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SafeGuard: a behavior based real-time malware detection scheme for mobile multimedia applications in android platform

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第17期76卷 18153-18173页

作者： Jeong, Eun Su Kim, In Seok Lee, Dong Hoon SK Planet Co Ltd 264 Pangyo Ro Seongnam Si Gyeonggi Do South Korea Korea Univ CIST Seoul South Korea

SafeGuard is proposed as a solution to monitor behaviors of smartphone applications in real-time and detect and block any malicious behaviors. This solution consists of a server that manages and deploys the blocking rules and the device solution that monitors various applications in android devices. The proposed scheme provides users with real-time malware information such as spyware detected by the SafeGuard library upon suspicious API call within the android platform. Except for use of Rootkit at the kernel level, the scheme can detect behaviors that use the API from the platform or caused by a combination of those APIs. The database that determines any malicious behaviors can be periodically updated to block various malicious behaviors by using preemptive responses different from existing anti-virus products. For this purpose, the behaviors of smartphone applications are classified and are defined for monitoring. The architecture to apply them is also proposed in the android framework and the proposed scheme is applied in the android smartphone environment to verify its stability and feasibility through measuring the overhead in the environment.

关键词： android malware detection Mobile multimedia application Behavior detection android platform Mobile security

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CloudIntellMal: An advanced cloud based intelligent malware detection framework to analyze android applications

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期119卷

作者： Mishra, Preeti Jain, Tanmay Aggarwal, Palak Paul, Gunjan Gupta, Brij B. Attar, Razaz Waheeb Gaurav, Akshat Doon Univ Sch Technol Dept Comp Sci Dehra Dun India Reverie Language Technol Ltd Bangalore India Cognizant Ghaziabad India Amazon Bangalore India Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan Kyung Hee Univ 26 Kyungheedae Ro Seoul South Korea Symbiosis Int Univ Symbiosis Ctr Informat Technol SCIT Pune India Univ Petr & Energy Studies UPES Ctr Interdisciplinary Res Dehra Dun India Princess Nourah bint Abdulrahman Univ Coll Business Adm Management Dept POB 84428 Riyadh 11671 Saudi Arabia Ronin Inst Montclair NJ USA

Serverless computing has become very popular in recent times which facilitates a greater abstraction in virtualization technology. With the rapid development in serverless computing, a new paradigm has been evolved to design and develop android applications. However, the android platform has become one of the most vulnerable targets due to the increase in the number of users and hence raises a strong security concern to develop an advanced security framework. In this paper, a serverless computing based intelligent malware detection framework, called CloudIntellMal is proposed to detect malware targeting android-based applications. The framework incorporates some popular key cloud services such as Amazon Web Services (AWS) Elastic Cloud Compute (EC2) and Simple Storage Service (S3) to store and pre-process the logs taken from the end user devices. An efficient feature extraction algorithm is designed that employs 'Bag of n-grams' to develop feature vectors. The framework runs the machine learning algorithm in the cloud infrastructure to learn malicious patterns. The decision model is loaded in EC2 at the time of detection phase to classify the monitored apps. A prototype of the approach has been developed and validated using Drebin dataset and results seem to be promising.

关键词： android malware detection Static analysis Amazon EC2 Amazon S3 malware detection Artificial Intelligence

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DeepFlow: Deep Learning-Based malware detection by Mining android Application for Abnormal Usage of Sensitive Data

DeepFlow: Deep Learning-Based Malware Detection by Mining An...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Zhu, Dali Jin, Hao Yang, Ying Wu, Di Chen, Weiyi Univ Chinese Acad Sci Sch Cyber Secur Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Comp Control Inst Optoelect Beijing Peoples R China

ISBN: (纸本)9781538616291

The open nature of android allows application developers to take full advantage of the system. While the flexibility is brought to developers and users, it may raise significant issues related to malicious applications. Traditional malware detection approaches based on signatures or abnormal behaviors are invalid when dealing with novel malware. To solve the problem, machine learning algorithms are used to learn the distinctions between malware and benign apps automatically. Deep learning, as a new area of machine learning, is developing rapidly as its better characterization of samples. We thus propose DeepFlow, a novel deep learning-based approach for identifying malware directly from the data flows in the android application. We test DeepFlow on thousands of benignware and malware. The results show that DeepFlow can achieve a high detection F1 score of 95.05%, outperforming traditional machine learning-based approaches, which reveals the advantage of deep learning technique in malware detection.

