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An android malware detection and Classification Approach Based on Contrastive Lerning

COMPUTERS & SECURITY 2022年 123卷

作者： Yang, Shaojie Wang, Yongjun Xu, Haoran Xu, Fangliang Chen, Mantun Natl Univ Def Technol Coll Comp Changsha Peoples R China

android malware detection is a serious issue for mobile security. Recent machine learning-based research could achieve high accuracy. However, there are far more unlabeled samples in the application scenario, while most studies must depend on labeled data for training. This paper proposed, as a solution, a frame-work based on contrastive learning that attempted to reduce the impact of past knowledge and pretrain the model without the participation of labels. The result indicated that the method could reach an ac-curacy above 96% for malware identification on public datasets and above 98% for multiclass detection (malware class and family). In addition, the model could have higher performance than the same super-vised model under the condition of a small proportion of labeled samples. Comparable performance to supervised approaches validated the feasibility of contrastive learning on malware detection by demon-strating its viability. Nonetheless, there are still gaps to be overcome in the future.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： android malware detection malware classification Contrastive learning Feature encoding Feature extraction Deep learning

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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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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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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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DroidmalwareDetector: A novel android malware detection framework based on convolutional neural network

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 206卷

作者： Kabakus, Abdullah Talha Duzce Univ Fac Engn Dept Comp Engn TR-81620 Duzce Turkey Duzce Univ Fac Engn Dept Comp Engn Konuralp Campus TR-81620 Duzce Turkey

Smartphones have become an integral part of our daily lives thanks to numerous reasons. While benefitting from what they offer, it is critical to be aware of the existence of malware in the android ecosystem and be away from them. To this end, an end-to-end and highly effective android malware detection framework based on CNN, namely, DroidmalwareDetector, was proposed within this study. Unlike most of the related work, DroidMalwar-eDetector was specifically designed to (i) automate feature extraction and selection, (ii) propose a novel CNN that operates with 1-dimensional data, and (iii) use intents and API calls alongside the widely used permissions to perform comprehensive malware analysis. The proposed framework was trained and evaluated on the con-structed dataset, which consisted of 14,386 apps from the de-facto standard datasets. The proposed framework's efficiency in terms of distinguishing malware from benign apps was revealed thanks to the conducted experi-ments. According to the experimental result, the accuracy of the proposed framework was calculated as high as 0.9, which was higher than the accuracy values obtained from a wide range of machine learning algorithms. The insights which were gained through the conducted experiments were revealed as another contribution to the research field.

关键词： android android malware detection Deep neural network Convolutional neural network Mobile security

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An optimized and efficient android malware detection framework for future sustainable computing

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年 54卷

作者： Smmarwar, Santosh K. Gupta, Govind P. Kumar, Sanjay Kumar, Prabhat Natl Inst Technol Raipur Dept Informat Technol Raipur 492010 Madhya Pradesh India LUT Univ Dept Software Engn LUT Sch Engn Sci Lappeenranta 53850 Finland

android-based smart devices cater to services in almost every aspect of our lives like personal, professional, social, banking, business, etc. However, people with increasingly dependent on the smartphone, malicious attacks against these gadgets have increased exponentially. To achieve a secured smart environment for future sustainable computing, android-based smart devices must provide more resilient and attack-resistant commitments, so that the android malware detector, once trained on a dataset, can continue to identify new malware without retraining. However, most of the existing malware detection approach suffers from a high false-positive rate and low detection rate of new malware variants. Motivated by the aforementioned challenges. In this paper, an Optimized and efficient Ensemble Learning-based android malware detection framework, called "OEL-AMD" is proposed. This framework employs statistical feature engineering to eliminate non-informative features as well as encode statistical characteristics, and Binary Grey Wolf Optimization (BGWO)-based meta-heuristic feature selection is used to prepare optimal feature sets for static and dynamic layers. Subsequently, different base learners are trained using hyper-parameters tuning to boost the inductive reasoning capability of the ensemble model for classification, and an aggregated performance is computed. The obtained highest classification accuracy is 96.95% for binary and 83.49% for the category classification using static and dynamic layer features, respectively reveals the promising performance. Further, the obtained results are compared with the existing methods, and statistical significance is evaluated. The statistical test verified the significance of the obtained results.

关键词： Smart Environment android malware detection Sustainability Ubiquitous computing

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MalWhiteout: Reducing Label Errors in android malware detection 22

MalWhiteout: Reducing Label Errors in Android Malware Detect...

