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28th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Hou, Shifu Fan, Yujie Zhang, Yiming Ye, Yanfang Lei, Jingwei Wan, Wenqiang Wang, Jiabin Xiong, Qi Shao, Fudong Case Western Reserve Univ Dept CDS Cleveland OH 44106 USA Tencent Tencent Secur Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781450369763

The explosive growth and increasing sophistication of android malware call for new defensive techniques that are capable of protecting mobile users against novel threats. To combat the evolving android malware attacks, systems of HinDroid and AiDroid have demonstrated the success of heterogeneous graph (HG) based classifiers in android malware detection;however, their success may also incentivize attackers to defeat HG based models to bypass the detection. By far, there has no work on adversarial attack and/or defense on HG data. In this paper, we explore the robustness of HG based model in android malware detection at the first attempt. In particular, based on a generic HG based classifier, (1) we first present a novel yet practical adversarial attack model (named HG-Attack) on HG data by considering android malware attackers' current capabilities and knowledge;(2) to effectively combat the adversarial attacks on HG, we then propose a resilient yet elegant defense paradigm (named Rad-HGC) to enhance robustness of HG based classifier in android malware detection. Promising experimental results based on the large-scale and real sample collections from Tencent Security Lab demonstrate the effectiveness of our developed system alpha Cyber, which integrates our proposed defense model Rad-HGC that is resilient against practical adversarial malware attacks on the HG data performed by HG-Attack.

关键词： android malware detection heterogeneous graph (HG) node classification adversarial attack and defense on HG

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A multi-view context-aware approach to android malware detection and malicious code localization

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EMPIRICAL SOFTWARE ENGINEERING 2018年第3期23卷 1222-1274页

作者： Narayanan, Annamalai Chandramohan, Mahinthan Chen, Lihui Liu, Yang Nanyang Technol Univ Singapore Singapore Nanyang Technol Univ Ctr Infocomm Technol Sch Elect & Elect Engn Singapore Singapore

Many existing Machine Learning (ML) based android malware detection approaches use a variety of features such as security-sensitive APIs, system calls, control-flow structures and information flows in conjunction with ML classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view) of apps' behaviors with inherent strengths and limitations. Meaning, some views are more amenable to detect certain attacks but may not be suitable to characterize several other attacks. Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevents them from detecting a vast majority of attacks. Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localization. The rationale is that, while a malware app can disguise itself in some views, disguising in every view while maintaining malicious intent will be much harder. MKLDroid uses a graph kernel to capture structural and contextual information from apps' dependency graphs and identify malice code patterns in each view. Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted combination of the views which yields the best detection accuracy. Besides multi-view learning, MKLDroid's unique and salient trait is its ability to locate fine-grained malice code portions in dependency graphs (e.g., methods/classes). Malicious code localization caters several important applications such as supporting human analysts studying malware behaviors, engineering malware signatures, and other counter-measures. Through our large-scale experiments on several datasets (incl. wild apps), we demonstrate that MKLDroid outperforms three state-of-the-art techniques consistently, in terms of accuracy while maintaining comparable efficiency. In our malicious code localization experiments on

关键词： android malware detection Graph kernels Multiple kernel learning Malicious code localization

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A Novel Dynamic android malware detection System With Ensemble Learning

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IEEE ACCESS 2018年 6卷 30996-31011页

作者： Feng, Pengbin Ma, Jianfeng Sun, Cong Xu, Xinpeng Ma, Yuwan Xidian Univ Sch Cyber Engn Xian 710071 Shaanxi Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China

With the popularity of android smartphones, malicious applications targeted android platform have explosively increased. Proposing effective android malware detection method for preventing the spread of malware has become an emerging issue. Various features extracted through static and dynamic analysis in conjunction with machine learning algorithm have been the mainstream in large-scale malware identification. In general, static analysis becomes invalid in detecting applications which adopt sophisticated obfuscation techniques like encryption or dynamic code loading. However, dynamic analysis is suitable to deal with these evasion techniques. In this paper, we propose an effective dynamic analysis framework, called EnDroid, in the aim of implementing highly precise malware detection based on multiple types of dynamic behavior features. These features cover system-level behavior trace and common application-level malicious behaviors like personal information stealing, premium service subscription, and malicious service communication. In addition, EnDroid adopts feature selection algorithm to remove noisy or irrelevant features and extracts critical behavior features. Extracting behavior features through runtime monitor, EnDroid is able to distinguish malicious from benign applications with ensemble learning algorithm. Through experiments, we prove the effectiveness of EnDroid on two datasets. Furthermore, we find Stacking achieves the best classification performance and is promising in android malware detection.

