scholarly journals Android Malware Detection Method Based on Frequent Pattern and Weighted Naive Bayes

2019 ◽  
pp. 36-51
Author(s):  
Jingwei Li ◽  
Bozhi Wu ◽  
Weiping Wen
Keyword(s):  
Naive Bayes ◽  
Detection Method ◽  
Malware Detection ◽  
Naïve Bayes ◽  
Frequent Pattern ◽  
Android Malware ◽  
Android Malware Detection

Android malware detection method based on naive Bayes and permission correlation algorithm

2017 ◽  
Vol 21 (1) ◽  
pp. 955-966 ◽  
Author(s):  
Fengjun Shang ◽  
Yalin Li ◽  
Xiaolin Deng ◽  
Dexiang He
Keyword(s):  
Naive Bayes ◽  
Detection Method ◽  
Malware Detection ◽  
Naïve Bayes ◽  
Android Malware ◽  
Android Malware Detection ◽  
Correlation Algorithm

scholarly journals SUIP: An Android malware detection method based on data flow features

2021 ◽  
Vol 1812 (1) ◽  
pp. 012010
Author(s):  
X R Chen ◽  
S S Shi ◽  
C L Xie ◽  
Z Yang ◽  
Y J Guo ◽  
...  
Keyword(s):  
Data Flow ◽  
Detection Method ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection ◽  
Flow Features

scholarly journals Permission Sensitivity-Based Malicious Application Detection for Android

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yubo Song ◽  
Yijin Geng ◽  
Junbo Wang ◽  
Shang Gao ◽  
Wei Shi
Keyword(s):  
Detection Method ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Android Malware ◽  
Android Malware Detection ◽  
Private Data ◽  
Weight Allocation

Since a growing number of malicious applications attempt to steal users’ private data by illegally invoking permissions, application stores have carried out many malware detection methods based on application permissions. However, most of them ignore specific permission combinations and application categories that affect the detection accuracy. The features they extracted are neither representative enough to distinguish benign and malicious applications. For these problems, an Android malware detection method based on permission sensitivity is proposed. First, for each kind of application categories, the permission features and permission combination features are extracted. The sensitive permission feature set corresponding to each category label is then obtained by the feature selection method based on permission sensitivity. In the following step, the permission call situation of the application to be detected is compared with the sensitive permission feature set, and the weight allocation method is used to quantify this information into numerical features. In the proposed method of malicious application detection, three machine-learning algorithms are selected to construct the classifier model and optimize the parameters. Compared with traditional methods, the proposed method consumed 60.94% less time while still achieving high accuracy of up to 92.17%.

scholarly journals Android Malware Detection Based on API Pairing

2020 ◽  
Vol 38 (5) ◽  
pp. 965-970
Author(s):  
Jun Guan ◽  
Huiying Liu ◽  
Baolei Mao ◽  
Xu Jiang
Keyword(s):  
Detection Method ◽  
Undirected Graph ◽  
Malware Detection ◽  
Experimental Results ◽  
Application Program Interface ◽  
Coarse Grained ◽  
Practical Significance ◽  
Android Malware ◽  
Android Malware Detection ◽  
Program Interface

Aiming at the problem that the permission-based detection is too coarse-grained, a malware detection method based on sensitive application program interface(API) pairing is proposed. The method decompiles the application to extract the sensitive APIs corresponding to the dangerous permissions, and uses the pairing of the sensitive APIs to construct the undirected graph of malicious applications and undirected graph of benign applications. According to the importance of sensitive APIs in malware and benign applications, different weights on the same edge in the different graphs are assigned to detect Android malicious applications. Experimental results show that the proposed method can effectively detect Android malicious applications and has practical significance.

scholarly journals LSTM-Based Hierarchical Denoising Network for Android Malware Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Jinpei Yan ◽  
Yong Qi ◽  
Qifan Rao
Keyword(s):  
Detection Method ◽  
Expert Knowledge ◽  
Detection Efficiency ◽  
Malware Detection ◽  
Mobile Security ◽  
Detection Methods ◽  
Android Malware ◽  
Android Malware Detection ◽  
Malicious Behavior ◽  
Gradient Scaling

Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks) to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM) for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than N-gram-based malware detection which is similar to our method.

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