Username   Password       Forgot your password?  Forgot your username? 


Method for Detecting Javascript Code Obfuscation based on Convolutional Neural Network

Volume 14, Number 12, December 2018, pp. 3167-3173
DOI: 10.23940/ijpe.18.12.p26.31673173

Wei Jianga,b, Huiqiang Wanga, and Keke Wua

aCollege of Computer Science and Technology, Harbin Engineering University, Harbin, 150080, China
bCollege of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150080, China

(Submitted on July 14, 2018; Revised on August 13, 2018; Accepted on September 11, 2018)


Malicious webpage attacks occur frequently, and most of the JavaScript attack code is implemented through obfuscation. In order to further confront malicious webpage attacks, detecting JavaScript obfuscation scripts has become one of the most urgent issues to be addressed. This paper proposes a method for detecting JavaScript code obfuscation based on Convolutional Neural Networks (CNNs). Firstly, the character matrix feature method of Bigram is used to extract features of JavaScript code. Secondly, a CNN model is applied to the JavaScript code obfuscation detection, which overcomes the high requirement of the machine code learning and the low accuracy of the obfuscation feature extraction of JavaScript code. Finally, the simulation results show that this method can not only reduce the requirements for the features, but also effectively improve the accuracy of the detection of JavaScript code obfuscation.


References: 16

                    1. S. Aebersold, K. Kryszczuk, S. Paganoni, B. Tellenbach, and T. Trowbridge, “Detecting Obfuscated JavaScripts Using Machine Learning,” in Proceedings of the Eleventh International Conference on Internet Monitoring and Protection, pp. 11-16, 2016
                    2. P. P. F. Chan, L. C. K. Hui, and S. M. Yiu, “Heap Graph based Software Theft Detection,” IEEE Transactions on Information Forensics and Security, Vol. 8, No. 1, pp. 101-110, 2013
                    3. J. Su, K. Yoshioka, J. Shikata, and T. Matsumoto, “An Efficient Method for Detecting Obfuscated Suspicious JavaScript based on Text Pattern Analysis,” in Proceedings of the 2016 ACM International on Workshop on Traffic Measurements for Cybersecurity, pp. 3-11, 2016
                    4. H. B. Chen, “Research and Implementation on Machine Learning-based Detection of Malicious Script Codes,” Zhejiang University of Technology, 2011
                    5. M. Jodavi, M. Abadi, E. Parhizkar, “JSObfusDetector: A Binary PSO-based One-Class Classifier Ensemble to Detect Obfuscated JavaScript Code,” in Proceedings of 2015 International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 322-327, 2015
                    6. H. L. Ma, W. Wang, and Z. Han, “Detecting and De-Obfuscating Obfuscated Malicous JavaScript Code,” Chinese Journal of Computers, Vol. 40, No. 7, pp. 1699-1713, 2017
                    7. J. Quackenbush, “Microarray Data Normalization and Transformation,” Nature Genetics, Vol. 32, pp. 496-501, 2002
                    8. J. B. Yang, W. Q. Zhang, and J. Liu, “Investigation of Normalization Methods in Speaker Adaptation of Deep Neural Network Using Ivector,” Journal of University of Chinese Academy of Sciences, Vol. 34, No. 5, pp. 633-639, 2017
                    9. X. Xiao and L. Zhou, “Speech Recognition Adaptive Clustering Feature Extraction Algorithms based on the k-Means Algorithm and the Normalized Intra-Class Variance,” Journal of Tsinghua University (Science and Technology), Vol. 8, pp. 857-861, 2017
                    10. M. Mohri, A. Rostamizadeh, and A. Talwalkar, “Foundations of Machine Learning,” Foundations of Machine Learning, MIT Press, pp. 287-306, 2012
                    11. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Communications of the ACM, Vol. 60, No. 2, pp. 1-13, 2012
                    12. Alexa Top Global Sites (, accessed March 2018)
                    13. Free JavaScript Obfuscator (, accessed March 2018)
                    14. JavaScript Obfuscator/Encoder (, accessed March 2018)
                    15. T. Dietterich, “Overfitting and Undercomputing in Machine Learning,” ACM Computing Surveys, Vol. 27, No. 3, pp. 326-327, 1995
                    16. J. Tang, S. Alelyani, and H. Liu, “Feature Selection for Classification: A Review,” Documentación Administrativa, pp. 313-334, 2014


                                      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

                                      Download this file (IJPE-2018-12-26.pdf)IJPE-2018-12-26.pdf[Method for Detecting Javascript Code Obfuscation based on Convolutional Neural Network]320 Kb
                                      This site uses encryption for transmitting your passwords.