关键词： android malware detection deep learning data flows characterization

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A Transparent and Multimodal malware detection Method for android Apps 19

A Transparent and Multimodal Malware Detection Method for An...

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22nd ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (ACM MSWiM)

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

ISBN: (纸本)9781450369046

While recent works have shown that deep learning method can improve the malware classification accuracy, the lack of the transparency has restricted its application in anti-virus scan engines. Existing researches have attempted to provide solutions to give high-fidelity explanations of the model's decision. However, current methods are not optimized for application security task, leading to a poor performance in android malware detection. In this paper, we propose a backtracking method to infer suspicious features of the apps to explain the reason of classification. Besides, we also propose a malware detection model based on the fusion convolutional neural network using different types of features (e.g., permission, API, URL, etc.). For maximizing the benefits of encompassing multiple feature types, our framework trains the sub-models for each type of features separately and merges them at the end of the system to obtain a comprehensive classification result. The experimental results show that the backtracking method has a significant improvement in fidelity level compared with existing methods. Furthermore, we evaluate the performance of the proposed framework with other existing works. Leveraging the backtracking method, our framework has better performance in classification and significantly reduces detection time by 69% compared with prior approaches.

关键词： android malware detection transparency backtracking convolutional neural network

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MulAV: Multilevel and Explainable detection of android malware with Data Fusion 18th

MulAV: Multilevel and Explainable Detection of Android Malwa...

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18th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Li, Qun Chen, Zhenxiang Yan, Qiben Wang, Shanshan Ma, Kun Shi, Yuliang Cui, Lizhen Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China Univ Nebraska Lincoln NE USA Shandong Univ Jinan 250101 Shandong Peoples R China

ISBN: (纸本)9783030050634;9783030050627

With the popularization of smartphones, the number of mobile applications has grown substantially. However, many malware are emerging and thus pose a serious threat to the user's mobile phones. malware detection has become a public concern that requires urgent resolution. In this paper, we propose MulAV, a multilevel and explainable detection method with data fusion. Our method obtain information from multiple levels (the APP source code, network traffic, and geospatial information) and combine it with machine learning method to train a model which can identify mobile malware with high accuracy and few false alarms. Experimental result shows that MulAV outperforms other anti-virus scanners and methods and achieves a detection rate of 97.8% with 0.4% false alarms. Furthermore, for the benefit of users, MulAV displays the explanation for each detection, thus revealing relevant properties of the detected malware.

关键词： android malware detection Data fusion Multilevel Result explanation

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An Early detection of android malware Using System Calls based Machine Learning Model 22

An Early Detection of Android Malware Using System Calls bas...

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17th International Conference on Availability, Reliability and Security (ARES)

作者： Zhang, Xinrun Mathur, Akshay Zhao, Lei Rahmat, Safia Niyaz, Quamar Javaid, Ahmad Yang, Xiaoli Purdue Univ Northwest Hammond IN 46323 USA Univ Toledo 2801 W Bancroft St Toledo OH 43606 USA

ISBN: (纸本)9781450396707

Several host intrusion detection systems (HIDSs) based on system call analysis have been proposed in the past to detect intrusions and malware using relevant datasets. Machine learning (ML) techniques have been applied on those datasets to improve the performances of HIDSs. However, the emphasis given on their real-world deployment is limited. To address this issue, we propose a framework for system call processing for benign and malware android apps with an ability of early detection of malware. We extracted and analyzed system call traces for benign and malware apps, and processed their system call traces with N-gram and TF-IDF models. Six ML algorithms - Decision Trees, Random Forest, K-Nearest Neighbors, Naive Bayes, Support Vector Machines, and Multi-layer Perceptron - were trained for the malware detection system. The experimental results demonstrate that our android malware detection system (AMDS), using traces of 3000 system calls, is capable of early detection with an average accuracy of 99.34%. We also implemented an android app based on a client-server architecture for the proposed AMDS to demonstrate its deployment for malware detection in real-time.

