Defect Prediction in Android Binary Executables Using Deep Neural Network

Software defect prediction locates defective code to help developers improve the security of software. However, existing studies on software defect prediction are mostly limited to the source code. Defect prediction for Android binary executables (called apks) has never been explored in previous studies. In this paper, we propose an explorative study of defect prediction in Android apks. We first propose smali2vec, a new approach to generate features that capture the characteristics of smali (decompiled files of apks) files in apks. Smali2vec extracts both token and semantic features of the defective files in apks and such comprehensive features are needed for building accurate prediction models. Then we leverage deep neural network (DNN), which is one of the most common architecture of deep learning networks, to train and build the defect prediction model in order to achieve accuracy. We apply our defect prediction model to more than 90,000 smali files from 50 Android apks and the results show that our model could achieve an AUC (the area under the receiver operating characteristic curve) of 85.98% and it is capable of predicting defects in apks. Furthermore, the DNN is proved to have a better performance than the traditional shallow machine learning algorithms (e.g., support vector machine and naive bayes) used in previous studies. The model has been used in our practical work and helped locate many defective files in apks.

[1]  Adam Doupé,et al.  Deep Android Malware Detection , 2017, CODASPY.

[2]  Junfeng Wang,et al.  A static Android malicious code detection method based on multi-source fusion , 2015, Secur. Commun. Networks.

[3]  Shane McIntosh,et al.  Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.

[4]  Wouter Joosen,et al.  Predicting Vulnerable Software Components via Text Mining , 2014, IEEE Transactions on Software Engineering.

[5]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[6]  Matthew Smith,et al.  VCCFinder: Finding Potential Vulnerabilities in Open-Source Projects to Assist Code Audits , 2015, CCS.

[7]  Honggang Zhang,et al.  Variational Bayesian Matrix Factorization for Bounded Support Data , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[9]  Radu State,et al.  Using opcode-sequences to detect malicious Android applications , 2014, 2014 IEEE International Conference on Communications (ICC).

[10]  Viet Hung Nguyen,et al.  Predicting vulnerable software components with dependency graphs , 2010, MetriSec '10.

[11]  Markus Flierl,et al.  Bayesian estimation of Dirichlet mixture model with variational inference , 2014, Pattern Recognit..

[12]  Junfeng Wang,et al.  Malware detection method based on the control-flow construct feature of software , 2014, IET Inf. Secur..

[13]  Bart Baesens,et al.  Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings , 2008, IEEE Transactions on Software Engineering.

[14]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[15]  Jun Guo,et al.  Feature selection for neutral vector in EEG signal classification , 2016, Neurocomputing.

[16]  Vandana Bhattacherjee,et al.  Software Fault Prediction Using Quad Tree-Based K-Means Clustering Algorithm , 2012, IEEE Transactions on Knowledge and Data Engineering.

[17]  Tao Wang,et al.  TBCNN: A Tree-Based Convolutional Neural Network for Programming Language Processing , 2014, ArXiv.

[18]  Arti Arya,et al.  A Study on Software Metrics based Software Defect Prediction using Data Mining and Machine Learning Techniques , 2015 .

[19]  Ruchika Malhotra,et al.  An empirical framework for defect prediction using machine learning techniques with Android software , 2016, Appl. Soft Comput..

[20]  Zhenlong Yuan,et al.  Droid-Sec: deep learning in android malware detection , 2015, SIGCOMM 2015.

[21]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[22]  SchmidhuberJürgen.  Deep learning in neural networks , 2015 .

[23]  Jun Guo,et al.  The Role of Data Analysis in the Development of Intelligent Energy Networks , 2017, IEEE Network.

[24]  Song Wang,et al.  Automatically Learning Semantic Features for Defect Prediction , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).

[25]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[26]  Jun Du,et al.  An Experimental Study on Speech Enhancement Based on Deep Neural Networks , 2014, IEEE Signal Processing Letters.

[27]  Nathan S. Netanyahu,et al.  DeepSign: Deep learning for automatic malware signature generation and classification , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[28]  Tracy Hall,et al.  A Systematic Literature Review on Fault Prediction Performance in Software Engineering , 2012, IEEE Transactions on Software Engineering.

[29]  Junfeng Wang,et al.  An unknown malware detection scheme based on the features of graph , 2013, Secur. Commun. Networks.

[30]  Ying Zou,et al.  Cross-Project Defect Prediction Using a Connectivity-Based Unsupervised Classifier , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).