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检索条件"主题词=Android Malware Detection"

共 213 条记录，以下是71-80 订阅

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FSNet: android malware detection with Only One Feature

FSNet: Android Malware Detection with Only One Feature

引用

IEEE Symposium on Computers and Communications (ISCC)

作者： Zhu, Dali Ma, Yuchen Xi, Tong Zhang, Yiming Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781728129990

Traditional android malware detection based on static analysis are experience-driven rule writing and feature engineering methods. Such methods only analyze the combination relationship or weight between rules and features of malwares, both of which lack deep representations of the semantics. In this paper, the problem of android malware detection is considered as task of text categorization, thus we propose FSNet, an effective and efficient data -driven model to solve this problem. FSNet has been designed to learn complex implicit semantic representations from data composed of a single-feature source. In addition, a new method of feature segmentation is proposed to further improve the performance of the model. Extensive experiments have been conducted. In an evaluation with two large datasets with about 16,000 applications, FSNet shows its remarkable superiority over the traditional methods and outperforms the state-of-the-art model at a fraction of the computational cost.

关键词： android malware detection Deep learning Text categorization

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MASKDROID: Robust android malware detection with Masked Graph Representations 24

MASKDROID: Robust Android Malware Detection with Masked Grap...

引用

39th ACM/IEEE International Conference on Automated Software Engineering (ASE)

作者： Zheng, Jingnan Liu, Jiaohao Zhang, An Zeng, Jun Yang, Ziqi Liang, Zhenkai Chua, Tat-Seng Natl Univ Singapore Singapore Singapore Zhejiang Univ Hangzhou Peoples R China State Key Lab Blockchain & Secur Hangzhou Peoples R China Hangzhou High Tech Zone Binjiang Inst Blockchain Hangzhou Peoples R China

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

android malware attacks have posed a severe threat to mobile users, necessitating a significant demand for the automated detection system. Among the various tools employed in malware detection, graph representations (e.g., function call graphs) have played a pivotal role in characterizing the behaviors of android apps. However, though achieving impressive performance in malware detection, current state-of-the-art graph-based malware detectors are vulnerable to adversarial examples. These adversarial examples are meticulously crafted by introducing specific perturbations to normal malicious inputs. To defend against adversarial attacks, existing defensive mechanisms are typically supplementary additions to detectors and exhibit significant limitations, often relying on prior knowledge of adversarial examples and failing to defend against unseen types of attacks effectively. In this paper, we propose MASKDROID, a powerful detector with a strong discriminative ability to identify malware and remarkable robustness against adversarial attacks. Specifically, we introduce a masking mechanism into the Graph Neural Network (GNN) based framework, forcing MASKDROID to recover the whole input graph using a small portion (e.g., 20%) of randomly selected nodes. This strategy enables the model to understand the malicious semantics and learn more stable representations, enhancing its robustness against adversarial attacks. While capturing stable malicious semantics in the form of dependencies inside the graph structures, we further employ a contrastive module to encourage MASKDROID to learn more compact representations for both the benign and malicious classes to boost its discriminative power in detecting malware from benign apps and adversarial examples. Extensive experiments validate the robustness of MASKDROID against various adversarial attacks, showcasing its effectiveness in detecting malware in real-world scenarios comparable to state-of-the-art approaches.

关键词： android malware detection Adversarial Attacks Graph Masking Graph Representation

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Structural Attack against Graph Based android malware detection 21

Structural Attack against Graph Based Android Malware Detect...

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ACM SIGSAC Conference on Computer and Communications Security (ACM CCS)

作者： Zhao, Kaifa Zhou, Hao Zhu, Yulin Zhan, Xian Zhou, Kai Li, Jianfeng Yu, Le Yuan, Wei Luo, Xiapu Hong Kong Polytech Univ Hong Kong Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781450384544

malware detection techniques achieve great success with deeper insight into the semantics of malware. Among existing detection techniques, function call graph (FCG) based methods achieve promising performance due to their prominent representations of malware's functionalities. Meanwhile, recent adversarial attacks not only perturb feature vectors to deceive classifiers (i.e., feature-space attacks) but also investigate how to generate real evasive malware (i.e., problem-space attacks). However, existing problem-space attacks are limited due to their inconsistent transformations between feature space and problem space. In this paper, we propose the first structural attack against graph-based android malware detection techniques, which addresses the inverse-transformation problem [1] between feature-space attacks and problem-space attacks. We design a Heuristic optimization model integrated with Reinforcement learning framework to optimize our structural ATtack (HRAT). HRAT includes four types of graph modifications (i.e., inserting and deleting nodes, adding edges and rewiring) that correspond to four manipulations on apps (i.e., inserting and deleting methods, adding call relation, rewiring). Through extensive experiments on over 30k android apps, HRAT demonstrates outstanding attack performance on both feature space (over 90% attack success rate) and problem space (up to 100% attack success rate in most cases). Besides, the experiment results show that combing multiple attack behaviors strategically makes the attack more effective and efficient.

