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检索条件"主题词=Android Malware Detection"

共 213 条记录，以下是81-90 订阅

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Fine-grained android malware detection based on Deep Learning 6

Fine-grained Android Malware Detection based on Deep Learnin...

引用

6th IEEE Conference on Communications and Network Security (CNS)

作者： Li, Dongfang Wang, Zhaoguo Xue, Yibo Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China Tsinghua Univ Tsinghua Natl Lab Informat Sci & Tech Beijing Peoples R China

ISBN: (纸本)9781538645864

android smartphone users have been suffering from the security problems these years. There is a serious threat to the network security and privacy brought by the mobile malware. In this paper, we use the deep-learning-based method to detect android malware and implement an automatic detection engine to detect the families of malicious applications. The results of the evaluation show that the engine can detect 97% of the malware at 0.1% false positive rate (FPR) when detecting the fine-grained malware families.

关键词： android malware detection Deep Neural Network Fine-grained Classification Smartphones Security

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DroidRadar: android malware detection Based on Global Sensitive Graph Embedding 20

DroidRadar: Android Malware Detection Based on Global Sensit...

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20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Song, Qige Zhang, Yongzheng Yao, Junliang Chinese Acad Sci Univ Chinese Acad Sci Sch Cyber Secur Inst Informat Engn Beijing Peoples R China

ISBN: (纸本)9781665416580

android application markets face severe threats of malware attacks. Existing learning-based malware detection approaches rely on easily obfuscated features or unscalable sophisticated graph analysis techniques. In this paper, we propose DroidRadar, an accurate android malware detection system based on lightweight graph embedding. The key insight of our method is constructing an entire android application collection as a global graph schema and using sensitive APIs as bridge nodes to propagate inter-application information. We conduct statistical correlation analysis from different perspectives to model the application's usage pattern of sensitive APIs, then apply graph convolution network (GCN) to perform node embedding and malware detection. We evaluate DroidRadar on large scale datasets spanning nine years. Results show that DroidRadar has an average detection accuracy of 98.57% and a false-positive rate of 1.4% on different time periods, which outperforms the state-of-the-art approaches, and it has strong robustness when detecting obfuscated malware variants.

关键词： android malware detection software characterization graph neural network mobile security

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Proposed New Features to Improve android malware detection 8

Proposed New Features to Improve Android Malware Detection

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8th International Symposium on Telecommunications (IST)

作者： Vajdi, Mahdi Torkaman, Atefeh Bahrololum, Marjan Tadayon, M. H. Salajegheh, Afshin ITRC Tehran Iran Islamic Azad Univ ITRC South Tehran Branch Tehran Iran Islamic Azad Univ South Tehran Branch Tehran Iran

ISBN: (纸本)9781509034352

In recent years, android has become most popular and largest installed operating systems in smartphones. The openness capability of android applications let the users to easily download many applications from markets. Accordingly, a large number of malicious applications may be received carelessly. Developers publish their applications in a market place without regard to special considerations. On the other hand, low knowledge of the users in analyzing the risk of these applications, create further exploits on information and privacy. These malicious applications can perform unauthorized activities such as gaining access to user's sensitive information, camera, SMS, phone calls, etc. In this paper a system which help the users to identify malicious or benign installed apps is proposed. The mentioned system implements a hybrid model based on AdaBoost, J48 to classify installed applications. We evaluate our system with 11096 real data collected from OCS Lab which includes 2124 malware samples and the rest is benign. The proposed system by addition some technical features;the number of Services, Intent Filter and Broadcast Receivers can improve the average true positive rate from 98.9% to 99.2% (by using Gain ratio) and 96.8% to 98.1% (by using CFS).

关键词： android malware detection Hybrid Model Classification

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Research and implementation of android malware detection algorithm based on Graph Convolutional Networks 24

Research and implementation of Android malware detection alg...

