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MVDroid: an android malicious VPN detector using neural networks

NEURAL COMPUTING & APPLICATIONS 2023年第29期35卷 21555-21565页

作者： Seraj, Saeed Khodambashi, Siavash Pavlidis, Michalis Polatidis, Nikolaos Univ Brighton Sch Architecture Technol & Engn Brighton BN2 4GJ England Islamic Azad Univ Dept Comp Engn Yadegar Eimam Khomeini RAH Shahr Erey Branch Tehran Iran

The majority of Virtual Private Networks (VPNs) fail when it comes to protecting our privacy. If we are using a VPN to protect our online privacy, many of the well-known VPNs are not secure to use. When examined closely, VPNs can appear to be perfect on the surface but still be a complete privacy and security disaster. Some VPNs will steal our bandwidth, infect our computers with malware, install secret tracking libraries on our devices, steal our personal data, and leave our data exposed to third parties. Generally, android users should be cautious when installing any VPN software on their devices. As a result, it is important to identify malicious VPNs before downloading and installing them on our android devices. This paper provides an optimised deep learning neural network for identifying fake VPNs, and VPNs infected by malware based on the permissions of the apps, as well as a novel dataset of malicious and benign android VPNs. Experimental results indicate that our proposed classifier identifies malicious VPNs with high accuracy, while it outperforms other standard classifiers in terms of evaluation metrics such as accuracy, precision, and recall.

关键词： android malware detection Virtual private network VPN Neural networks

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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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Using AI to Detect android malware Families 20

Using AI to Detect Android Malware Families

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20th International Conference on the Design of Reliable Communication Networks (DRCN)

作者： Alrabaee, Saed Al-kfairy, Mousa Taha, Mohammad Bany Alfandi, Omar Taher, Fatma El Fiky, Ahmed Hashem UAE Univ Coll IT Al Ain U Arab Emirates Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates Amer Univ Madaba Data Sci & Artificial Intelligence Madaba Jordan VERN Univ Appl Sci Business Adm Zagreb Croatia

ISBN: (纸本)9798350348972

In today's digital era, many smartphone users often overlook security measures when installing apps, leaving android devices particularly vulnerable to malware threats. Addressing this critical issue, there is a significant interest in leveraging Machine Learning (ML) and Deep Learning (DL) as proactive approaches for detecting and classifying android malware, thus aiming to safeguard mobile and IoT sectors. This study evaluates the effectiveness of data-driven methods in identifying and categorizing android malware families, specifically focusing on two advanced models: the 2-D Convolutional Neural Network (CNN) and Random Forest, which are essential for pattern recognition and decision-making. By utilizing a comprehensive dataset of android malware, our research contrasts these models' performances and unexpectedly finds that Random Forest outperforms CNN, challenging the latter's reputed superiority in complex classification scenarios. This surprising result highlights Random Forest's efficacy in cybersecurity and underscores the potential of ensemble learning methods in this domain, suggesting new directions for future research in malware detection strategies. Our findings contribute to the cybersecurity field by enhancing understanding of ML and DL applications in malware detection and underscore the necessity for continuous exploration into more intricate scenarios and advanced learning methodologies to stay ahead of evolving cyber threats, especially within the android ecosystem. This research not only opens new avenues for developing sophisticated and tailored ML/DL models but also significantly contributes to bolstering the security of mobile and IoT devices, marking a significant step forward in the ongoing battle against malware.

关键词： android apps android malware android malware detection Machine Learning Deep Learning

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29th Pacific Rim International Symposium on Dependable Computing

作者： Arifin, Md Mashrur Yeh, Jyh-haw Boise State Univ Comp Sci Dept Boise ID 83725 USA

ISBN: (纸本)9798331540746;9798331540753

Generative Adversarial Networks (GANs) have revolutionized the generation of synthetic malware images, providing significant applications in cybersecurity. Traditional image-based metrics such as Inception Score (IS) and Fr echet Inception Distance (FID) evaluate the visual quality of these images but fail to capture their malicious nature fully. We propose a novel evaluation framework, cTIMS (Correlated Textual-Based and Image-Based Metric Suites), which integrates both image and text-based metrics to provide a comprehensive assessment. Utilizing the multimodal image captioning model BLIP, we extract textual descriptions of GAN-synthesized malware images and analyze the correlation between image metrics (IS, FID) and text metrics (BLEU, METEOR, ROUGE). Additionally, we introduce Kolmogrov-Arnold Network (KAN)-based CNN architectures (VGG16-KAN, VGG19-KAN), demonstrating significant performance improvements in malware classification. The effectiveness of the Train Real Test Synthetic (TRTS) approach in validating these correlations is also evaluated, confirming the method's reliability in selecting high-quality GAN-generated images.

