SimCoDe-NET: Similarity Detection in Binary Code using Deep Learning Network

Abstract

Binary code similarity detection is a fundamental task in the field of computer binary security. However, code similarity is crucial today because of the prevalence of issues like plagiarism, code cloning, and recycling in software due to the ongoing increase of software scale. To resolve these issues, a novel SIMilarity detection in binary COde using DEep learning NETwork (SimCoDe-NET) has been proposed. Initially, op-code features are extracted from the input data by using reverse engineering process and the opcode embedding is generated using N-skip gram method. The extracted features are fed into Bi-GRU neural network for classifying the similarity of the binary codes. The Bi-GRU neural network compares two data samples in feature space to identify whether they belong to similar data or non-similar data. The SimCoDe-NET framework is evaluated by using generated dataset to assess the efficiency of this method. The efficacy of the proposed SimCoDe-NET framework is assessed in terms of precision, accuracy, sensitivity, recall, similarity detection time and similarity detection rate. The accuracy of the proposed method is 99.10% which is relatively high compared to the existing method. The proposed SimCoDe-NET approach improves the accuracy by 84.9%, 88.58%, and 93.9% better than jTrans, UPPC, and HEBCS respectively.