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International Conference on Algorithms, Software Engineering, and Network Security (ASENS)

作者： Wang, Yue Kezierbieke, Hailati Chen, Qinglin Xinjiang Univ Coll Software Urumqi 830046 Xinjiang Peoples R China Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Xinjiang Peoples R China

ISBN: (纸本)9798400709784

android malware detection is one of the research hotspots in the field of malicious software. Traditional rule-based and feature-based methods face challenges in handling large-scale android malware detection. This paper proposes an android malware detection model based on graph convolutional network algorithms. Additionally, an ensemble learning method is utilized, selecting three different graph convolutional layer algorithms as base classifiers. The base classifiers are trained and tested to obtain accuracy, precision, recall, and F1 score as evaluation metrics, and the weights of the classifiers are calculated using these metrics. This approach avoids the potential distortion in classification results from a single graph convolutional network algorithm, thereby enhancing its classification performance. This paper also introduces a ReliefF feature selection algorithm based on mutual information optimization. By utilizing mutual information from information theory to calculate the correlation between features and improving the computation of feature weights, it addresses the issue of handling redundant features in the traditional ReliefF algorithm to a certain extent.

关键词： Graph convolutional network android malware detection Function call graph Node feature

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An android malware detection System Based on Behavior Comparison Analysis 17th

An Android Malware Detection System Based on Behavior Compar...

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17th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Tao, Jing Zhang, Yan Cao, Pengfei Wang, Zheng Zhao, Qiqi Xi An Jiao Tong Univ Minist Educ Key Lab Intelligent Networks & Network Secur Xian 710049 Peoples R China

ISBN: (纸本)9783319654829;9783319654812

At present, android malwares become more and more subtle and intelligent, after their invasion, they often detect whether the running environment is a real environment, to decide whether to perform their malicious behavior. Therefore, malware tend to execute different behavior when running in different environments. Benign applications will perform the same functions in different environments, their behaviors have a strong consistency. Based on this basic idea, we design an android malware detection method based on behavior comparison analysis. First, design and development a number of specific different running environments, and then execute application in these environments. With the same event input, record and compare the behaviors of this application, calculate the difference, determine whether it is malicious. Under the guidance of this thought, we design and development the android malware detection system EmuProtect. We evaluate EmuProtect system from the aspects of accuracy and validity, the results show that this system can effectively detect android malicious applications.

关键词： android malware detection android application behavior model Behavior analysis

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TFDroid: android malware detection by Topics and Sensitive Data Flows Using Machine Learning Techniques 2

TFDroid: Android Malware Detection by Topics and Sensitive D...

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2nd IEEE International Conference on Information and Computer Technologies (ICICT)

作者： Lou, Songhao Cheng, Shaoyin Huang, Jingjing Jiang, Fan Univ Sci & Technol China Sch Comp Sci & Technol Hefei Anhui Peoples R China Univ Sci & Technol China Informat Technol Secur Evaluat Ctr Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9781728133232

With explosive growth of android malware and due to the severity of its damages to smart phone users, efficient android malware detection methods are urgently needed. As is known to us, different categories of applications divided by their functions use sensitive data in distinct ways. Besides, in each category, malicious applications treat sensitive data differently from benign applications. We thus propose TFDroid, a novel machine learning-based approach to detect malware using the related topics and data flows of android applications. We test TFDroid on thousands of benign and malicious applications. The results show that TFDroid can correctly identify 93.7% of all malware.

关键词： android malware detection topic modeling data flows machine learning

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Two Phase Static Analysis Technique for android malware detection 15

Two Phase Static Analysis Technique for Android Malware Dete...

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3rd International Symposium on Women in Computing and Informatics (WCI)

作者： Kate, Priyadarshani M. Dhavale, Sunita V. Def Inst Adv Technol Dept Comp Engn Pune 411025 Maharashtra India

ISBN: (纸本)9781450333610

The growing popularity of android based smart phones has greatly fuelled the spread of android malware. Further, these malwares are evolving rapidly to escape from traditional signature -based detection methods. Hence, there is a serious need to develop effective android malware detection techniques. In this paper, we propose two phase static android malware analysis scheme using bloom filters. The Phase I involves two different bloom filters that classify a given sample into malware or benign class based on permission feature set only. The evaded malicious samples from Phase I are further analyzed by Phase II consisting Na ve Bayes Classifier using permission and code based mixed feature set. Inclusion of Phase I classification makes the technique computationally less intensive;while addition of the Phase II classification improves the overall accuracy of the proposed model. Experimental results indicate both detection accuracy and computational efficiency of the proposed technique.