关键词： NLP in Cybersecurity Generative Adversarial Network android malware detection GAN-Test BLIP

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A Comprehensive Review on Machine Learning and Deep Learning Based malware detection Methods 2

A Comprehensive Review on Machine Learning and Deep Learning...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Ganesamoorthi, Mahesh Subramanian, Kannimuthu Bhanu, D. Expedia Group Seattle United States Karpagam College of Engineering Coimbatore India Karpagam Institute of Technology Coimbatore India

ISBN: (纸本)9798331534950

malware detection has become a significant aspect of cybersecurity, specifically with the widespread use of android devices. Conventional malware detection methods, such as static and signature-based approaches have been overtaken by evolving and increasingly sophisticated malware techniques. Machine learning and deep learning have emerged as powerful tools in this field, offering enhanced detection capabilities through advanced pattern recognition. This paper presents a comprehensive review of various machine learning and deep learning methods, including convolutional neural networks (CNN), Bayesian classification, ensemble learning, and hybrid models for malware detection. The study evaluates these techniques in terms of accuracy, efficiency, adaptability, and their capacity to handle real-time detection, dataset diversity, and obfuscated malware. Additionally, it explores challenges such as class imbalance and the need for more interpretable models. The findings suggest that while CNN-based methods offer the highest accuracy, ensemble models strike a balance between precision and computational efficiency. © 2024 IEEE.

关键词： android malware detection Deep learning Hybrid models Machine learning Mobile security Real-time detection

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A Formal Method for Description and Decision of android Apps Behavior Based on Process Algebra

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IEEE ACCESS 2022年 10卷 108668-108683页

作者： Liang, Dongkui Shen, Limin Chen, Zhen Ma, Chuan Feng, Jiayin Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Engn Training Ctr Qinhuangdao 066004 Hebei Peoples R China

android is the most popular mobile platform, and it has become a primary malware target. Existing behavior-based android malware detection methods suffer from false positive and false negative problems, which lead to low detection accuracy. Formal theory is crucial in studying the behaviors of android applications characterized by high concurrency, interaction, and mobility. However, existing formal methods mainly focus on specific issues and lack the essential abstraction and high-level description of application behavior. In this study, we propose a formal method for the description and decision of application behavior based on process algebra. First, we propose a formal method for describing application behavior at a component level using process algebra. By extending pi-calculus theory, we establish the mapping relationship from the android application to process algebra, and present the semantics and evolution rules of behavior based on process algebra. Second, we describe the behavior of four types of components in applications and characterize concurrent interactions of components using process algebra expressions. Third, we define the behavior equivalence and simulation mechanism for application behavior analysis and propose the decision rules based on weak simulation. Finally, we discuss a demonstration case, which includes malicious behavior, to demonstrate the feasibility and effectiveness of the proposed method. The results show that our method can accurately describe and analyze application behavior, which provides theoretical support for technologies and methods of behavior-based detection.

关键词： android malware detection behavior formalization process Algebra process equivalence simulation mechanism

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An efficient malware detection approach with feature weighting based on Harris Hawks optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第4期25卷 2369-2387页

作者： Alzubi, Omar A. Alzubi, Jafar A. Al-Zoubi, Ala' M. Hassonah, Mohammad A. Kose, Utku Al Balqa Appl Univ Prince Abdullah Bin Ghazi Fac Informat & Commun T Al Salt Jordan Al Balqa Appl Univ Fac Engn Al Salt Jordan Univ Granada Sch Sci Technol & Engn Granada Spain Suleyman Demirel Univ Dept Comp Engn Isparta Turkey

This paper introduces and tests a novel machine learning approach to detect android malware. The proposed approach is composed of Support Vector Machine (SVM) classifier and Harris Hawks Optimization (HHO) algorithm. More specifically, the role of HHO algorithm is to optimize SVM classifier hyperparameters while the SVM performs the classification of malware based on the best-chosen model, as well as producing the optimal solution for weighting the features. The effectiveness of the proposed approach and the ability to increase detection performance are demonstrated by scientific testing using CICMalAnal2017 sampled datasets. We test our method and its robustness on five sampled datasets and achieved the best results in most datasets and measures when compared with other approaches. We also illustrate the ability of the proposed approach to measure the significance of each feature. In addition, we provide deep analysis of possible relationships between weighted features and the type of malware attack. The results show that the proposed approach outperforms the other metaheuristic algorithms and state-of-art classifiers.

关键词： Machine learning Security android malware detection Harris Hawks optimization Support vector machine Feature weighting

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AProctor - A practical on-device antidote for android malware 23

AProctor - A practical on-device antidote for Android malwar...

