Abstract
Malware is a virus program written to enter a system and damage or alter files and data. A computer virus is more vulnerable as it makes changes or deletes files and can enter into a system as an attachment of images or audio and video files. It also infects the system through the downloads on the Internet. This paper proposes an intelligent learning model to recognize malware using convolutional neural network approach. Here, malware detection identification models through binary classifier, and two approaches of deep neural network classification and convolutional neural network are used. A database consisting of 600 exe files of which 300 malware files and 300 benign files have been considered here for building the malware and benign detection model. All the exe files are converted into images files. The contemporary CNN-based binary classification which takes the grayscale images as input. The convolutional neural networks-based classification model proves accuracy of 93% in discriminate from malware and benign files. The convolutional neural network-based malware detection model has higher performance when compared with deep neural network classification model trained with GIST features of image.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
B. Thuraisingham, L. Khan, M.M. Masud, K.W. Hamlen, Data Mining For Security Applications. IEEE/IFIP International Conference on Embedded And Ubiquitous Computing, 2010
T. Lee, J.J. Mody, Behavioral classification. Proceedings of the European Institute for Computer Antivirus Research Conference (EICAR’06), 2006
B. Anderson, C. Storlie, T. Lane, Improving malware classification bridging the static/dynamic gap. Proceedings of 5th ACM Workshop on Security and Artificial Intelligence (AISec), 2012
K. O’Shea, R. Nash, An introduction to convolutional neural networks, Department of Computer Science, Aberystwyth University, Ceredigion, SY23 3DB, 2015
M. Siddiqui, M.c. Wang, J. Lee, A survey of data mining techniques for malware detection using file features. In Proceedings of the 46th Annual Southeast Regional Conference on Xx. 2008. Acm
M.E. Boujnouni, M. Jedra, N. Zahid, New malware detection framework based on N-grams and support vector domain description, 11th International Conference On Information Assurance And Security (Ias), pp 123–128
C.I. Fan, H.W. Hsiao, C.H. Chou, Y.F. Tseng, Malware detection systems based on api log data mining, IEEE 39th Annual Computer Software And Applications Conference, 2015, pp. 255–260
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Krithika, V., Vijaya, M.S. (2021). Malware and Benign Detection Using Convolutional Neural Network. In: Bhateja, V., Satapathy, S.C., Travieso-González, C.M., Aradhya, V.N.M. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 1407. Springer, Singapore. https://doi.org/10.1007/978-981-16-0171-2_4
Download citation
DOI: https://doi.org/10.1007/978-981-16-0171-2_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0170-5
Online ISBN: 978-981-16-0171-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)