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检索条件"主题词=Android Malware detection"

共 213 条记录，以下是91-100 订阅

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Effectiveness of Video-Classification in android malware detection Through API-Streams and CNN-LSTM Autoencoders 5th

Effectiveness of Video-Classification in Android Malware Det...

引用

5th International Symposium on Mobile Internet Security (MobiSec)

作者： D'Angelo, Gianni Palmieri, Francesco Robustelli, Antonio Univ Salerno Dept Comp Sci Fisciano Italy

ISBN: (纸本)9789811695766;9789811695759

The outbreak of the COVID-19 pandemic has forced worldwide employees to massive use of their mobile devices to access corporate systems. This new scenario has made mobile devices more susceptible to malicious applications, which are yearly developed to conduct several hostile activities. Concerned about this fact, many Deep Learning (DL) based solutions have been proposed, in the last decade, by considering both static and dynamic approaches. However, static solutions are adversely affected by obfuscation techniques and polymorphic applications, while dynamic ones cannot reduce the damages caused during applications execution. To this purpose, the following paper aims to propose a novel approach called API-Streams to minimize damages at Run-time. Therefore, we investigate several Video-Classification tasks through CNN-LSTM Autoencoders (CNN-LSTM-AEs). More precisely, we combine the capability of AEs in finding compact features with the classification abilities of Deep Neural Networks (DNNs), and we show that the proposed approach achieves an average accuracy of 98% in the presence of several unbalanced training datasets. Finally, we use the t-Stochastic Neighbor Embedded (t-SNE) representation technique to investigate the abilities of the employed AE to cluster data into their respective classes by limiting their overlapping.

关键词： android malware detection API-Streams Autoencoders Video classification

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SADroid: A Deep Classification Model for android malware detection Based on Semantic Analysis

SADroid: A Deep Classification Model for Android Malware Det...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Zhu, Dali Xi, Tong Jing, Pengfei Xia, Qing Wu, Di Zhang, Yiming Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131061

Previous works have designed many deep learning models for android malware detection using various features (e.g. permissions, APIs et.) to achieve better classification performance. However, these methods usually input each feature into the classifier independently and completely (using One-Hot Encoding) so that features are orthogonal to each other. This discrete representation is difficult to preserve the semantic information of features. In this paper, we design two feature segmentation methods to enhance the semantics of the features in preprocessing. Besides that, we propose a malware detection model that consists of a distributed representation process for android features and an optimized convolutional neural network for classification, named Semantic Analysis detection (SADroid). In SADroid, the distance between features with similar semantics is closer in vector space. It provides the semantic information of features to the classifier to improve the classification performance. In the evaluation, SADroid outperforms the advanced models in detection accuracy on a data set of 19,600 applications, while maintaining a low computational cost.

关键词： Deep learning android malware detection Semantic information Distributed representation

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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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MASKDROID: Robust android malware detection with Masked Graph Representations 24

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

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

Research and implementation of Android malware detection alg...

引用

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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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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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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