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Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

作者： Liu Wang Haoyu Wang Xiapu Luo Yulei Sui Huazhong University of Science and Technology China and Beijing University of Posts and Telecommunications China Huazhong University of Science and Technology China The Hong Kong Polytechnic University China University of Technology Sydney Australia

ISBN: (纸本)9781450394758

Machine learning based android malware detection has attracted a great deal of research work in recent years. A reliable malware dataset is critical to evaluate the effectiveness of malware detection approaches. Unfortunately, existing malware datasets used in our community are mainly labelled by leveraging existing anti-virus services (i.e., VirusTotal), which are prone to mislabelling. This, however, would lead to the inaccurate evaluation of the malware detection techniques. Removing label noises from android malware datasets can be quite challenging, especially at a large data scale. To address this problem, we propose an effective approach called MalWhiteout to reduce label errors in android malware datasets. Specifically, we creatively introduce Confident Learning (CL), an advanced noise estimation approach, to the domain of android malware detection. To combat false positives introduced by CL, we incorporate the idea of ensemble learning and inter-app relation to achieve a more robust capability in noise detection. We evaluate MalWhiteout on a curated large-scale and reliable benchmark dataset. Experimental results show that MalWhiteout is capable of detecting label noises with over 94% accuracy even at a high noise ratio (i.e., 30%) of the dataset. MalWhiteout outperforms the state-of-the-art approach in terms of both effectiveness (8% to 218% improvement) and efficiency (70 to 249 times faster) across different settings. By reducing label noises, we show that the performance of existing malware detection approaches can be improved.

关键词： confident learning android malware detection label noise

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A Systematic Literature Review of android malware detection Using Static Analysis

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IEEE ACCESS 2020年 8卷 116363-116379页

作者： Pan, Ya Ge, Xiuting Fang, Chunrong Fan, Yong Southwest Univ Sci & Technol Dept Comp Sci & Technol Mianyang 621000 Sichuan Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Peoples R China

android malware has been in an increasing trend in recent years due to the pervasiveness of android operating system. android malware is installed and run on the smartphones without explicitly prompting the users or without the user's permission, and it poses great threats to users such as the leakage of personal information and advanced fraud. To address these threats, various techniques are proposed by researchers and practitioners. Static analysis is one of these techniques, which is widely applied to android malware detection and can detect malware quickly and prohibit malware before installation. To provide a clarified overview of the latest work in android malware detection using static analysis, we perform a systematic literature review by identifying 98 studies from January 2014 to March 2020. Based on the features of applications, we first divide static analysis in android malware detection into four categories, which include android characteristic-based method, opcode-based method, program graph-based method, and symbolic execution-based method. Then we assess the malware detection capability of static analysis, and we compare the performance of different models in android malware detection by analyzing the results of empirical evidence. Finally, it is concluded that static analysis is effective to detect android malware. Moreover, there is a preliminary result that neural network model outperforms the non-neural network model in android malware detection. However, static analysis still faces many challenges. Thus, it is necessary to derive some novel techniques for improving android malware detection based on the current research community. Moreover, it is essential to establish a unified platform that is used to evaluate the performance of a series of techniques in android malware detection fairly.

关键词： malware Static analysis Feature extraction Analytical models Bibliographies Sensitivity Systematics android malware detection static analysis systematic literature review

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Deep and broad URL feature mining for android malware detection

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INFORMATION SCIENCES 2020年 513卷 600-613页

作者： Wang, Shanshan Chen, Zhenxiang Yan, Qiben Ji, Ke Peng, Lizhi Yang, Bo Conti, Mauro Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan Peoples R China Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA Univ Padua Dept Math Padua Italy

In recent years, the scale and diversity of malicious software on mobile networks have grown significantly, thereby causing considerable danger to users' property and personal privacy. In this study, we propose a malware detection method that uses the URLs visited by apps to identify malware. A multi-view neural network is used to create a malware detection model that emphasizes depth and width. This neural network can create multiple views of inputs automatically and distribute soft attention weights to focus on different features of inputs. Multiple views preserve rich semantic information from inputs for classification without requiring complicated feature engineering. In addition, we conduct comprehensive experiments to compare the proposed method with others and verify the validity of the detection model. The experimental results show that our method achieves robust and timely malware detection. It can not only effectively detect malware discovered in different months of a certain year, but also detect potentially malicious apps in the third-party app market. We also compare the detection results of the proposed method on wild apps with 10 popular anti-virus scanners, and the final result shows that our approach ranks second in terms of detection performance. (C) 2019 Elsevier Inc. All rights reserved.

关键词： android malware detection Multi-view neural network URL feature mining

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android malware detection based on system call sequences and LSTM

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第4期78卷 3979-3999页

作者： Xiao, Xi Zhang, Shaofeng Mercaldo, Francesco Hu, Guangwu Sangaiah, Arun Kumar Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China Natl Res Council Italy Inst Informat & Telemat I-56124 Pisa Italy Shenzhen Inst Informat Technol Sch Comp Sci Shenzhen 518172 Peoples R China VIT Univ Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

As android-based mobile devices become increasingly popular, malware detection on android is very crucial nowadays. In this paper, a novel detection method based on deep learning is proposed to distinguish malware from trusted applications. Considering there is some semantic information in system call sequences as the natural language, we treat one system call sequence as a sentence in the language and construct a classifier based on the Long Short-Term Memory (LSTM) language model. In the classifier, at first two LSTM models are trained respectively by the system call sequences from malware and those from benign applications. Then according to these models, two similarity scores are computed. Finally, the classifier determines whether the application under analysis is malicious or trusted by the greater score. Thorough experiments show that our approach can achieve high efficiency and reach high recall of 96.6% with low false positive rate of 9.3%, which is better than the other methods.

关键词： android malware detection System call sequences Deep learning LSTM language model

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