关键词： Structural attack android malware detection Function call graph

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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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Towards Multi-view android malware detection Through Image-based Deep Learning 18

Towards Multi-view Android Malware Detection Through Image-b...

引用

18th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Geremias, Jhonatan Viegas, Eduardo K. Santin, Altair O. Britto, Alceu Horchulhack, Pedro Pontificia Univ Catolica Parana PUCPR Grad Program Comp Sci PPGIa Curitiba Parana Brazil Secure Syst Res Ctr Technol Innovat Inst TII Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665467490

Over the last years, several works have proposed highly accurate android malware detection techniques. Surprisingly, modern malware apps can still pave their way to official markets, thus, demanding the provision of more robust and accurate detection approaches. This paper proposes a new multi-view android malware detection through image-based deep learning, implemented threefold. First, apps are evaluated according to several feature sets in a multi-view setting, thus, increasing the information provided for the classification task. Second, extracted feature sets are converted to an image format while maintaining the principal components of the data distribution, keeping the information for the classification task. Third, built images are jointly represented in a single shot, each in a predefined image channel, enabling the application of deep learning architectures. Experiments on a new version of a publicly available android malware dataset composed of over 11 thousand android apps have shown our proposal's feasibility. It reaches true-negative rates of up to 99.5% when implemented with a single-view approach with our new image-building technique. In addition, if our proposed multi-view scheme is used, the classification accuracies of malware families become more stable, reaching a true-positive rate of up to 98.7%.

关键词： android malware detection Deep Learning Static Analysis

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Research on Data Drift and Class Imbalance in android malware detection 20th

Research on Data Drift and Class Imbalance in Android Malwar...

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20th EAI International Conference on Mobile and Ubiquitous Systems - Computing Networking and Services (EAI MobiQuitous)

作者： Liu, Zhen Wang, Ruoyu Peng, Bitao Wang, Changji Gan, Qingqing Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou 510006 Peoples R China South China Univ Technol Informat & Network Engn Res Ctr Guangzhou 510041 Peoples R China

ISBN: (纸本)9783031639883;9783031639890

In the android ecosystem, malware detection is still a nontrivial task. Existing works have recently applied convolution neural networks (CNNs) for detecting android malwares. However, data drift and class imbalance are still open problems in this field. The distribution of malware data may vary significantly if data are represented by unstable features, leading to data drift problems. The model may not be able to effectively detect malwares on the future data. In addition, the class imbalance may degrade a model on identifying a specific type of malwares with fewer training samples. To handle both of the two problems, this paper presents a new android malware detection framework. Specifically, we devise a data distribution-aware feature learning framework for learning features with a stable distribution to handle data drift. We further devise a new loss function for CNN to handle the class imbalance problem. Using our loss function, this model can reinforcement learn the minority class samples and hard samples. The experimental results on the real datasets revealed that our method outperforms existing works for android malware detection on the datasets with data drift and class imbalance problems.

关键词： android malware detection data drift class imbalance feature learning CNN

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AMDroid: android malware detection Using Function Call Graphs 19

AMDroid: Android Malware Detection Using Function Call Graph...

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19th IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Ge, Xiuting Pan, Ya Fang, Chunrong Fan, Yong Southwest Univ Sci & Technol Dept Comp Sci & Technol Mianyang Sichuan Peoples R China Nanjing Univ Software Inst Nanjing Peoples R China

ISBN: (纸本)9781728139258

With the rapid development of the mobile Internet, android has been the most popular mobile operating system. Due to the open nature of android, countless malicious applications are hidden in a large number of benign applications, which pose great threats to users. Most previous malware detection approaches mainly rely on features such as permissions, API calls, and opcode sequences. However, these approaches fail to capture structural semantics of applications. In this paper, we propose AMDroid that leverages function call graphs (FCGs) representing the behaviors of applications and applies graph kernels to automatically learn the structural semantics of applications from FCGs. We evaluate AMDroid on the Genome Project, and the experimental results show that AMDroid is effective to detect android malware with 97.49% detection accuracy.

