scholarly journals Control Flow Obfuscation Using Neural Network to Fight Concolic Testing

2015 ◽  
pp. 287-304 ◽  
Author(s):  
Haoyu Ma ◽  
Xinjie Ma ◽  
Weijie Liu ◽  
Zhipeng Huang ◽  
Debin Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Flow ◽  
Concolic Testing ◽  
Control Flow Obfuscation

scholarly journals Integrated Software Fingerprinting via Neural-Network-Based Control Flow Obfuscation

2016 ◽  
Vol 11 (10) ◽  
pp. 2322-2337 ◽  
Author(s):  
Haoyu Ma ◽  
Ruiqi Li ◽  
Xiaoxu Yu ◽  
Chunfu Jia ◽  
Debin Gao
Keyword(s):  
Neural Network ◽  
Control Flow ◽  
Control Flow Obfuscation ◽  
Integrated Software

scholarly journals Order Matters: Semantic-Aware Neural Networks for Binary Code Similarity Detection

2020 ◽  
Vol 34 (01) ◽  
pp. 1145-1152 ◽  
Author(s):  
Zeping Yu ◽  
Rui Cao ◽  
Qiyi Tang ◽  
Sen Nie ◽  
Junzhou Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Security ◽  
Semantic Information ◽  
Binary Code ◽  
Graph Matching ◽  
Control Flow ◽  
Binary Function ◽  
Similarity Detection ◽  
Block Level

Binary code similarity detection, whose goal is to detect similar binary functions without having access to the source code, is an essential task in computer security. Traditional methods usually use graph matching algorithms, which are slow and inaccurate. Recently, neural network-based approaches have made great achievements. A binary function is first represented as an control-flow graph (CFG) with manually selected block features, and then graph neural network (GNN) is adopted to compute the graph embedding. While these methods are effective and efficient, they could not capture enough semantic information of the binary code. In this paper we propose semantic-aware neural networks to extract the semantic information of the binary code. Specially, we use BERT to pre-train the binary code on one token-level task, one block-level task, and two graph-level tasks. Moreover, we find that the order of the CFG's nodes is important for graph similarity detection, so we adopt convolutional neural network (CNN) on adjacency matrices to extract the order information. We conduct experiments on two tasks with four datasets. The results demonstrate that our method outperforms the state-of-art models.

Control flow obfuscation based protection method for Android applications

2017 ◽  
Vol 14 (11) ◽  
pp. 247-259 ◽  
Author(s):  
Yong Peng ◽  
Guanyu Su ◽  
Bin Tian ◽  
Maohua Sun ◽  
Qi Li
Keyword(s):  
Control Flow ◽  
Protection Method ◽  
Android Applications ◽  
Control Flow Obfuscation
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