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International Conference on Computational Intelligence and Intelligent Systems (CIIS)

作者： Kakavand, Mohsen Dabbagh, Mohammad Dehghantanha, Ali Sunway Univ Sch Sci & Technol Kuala Lumpur Malaysia Univ Guelph Sch Comp Sci Guelph ON Canada

ISBN: (纸本)9781450365956

As the popularity of android smart devises increases, the battle of alleviating android malware has been considered as a crucial activity with the advent of new attacks including progressively complicated evasion techniques, consequently entailing more cutting-edge detection techniques. Hence, in this paper, two Machine Learning (ML) algorithms, called Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), are applied and evaluated to perform classification of the feature set into either benign or malicious applications (apps) through supervised learning process. This work involves in static analysis of apps, which checks for the presence and frequency of keywords in the android apps' manifest file and derives the static feature sets from a 400-app dataset to produce better malware detection results. The classification performance of the ML algorithms is measured in terms of accuracy and true positive rate and interpreted to determine which algorithm is more applicable for the android malware detection. The experimental results for a dataset of real malware and benign apps indicate the average accuracy rate of 79.08% and 80.50% with average true positive rate of over 67.00% and 80.00% using SVM and KNN, respectively.

关键词： Machine Learning Static Analysis android malware detection SVM KNN

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Systematic Review on Various Techniques of android malware detection 1

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3rd International Conference on Computing Science, Communication and Security (COMS)

作者： Dave, Dharmesh D. Rathod, Digvijaysinh Natl Forens Sci Univ Gandhinagar India

ISBN: (数字)9783031105517

ISBN: (纸本)9783031105517;9783031105500

Smartphone has become the 4th basic necessity of human being after Food, Cloths and Home. It has become an integral part of the life that most of the business and office work can be operated by mobile phone and the demand for online classes demand for all class of students have become a compulsion without any alternate due to the COVID-19 pandemic. android is considered as the most prevailing and used operating system for the mobile phone on this planet and for the same reason it is the most targeted mobile operating system by the hackers. android malware has been increasing every quarter and every year. An android malware is installed and executed on the smartphones quietly without any indication and user's acceptance, that possess threats to the consumer's personal and/or classified information stored. To address these threats, varieties of techniques have been proposed by the researchers like Static, Dynamic and Hybrid. In this paper a systematic review has been carried out on the relevant studies from 2017 to 2020. Assessment of the malware detection capabilities of various techniques used by different researchers has been carried out with comparison of the performance of different machine learning models for the detection of android malwares by assessing the results of empirical evidences such as datasets, features, tools, etc. However the android malware detection still faces several challenges and the possible solution with some novel approach or technique to improve the detection capabilities is discussed in the discussion and conclusion.

关键词： android malware detection Hybrid analysis Static analysis Mobile malware

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android malware detection Using Support Vector Regression for Dynamic Feature Analysis

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INFORMATION 2024年第10期15卷 658-658页

作者： Aldhafferi, Nahier Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Informat Syst Dammam 34212 Saudi Arabia

Mobile devices face significant security challenges due to the increasing proliferation of android malware. This study introduces an innovative approach to android malware detection, combining Support Vector Regression (SVR) and dynamic feature analysis to address escalating mobile security challenges. Our research aimed to develop a more accurate and reliable malware detection system capable of identifying both known and novel malware variants. We implemented a comprehensive methodology encompassing dynamic feature extraction from android applications, feature preprocessing and normalization, and the application of SVR with a Radial Basis Function (RBF) kernel for malware classification. Our results demonstrate the SVR-based model's superior performance, achieving 95.74% accuracy, 94.76% precision, 98.06% recall, and a 96.38% F1-score, outperforming benchmark algorithms including SVM, Random Forest, and CNN. The model exhibited excellent discriminative ability with an Area Under the Curve (AUC) of 0.98 in ROC analysis. The proposed model's capacity to capture complex, non-linear relationships in the feature space significantly enhanced its effectiveness in distinguishing between benign and malicious applications. This research provides a robust foundation for advancing android malware detection systems, offering valuable insights for researchers and security practitioners in addressing evolving malware challenges.