关键词： android malware detection smartphone security system call analysis

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Exploring Feature Extraction and ELM in malware detection for android Devices 12th

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12th International Symposium on Neural Networks (ISNN)

作者： Zhang, Wei Ren, Huan Jiang, Qingshan Zhang, Kai Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Sci & Technol China Hefei 230051 Peoples R China

ISBN: (纸本)9783319253930;9783319253923

A huge increase in the number of mobile malware brings a serious threat to Internet security, as the adoption rate of mobile device is soaring, especially android device. A variety of researches have been developed to defense malware, but the mobile device users continuously suffer private information leak or economic losses from malware. Recently, a large number of methods have been proposed based on static or dynamic features analysis combining with machine learning methods, which are considered effective to detect malware on mobile device. In this paper, we propose an effective framework to detect malware on android device based on feature extraction and neural network calssifier. In this framework, we take use of static features to represent malware and utilize extreme learning machine (ELM) algorithm to learn the neural network. We first extract features from the malware, and then utilize three different feature extraction methods including principal component analysis (PCA), Karhunen-Loeve transform (KLT) and independent component analysis (ICA) to transform the feature matrix into new feature spaces and generate three new feature matrixes. For each feature matrix, we construct En base classifiers by using ELM. Finally, we utilize Stacking method to combine the results. Experimental results suggest that the proposed framework is effective in detecting malware on android device.

关键词： Feature extraction android malware detection ELM Stacking method

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Deep Convolution Neural Networks for Image-Based android malware Classification

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Computers, Materials & Continua 2025年第3期82卷 4093-4116页

作者： Amel Ksibi Mohammed Zakariah Latifah Almuqren Ala Saleh Alluhaidan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadhP.O.Box 11442Saudi Arabia

The analysis of android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of *** resolve this problem,efficient and flexible malware detection tools are *** work examines the possibility of employing deep CNNs to detect android malware by transforming network traffic into image data ***,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:***,***,***,***,*** network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained *** addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify android malware,providing greater accuracy than conventional *** success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.

关键词： android malware detection deep convolutional neural network(DCNN) image processing CIC-AndMal2017 dataset exploratory data analysis VGG16 model

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Hybrid Multilevel detection of Mobile Devices malware Under Concept Drift

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2025年第2期33卷 1-32页

作者： Augello, Andrea De Paola, Alessandra Lo Re, Giuseppe Univ Palermo Dept Engn Palermo Italy

malwares are a major threat to the security of mobile devices, and Machine Learning (ML) is a widespread approach to automatically detect them. However, running ML analysis pipelines can be excessively burdensome for energy-constrained mobile devices. On the other hand, completely off-loading all the analysis to a remote server can introduce unacceptable communication overheads and delays in the detection process. In this paper, we propose a multilevel approach for malware detection on mobile devices that combines a lightweight local analysis of static features with a more computationally expensive remote analysis of dynamic features, through the adoption of ML methods. However, the effectiveness of automatic malware detection systems based on ML is often limited by unforeseen variations in the statistical characteristics of the observed data. This phenomenon, known as concept drift, can lead to a degradation of the performance of ML models over time. The proposed malware detection system is equipped with self-evaluation capabilities, enabling it to detect the occurrence of periods when its predictions become unreliable due to concept drift so that appropriate response strategies can be activated. In particular, when such critical events occur, the self-evaluation agent triggers the execution of an additional layer of analysis, hosted by a remote server, which allows the system to react to the unexpected reduction in its detection capabilities. The computational cost of the detection process is minimized by limiting the remote analysis to only those samples for which the analysis performed on-board the mobile device is likely to incorrectly classify the app.

关键词： android malware detection Mobile security Concept drift Machine learning

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ASDroid: Resisting Evolving android malware With API Clusters Derived From Source Code

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 1822-1835页

作者： Hu, Qihua Wang, Weiping Song, Hong Guo, Song Zhang, Jian Zhang, Shigeng Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

Machine learning-based android malware detection has consistently demonstrated superior results. However, with the continual evolution of the android framework, the efficacy of the deployed models declines markedly. Existing solutions necessitate frequent and expensive model retraining to resist the constant evolution of malware accompanying android framework updates. To address this, we introduce a solution called ASDroid, which generalizes specific APIs into similar API clusters to counteract evolving android malware threats. One primary challenge lies in identifying analogous API clusters that correspond to specific APIs. Our approach involves extracting semantic information from open-source API source code to construct a heterogeneous information graph, and utilizing embedding algorithms to obtain semantic vector representations of APIs. APIs that are close in embedding distance are presumed to have similar semantics. Our dataset encompasses android applications spanning nine years from 2011 to 2019. In comparison to existing android malware detection model aging mitigation solutions like APIGraph, SDAC and MaMaDroid, ASDroid demonstrates greater accuracy and more effective at resisting continuously evolving malware.

关键词： Source coding Prevention and mitigation Semantics Clustering algorithms Resists Aging malware Vectors Data mining Resilience Mobile security android malware detection model aging

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