Malware Detection with Deep Neural Network Using Process Behavior

By Shun Tobiyama, Yukiko Yamaguchi, Hajime Shimada, Tomonori Ikuse, Takeshi Yagi. Annual International Computer Software and Applications Conference, 2016

309 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f2f31d7b289435d9ee5172cb92acf66bd85170f2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. V. Varadharajan
2. U. Tupakula
3. Daniel S. Berman
4. Anna L. Buczak
5. Cherita L. Corbett
6. Jeffrey S. Chavis
7. Vijay Janappa Reddi
8. Thanh Thi Nguyen

In Information Systems

1. Len Hamey
2. Mahmood Yousefi-Azar
3. V. Varadharajan
4. Anna L. Buczak

In Signal Processing

1. Len Hamey
2. Mahmood Yousefi-Azar
3. V. Varadharajan
4. U. Tupakula
5. Daniel S. Berman
6. Anna L. Buczak
7. Cherita L. Corbett
8. Jeffrey S. Chavis

Citations in the corpus

• A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “[80] built a malware detector that fed the API calls time series data into an RNN to perform feature extraction.”
• Robust Intelligent Malware Detection Using Deep Learning (2019, methodology): “To find out the hidden units for DNN, various experiments are run for the neurons in the range [4-128].”
• Autoencoder-based feature learning for cyber security applications (2017, methodology): “The models use a wide range of structures such as Convolutional Neural Network (CNN), Auto-encoders (AEs), Recurrent Neural Networks (RNNs) and Deep Belief Networks (DBN) [13, 14, 15, 16, 17, 18, 19].”