Lightweight method for detecting JavaScript-based malicious Web pages

作者：Ma Hongliang; Wang Wei; Han Zhen

来源：Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2014, (11).

DOI：10.13245/j.hust.141107

• anomaly detection
• malicious Web pages
• principal component analysis
• Web security
• machine learning

In order to detect malicious Web pages efficiently ,a lightweight analysis method was pro-posed based on basic JavaScript code words .First ,crawler got all source codes from Web pages ,and then extracted JavaScript codes from source codes .Second ,self-defined basic code words replaced all JavaScript codes .Last ,three machine learning algorithms ,namely ,K-nearest neighbor (K-NN ) , principal component analysis (PCA ) as well as one-class support vector machine (SVM ) were em-ployed to detect malicious Web pages based on anomaly detection .The extensive experimental results show that our method can detect Web pages efficiently .In particular ,PCA achieves a detection rate as 90% with false positive rate of 1% ,detecting 250 s-1 .

• 单位
  石河子大学; 北京交通大学