Detection of unknown malicious script code using a conceptual graph and SVM
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Mining source code repositories at massive scale using language modeling
Proceedings of the 10th Working Conference on Mining Software Repositories
Detection of cross site scripting attack in wireless networks using n-Gram and SVM
Mobile Information Systems - Advances in Network-Based Information Systems
| Hi-index | 0.00 |
--As the use of the internet increases, the distribution of web based malicious code has also vastly increased. By inputting malicious code that can attack vulnerabilities, it enables one to perform various illegal acts, such as SQL Injection and Cross Site Scripting (XSS). Furthermore, an extensive amount of computer, network and human resources are consumed to prevent it. As a result much research is being done to prevent and detecting malicious code. Currently, research is being done on readable sentences which do not use proper grammar. This type of malicious code cannot be classified by previous vocabulary analysis or document classification methods. This paper proposes an approach that results in an effective n-gram feature extraction from malicious code for classifying executable as malicious or benign with the use of Support Vector Machines (SVM) as the machine learning classifier.