关键词： android malware detection Static Analysis Function Call Graph Graph Kernels

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DroidDelver: An android malware detection System Using Deep Belief Network Based on API Call Blocks 17th

DroidDelver: An Android Malware Detection System Using Deep ...

引用

17th International Conference on Web-Age Information Management (WAIM)

作者： Hou, Shifu Saas, Aaron Ye, Yanfang Chen, Lifei West Virginia Univ Dept Comp Sci & Elect Engn Morgantown WV 26506 USA Fujian Normal Univ Sch Math & Comp Sci Fuzhou 350117 Peoples R China

ISBN: (纸本)9783319471211;9783319471204

Because of the explosive growth of android malware and due to the severity of its damages, the detection of android malware has become an increasing important topic in cyber security. Currently, the major defense against android malware is commercial mobile security products which mainly use signature-based method for detection. However, attackers can easily devise methods, such as obfuscation and repackaging, to evade the detection, which calls for new defensive techniques that are harder to evade. In this paper, resting on the analysis of Application Programming Interface (API) calls extracted from the smali files, we further categorize the API calls which belong to the some method in the smali code into a block. Based on the generated code blocks, we then apply a deep learning framework (i.e., Deep Belief Network) for newly unknown android malware detection. Using a real sample collection from Comodo Cloud Security Center, a comprehensive experimental study is performed to compare various malware detection approaches. Promising experimental results demonstrate that DroidDelver which integrates our proposed method outperform other alternative android malware detection techniques.

关键词： android malware detection API call block Deep belief network

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HinDroid: An Intelligent android malware detection System Based on Structured Heterogeneous Information Network 17

HinDroid: An Intelligent Android Malware Detection System Ba...

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23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Hou, Shifu Ye, Yanfang Song, Yangqiu Abdulhayoglu, Melih West Virginia Univ Dept CSEE Morgantown WV 26506 USA HKUST Dept CSE Hong Kong Peoples R China Comodo Secur Solut Inc Clinton NY USA

ISBN: (纸本)9781450348874

With explosive growth of android malware and due to the severity of its damages to smart phone users, the detection of android malware has become increasingly important in cybersecurity. The increasing sophistication of android malware calls for new defensive techniques that are capable against novel threats and harder to evade. In this paper, to detect android malware, instead of using Application Programming Interface (API) calls only, we further analyze the different relationships between them and create higher-level semantics which require more efforts for attackers to evade the detection. We represent the android applications (apps), related APIs, and their rich relationships as a structured heterogeneous information network (HIN). Then we use a meta path based approach to characterize the semantic relatedness of apps and APIs. We use each meta-path to formulate a similarity measure over android apps, and aggregate different similarities using multi-kernel learning. Then each meta-path is automatically weighted by the learning algorithm to make predictions. To the best of our knowledge, this is the first work to use structured HIN for android malware detection. Comprehensive experiments on real sample collections from Comodo Cloud Security Center are conducted to compare various malware detection approaches. Promising experimental results demonstrate that our developed system HinDroid outperforms other alternative android malware detection techniques.

关键词： android malware detection Application Programming Interface Calls Relation Analysis Heterogeneous Information Network

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Robust android malware detection against Adversarial Example Attacks 21

Robust Android Malware Detection against Adversarial Example...

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30th World Wide Web Conference (WWW)

作者： Li, Heng Zhou, Shiyao Yuan, Wei Luo, Xiapu Gao, Cuiying Chen, Shuiyan Huazhong Univ Sci & Technol Wuhan Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781450383127

Adversarial examples pose severe threats to android malware detection because they can render the machine learning based detection systems useless. How to effectively detect android malware under various adversarial example attacks becomes an essential but very challenging issue. Existing adversarial example defense mechanisms usually rely heavily on the instances or the knowledge of adversarial examples, and thus their usability and effectiveness are significantly limited because they often cannot resist the unseen-type adversarial examples. In this paper, we propose a novel robust android malware detection approach that can resist adversarial examples without requiring their instances or knowledge by jointly investigating malware detection and adversarial example defenses. More precisely, our approach employs a new VAE (variational autoencoder) and an MLP (multi-layer perceptron) to detect malware, and combines their detection outcomes to make the final decision. In particular, we share a feature extraction network between the VAE and the MLP to reduce model complexity and design a new loss function to disentangle the features of different classes, hence improving detection performance. Extensive experiments confirm our model's advantage in accuracy and robustness. Our method outperforms 11 state-of-the-art robust android malware detection models when resisting 7 kinds of adversarial example attacks.

关键词： android malware detection Adversarial Example Mobile Security

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