关键词： android malware detection support vector regression dynamic feature analysis machine learning mobile security

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Static Analysis for android malware detection with Document Vectors 21

Static Analysis for Android Malware detection with Document ...

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21st IEEE International Conference on Data Mining (IEEE ICDM)

作者： Raghav, Utkarsh Martinez-Marroquin, Elisa Ma, Wanli Univ Canberra Sch IT & Syst Canberra ACT Australia

ISBN: (纸本)9781665424271

With the increase of smart mobile devices in use, the number of malware targeting the mobile platforms has been increasing. As the major market player in the industry, android OS has been the favourite target of perpetrators targeting mobile platforms. The current machine learning and deep learning approaches for android malware detection utilize various feature creation methods. The majority of these feature creation methods use frequency-based vectors created from different files present in the android application package (APK). These frequency-based feature creation methods fail to preserve the semantic information that is present in those files. In this paper we propose a method that utilises the static analysis and natural language processing (NLP) technique of document embeddings to generate feature vectors that can represent the information contained in android manifests and dalvik executables files present inside an APK. These embeddings are then used to train binary classifiers which can effectively differentiate between a benign or malicious android application. Our proposed method in the experiments has outperformed the other related works on the test datasets.

关键词： android malware detection document embeddings android malwares cybersecurity

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Sensitivity Analysis of Static Features for android malware detection 22

Sensitivity Analysis of Static Features for Android Malware ...

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22nd Iranian Conference on Electrical Engineering (ICEE)

作者： Moghaddam, Samaneh Hosseini Abbaspour, Maghsood Shahid Beheshti Univ Fac Elect & Comp Engn Dept Comp Engn Network Secur LabGC Tehran Iran

ISBN: (纸本)9781479944095

The recent explosion of the number of mobile malware in the wild, significantly increases the importance of developing techniques to detect them. There are many published research in this area which employed traditional desktop malware detection approaches like dynamic and static analysis techniques to detect mobile malwares, but none of them applied a thorough study on the sensitivity analysis of the features used. In this paper we divide static features of classification-based android malware detection techniques proposed in different papers into some related categories and study the influence of using each category of features on the efficiency of classification-based android malware detections technique using all the static features.

关键词： component mobile malware detection static analysis static feature sensitivity analysis android malware detection

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XGBoost-Based android malware detection 13

XGBoost-Based Android Malware Detection

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13th International Conference on Computational Intelligence and Security (CIS)

作者： Wang, Jiong Li, Boquan Zeng, Yuwei Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100049 Peoples R China

ISBN: (纸本)9781538648223

malware remains the most significant security threat to smartphones in spite of the constantly upgrading of the system. In this paper, we introduce an android malware detection method based on XGBoost model. We subsequently discuss the effect of feature selection on the classification. In the end, we verify the high efficacy and good accuracy of this model through the experiment, which achieves similar results to the SVM while spending less time.

关键词： android malware detection static analysis machine learning XGBoost random forest algorithm

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URefFlow: A Unified android malware detection Model Based on Reflective Calls 37

URefFlow: A Unified Android Malware Detection Model Based on...

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37th IEEE International Performance Computing and Communications Conference (IPCCC)

作者： Liu, Chao Li, Jianan Yu, Min Li, Gang Luo, Bo Chen, Kai Jiang, Jianguo Huang, Weiqing Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Deakin Univ Sch Informat Technol Geelong Vic Australia Univ Kansas Dept Elect Engn & Comp Sci Lawrence KS 66045 USA

ISBN: (纸本)9781538668085

In android malware detection, sensitive dataflows provide more accurate information on the application's behavior than regular features such as signatures and permissions. Currently, android static taint analysis is widely adopted to identify sensitive data-flows because of its high code coverage and low false negative rate. However, existing static taint analysis tools cannot effectively analyze applications that adopt android reflection mechanism. Reflection mechanism can block the control-flows and data-flows of the application. When constructing a call graph, the call information will point directly to the system's reflection processing method, rather than the actual method invoked by the application. This significantly affects the accurate representation of the application's behavior. To address this issue, this paper proposes a unified android malware detection model based on reflective calls named URefFlow, in which the reflective call statement is replaced by the non-reflective call statement to make the reflective calls explicit by combining the parameters of the reflective calls into standard function calls. After extracting the complete sensitive data-flows with reflective calls from an application, we analyze the characteristics of these data-flows to determine whether the application is malicious. Evaluation results on thousands of applications show that URefFlow can achieve an impressive detection accuracy of 95.6% with a false positive rate of 0.8%. In addition, the proposed approach complements well with existing static stain analysis techniques.

