Detection of Malware of Code Clone using String Pattern Back Propagation Neural Network Algorithm

Abstract

Background/Objectives: Malware is progressing at a faster pace so the identification of malware is a vital area in modernized world where information technology is rapidly emerging. This paper emphasizes on enhancement of performance parameters for malware detection of source code clones using proposed clone detection algorithm. Methods/ Statistical Analysis: The approaches defined by researchers didn’t consider data types, variables while clone detection. To fulfill the goal of proposed work, malware detection of clone clone and achieve better results the approach adopted is implementation of a clone detection algorithm ‘String Pattern Back Propagation Neural Network’ to determine the code clones and matching them with malware signatures in the repository for computation of performance parameters. Findings: The identification of malware is proceeded by utilizing java projects having different window size (20,40). The source code files are put into modularization phase to extract functions from different classes. Code clones are determined by applying the implemented algorithm for the evaluation of malware signatures. It was observed that employed approach results into better performance with high accuracy of 96.97% and hence, the approach developed proved to be deterministic and efficient. The paper provides an overview of state of the art and focuses on enhanced performance in terms of precision, recall and F-measure in case of Java language where the data types, variables, comments in the application are also given priority to detect code clones as compared to existing research malware binaries for achieving better performance. Applications/ Improvements: To handle the tremendous range of malicious code, the approach can be applied in varied multiple languages to detect the number of clones in an application or a system and achieve greater outcomes.