关键词： Bloom Filter Machine learning android malware detection

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ByteDroid: android malware detection Using Deep Learning on Bytecode Sequences 1

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13th Chinese Conference on Trusted Computing and Information Security (CTCIS)

作者： Zou, Kewen Luo, Xi Liu, Pengfei Wang, Weiping Wang, Haodong Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Hunan Police Acad Dept Informat Technol Changsha 410083 Peoples R China Cleveland State Univ Dept Elect Engn & Comp Sci Cleveland OH 44115 USA

ISBN: (数字)9789811534188

ISBN: (纸本)9789811534188;9789811534171

The explosive growth of the android malware poses a great threat to users' privacy and sensitive personal information. It is urgent to develop an effective and efficient android malware detection system. Existing studies usually require the manual feature engineering for the feature extraction. In fact, the detection performance is heavily relied on the quality of the feature extraction. Additionally, the feature extraction becomes extremely difficult in the malware detection due to the fact that malware developers often deploy the obfuscation techniques. To address this issue, we focus on the android malware detection using the deep neural networks without the human factors. In this paper, we propose ByteDroid, an android malware detection scheme that processes the raw Dalvik bytecode using the deep learning. ByteDroid resizes the raw bytecode and constructs a learnable vector representation as the input to the neural network. Then, ByteDroid adopts a Convolutional Neural Networks (CNNs) to automatically extract the malware features and perform the classification. Our experiment results demonstrate that ByteDroid not only can effectively detect android malware, but also has a great generalization performance given untrained malware. Moreover, ByteDroid maintains resilience to obfuscation techniques.

关键词： android malware detection Dalvik bytecode Convolutional Neural Networks

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Impact of datasets on machine learning based methods in android malware detection: an empirical study 21

Impact of datasets on machine learning based methods in Andr...

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21st IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Ge, Xiuting Huang, Yifan Hui, Zhanwei Wang, Xiaojuan Cao, Xu Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Mil Acad Sci Beijing Peoples R China Southwest Univ Sci & Technol Dept Comp Sci & Technol Mianyang Sichuan Peoples R China

ISBN: (纸本)9781665458139

For android malware detection, machine learning-based (ML-based) methods show promising performance. However, limited studies are performed to investigate the impact of factors related to datasets on ML-based methods, while the performance of ML-based methods dramatically relies on datasets. To partially bridge the gap, we conduct an empirical study to investigate the impact of factors related to datasets on ML-based android malware detection methods. By investigating dataset differences between real-world scenarios and experimental settings, we summarize three dataset factors (i.e., class imbalance, quality, and timelines) and assess the impact of these factors on ML-based android malware detection methods. We conduct experiments on more than 11K benign and 17K malicious applications. The results show that these three dataset factors yield significant biases in the existing ML-based android malware detection methods. Based on these results, we learn some lessons about assessing ML-based android malware detection methods when taking dataset factors into account.

关键词： Class imbalance Dataset quality Concept drift Machine learning android malware detection

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Mining API Calls and Permissions for android malware detection 13

Mining API Calls and Permissions for Android Malware Detecti...

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13th International Conference on Cryptology and Network Security (CANS)

作者： Sharma, Akanksha Dash, Subrat Kumar LNM Inst Informat Technol Dept Comp Sci & Engn Jaipur Rajasthan India

ISBN: (纸本)9783319122809;9783319122793

The popularity of android platform is increasing very sharply due to the large market share of android and openness in nature. The increased popularity is making android an enticing target for malwares. A worrying trend that is alarming is the increasing sophistication of android malware to evade detection by traditional signature based scanners. Several approaches have been proposed in literature for android malware detection. However, most of them are less effective in terms of true positive rate and involves computational overheads. In this paper, we propose an effective approach to attenuate the problem of android malware detection using static code analysis based models. The proposed models, in this paper, are built to capture features relevant to malware behaviour based on API calls as well as permissions present in various android applications. Thereafter, models are evaluated using Naive Bayesian as well as K-Nearest Neighbour classifiers. Proposed models are able to detect real malwares in the wild and achieve an accuracy of 95.1% and true positive rate with highest value one.

关键词： android malware detection API calls Permissions Feature Selection Classification

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