关键词： Data-flow Reflection Standardization android malware detection

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android malware detection Using Local Binary Pattern and Principal Component Analysis

Android Malware Detection Using Local Binary Pattern and Pri...

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The Third International Conference of Pioneering Computer Scientists, Engineers and Educators,ICPCSEE 2017(originally ICYCSEE)

作者： Qixin Wu Zheng Qin Jinxin Zhang Hui Yin Guangyi Yang Kuangsheng Hu College of Computer Science and Electronic Engineering Hunan University Hunan Key Laboratory of Big Data Research and Application Hunan Institute of Metrology and Test

Nowadays, analysis methods based on big data have been widely used in malicious software detection. Since android has become the dominator of smartphone operating system market, the number of android malicious applications are increasing rapidly as well, which attracts attention of malware attackers and researchers alike. Due to the endless evolution of the malware, it is critical to apply the analysis methods based on machine learning to detect malwares and stop them from leakaging our privacy information. In this paper, we propose a novel android malware detection method based on binary texture feature recognition by Local Binary Pattern and Principal Component Analysis, which can visualize malware and detect malware accurately. Also,our method analyzes malware binary directly without any decompiler,sandbox or virtual machines, which avoid time and resource consumption caused by decompiler or monitor in this process. Experimentation on 5127 benigns and 5560 malwares shows that we obtain a detection accuracy of 90%.

关键词： android malware detection Binary texture feature Local binary pattern Principal component analysis

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Dandroid: A Multi-View Discriminative Adversarial Network for Obfuscated android malware detection 20

DANdroid: A Multi-View Discriminative Adversarial Network fo...

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10th ACM Conference on Data and Application Security and Privacy (CODASPY)

作者： Millar, Stuart McLaughlin, Niall del Rincon, Jesus Martinez Miller, Paul Zhao, Ziming Queens Univ Belfast CSIT Belfast Antrim North Ireland Rochester Inst Technol CactiLab Rochester NY USA

ISBN: (纸本)9781450371070

We present Dandroid, a novel android malware detection model using a deep learning Discriminative Adversarial Network (DAN) that classifies both obfuscated and unobfuscated apps as either malicious or benign. Our method, which we empirically demonstrate is robust against a selection of four prevalent and real-world obfuscation techniques, makes three contributions. Firstly, an innovative application of discriminative adversarial learning results in malware feature representations with a strong degree of resilience to the four obfuscation techniques. Secondly, the use of three feature sets;raw opcodes, permissions and API calls, that are combined in a multi-view deep learning architecture to increase this obfuscation resilience. Thirdly, we demonstrate the potential of our model to generalize over rare and future obfuscation methods not seen in training. With an overall dataset of 68,880 obfuscated and unobfuscated malicious and benign samples, our multi-view DAN model achieves an average F-score of 0.973 that compares favourably with the state-of-the-art, despite being exposed to the selected obfuscation methods applied both individually and in combination.

关键词： Deep Learning Convolutional Neural Networks android malware detection Obfuscation

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Emulation Vs Instrumentation For android malware detection

Emulation Vs Instrumentation For Android Malware Detection

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作者： Anukriti Sinha San Jose State University

学位级别：硕士

In resource constrained devices, malware detection is typically based on offline analysis using emulation. In previous work it has been claimed that such emulation fails for a significant percentage of android malware because well-designed malware detects that the code is being emulated. An alternative to emulation is malware analysis based on code that is executing on an actual android device. In this research, we collect features from a corpus of android malware using both emulation and on-phone instrumentation. We train machine learning models based on emulated features and also train models based on features collected via instrumentation, and we compare the results obtained in these two cases.

关键词： android malware detection Machine Learning Artificial Intelligence and Robotics Information Security

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