关键词： android security dynamic analysis ensemble learning android malware detection

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Permission-based android malware detection System Using Feature Selection with Genetic Algorithm

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2019年第2期29卷 245-262页

作者： Yildiz, Oktay Dogru, Ibrahim Alper Gazi Univ Fac Engn Dept Comp Engn Ankara Turkey

As the use of smartphones increases, android, as a Linux-based open source mobile operating system (OS), has become the most popular mobile OS in time. Due to the widespread use of android, malware developers mostly target android devices and users. malware detection systems to be developed for android devices are important for this reason. Machine learning methods are being increasingly used for detection and analysis of android malware. This study presents a method for detecting android malware using feature selection with genetic algorithm (GA). Three different classifier methods with different feature subsets that were selected using GA were implemented for detecting and analyzing android malware comparatively. A combination of Support Vector Machines and a GA yielded the best accuracy result of 98.45% with the 16 selected permissions using the dataset of 1740 samples consisting of 1119 malwares and 621 benign samples.

关键词： android malware detection static analysis machine learning support vector machines genetic algorithm feature selection

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Fine-grained android malware detection based on Deep Learning 6

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

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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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Application of Machine Learning Algorithms for android malware detection 2018

Application of Machine Learning Algorithms for Android Malwa...

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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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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 3rd

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

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3rd International Conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE)

作者： Wu, Qixin Qin, Zheng Zhang, Jinxin Yin, Hui Yang, Guangyi Hu, Kuangsheng Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China Hunan Key Lab Big Data Res & Applicat Changsha Hunan Peoples R China Hunan Inst Metrol & Test Changsha Hunan Peoples R China

ISBN: (纸本)9789811063855;9789811063848

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

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

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IEEE International Performance Computing and Communications Conference

作者： Chao Liu Jianan Li Min Yu Gang Li Bo Luo Kai Chen Jianguo Jiang Weiqing Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Information Technology Deakin University VIC Australia Department of Electrical Engineering and Computer Science University of Kansas Lawrence Kansas City USA

ISBN: (纸本)9781538668092;9781538668085

In android malware detection, sensitive data-flows 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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FgDetector: Fine-grained android malware detection 2

FgDetector: Fine-grained Android Malware Detection

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2nd IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Li, Dongfang Wang, Zhaoguo Li, Lixin Wang, Zhihua Wang, Yucheng 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 Beijing Key Lab Power Dispatching Automat Technol Beijing Peoples R China China Elect Power Res Inst Beijing Peoples R China State Grid Shanghai Municipal Elect Power Co Shanghai Peoples R China

ISBN: (纸本)9781538616000

Smartphones are rapidly becoming a necessity in our lives and android is one of the most popular mobile operating systems. However, a large number of android malicious applications hidden behind the benign applications pose a serious threat to the android platform. In this paper, we have proposed and implemented FgDetector, an automatically malware detection tool, based on machine learning models. FgDetector can extract features from android applications and convert it into a low-dimensional feature vector for training the detection model to detect whether an application is malicious or not. Furthermore, FgDetector can classify the fine-grained malware families when identifying malicious applications. To better assess, we have conducted an evaluation with a public data set with 123,453 benign applications and 5560 malware to prove the feasibility of FgDetector. Our experimental results show that FgDetector outperforms several related traditional machine learning models whether in binary classification or multiclass classification. FgDetector can detect 99.20% of the malware with a high F1-score and can achieve 93.77% precision when detecting the detailed malware family of a malicious application.

关键词： android malware detection Machine Learning Multiclass Classification Smartphones Security

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