm signature generation module, there are two phrase:DNN is firstly utilized for training the model with worm payloads and their corresponding signatures as input in the training phrase. After worm payloads are fed into the trained DNN model in the test phrase, worm signatures are generated by our proposed Signature Beam Search algorithm. In the experiment, we firstly analyzed the impact of different data preprocessing methods and the number of convolution-pooling layers in the CNN model on the worm detection performance. Then we analyzed the effects of different signatures in the DNN algorithm on the automatic generation of worm signatures. Experiments show that the generated signatures have a good detection performance.

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Classifying malware using convolutional gated neural network

Cited by 8 publications

References 3 publications

Random CapsNet Forest Model for Imbalanced Malware Type Classification Task

Random CapsNet Forest Model for Imbalanced Malware Type Classification Task

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

A Worm Detection System Based on Deep Learning

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