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Australian Computer Science Week (ACSW)

作者： Patel, Akash Kumar, Nitesh Handa, Anand Shukla, Sandeep K. IIT Kanpur C3i Ctr Kanpur India

ISBN: (纸本)9798400700057

As the number of smartphone users increases, the attacker's interest in breaching android security also increases. To protect the user from malware attacks and enhance his mobile security, we present AProctor, an android application capable of differentiating between benign and malware applications on the android device. AProctor is a serverless solution. Hence it is a complete on-device solution. Indeed famous anomaly-based detection can protect the user from new and unknown malware, but it has its downsides. Anomaly-based algorithms demand a significant amount of energy and resource, but the platform for which we are developing malware detectors is resource-constrained android phones. So for this, we first implemented a fast and efficient way to extract features in the android device. We collect the dataset from various repositories and categorize them according to their packaging time. We use the feature importance technique to reduce the feature set to build lightweight models. We train our model on the server and deploy it on the device. The detection rate of AProctor is 97% when the training and testing sets are of the same period. Moreover, the detection rate comes to 92% when the test set is of a different period, i.e., for unknown APKs.

关键词： android malware detection malware Analysis Machine learning On-device detection

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Unveiling vulnerabilities in deep learning-based malware detection: Differential privacy driven adversarial attacks

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COMPUTERS & SECURITY 2024年 146卷

作者： Taheri, Rahim Shojafar, Mohammad Arabikhan, Farzad Gegov, Alexander Univ Portsmouth Fac Technol Sch Comp Portsmouth England Univ Surrey Inst Commun Syst 5G & 6G Innovat Ctr 5G 6GIC Guildford England

The exponential increase of android malware creates a severe threat, motivating the development of machine learning and especially deep learning-based classifiers to detect and mitigate malicious applications. However, these classifiers are susceptible to adversarial attacks that manipulate input data to deceive the classifier and compromise performance. This paper investigates the vulnerability of deep learning-based android malware classifiers against two adversarial attacks: Data Poisoning with Noise Injection (DP-NI) and Gradient-based Data Poisoning (GDP). In these attacks, we explore the utilization of differential privacy techniques by attackers aiming to compromise the effectiveness of deep learning based android malware classifiers. We propose and evaluate a novel defense mechanism, Differential Privacy-Based Noise Clipping (DP-NC), designed to enhance the robustness of android malware classifiers against these adversarial attacks. By leveraging deep neural networks and adversarial training techniques, DP-NC demonstrates remarkable efficacy in mitigating the impact of both DP-NI and GDP attacks. Through extensive experimentation on three diverse android datasets (Drebin, Contagio, and Genome), we evaluate the performance of DP-NC against proposed adversarial attacks. Our results show that DP-NC significantly reduces the false-positive rate and improves classification accuracy across all datasets and attack scenarios. For instance, our findings on the Drebin dataset reveal a significant decrease in accuracy to 51% and 30% after applying DP-NI and GDP techniques, respectively. However, upon applying the DP-NC defense mechanism, the accuracy in both cases improved to approximately 70%. Furthermore, employing DP-NC defense against DP-NI and GDP attacks leads to a notable reduction in false positive rates by 45.46% and 7.67%, respectively. Similar results have been obtained in two other datasets, Contagio and Genome. These results underscore the effectiveness of

关键词： Adversarial attacks android malware detection Deep learning Differential privacy Gradient perturbation

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Strengthening LLM ecosystem security: Preventing mobile malware from manipulating LLM-based applications

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INFORMATION SCIENCES 2024年 681卷

作者： Huang, Lu Xue, Jingfeng Wang, Yong Chen, Junbao Lei, Tianwei Beijing Inst Technol Beijing 100081 Peoples R China

Large language model (LLM) platform vendors have begun to make their models available for developers to build for different use cases. However, the emergence of LLM-based applications may raise security and privacy issues, and even LLM-based applications may be susceptible to malware. To strengthen LLM ecosystem security, it's crucial to develop malware detection algorithms for various platforms. We pay attention to android malware because the android platform is widely used and vulnerable. Existing single feature based-solutions cannot effectively describe applications, and aged models fail to detect new malware as android platform develops and malware evolves. Therefore, existing detection methods are ill-suited for evolved malware that may manipulate LLM-based applications. To tackle the above problems, we design EvolveDroid, an anti-aging android malware detection system. On the one hand, EvolveDroid utilizes different view features to reflect malware behavior from multiple dimensions, and maximizes the advantages of each feature type through feature aggregation. On the other hand, EvolveDroid learns good representation of applications through contrastive learning and generates pseudo labels by measuring the distance between unknown samples and existing samples for model updating. Extensive evaluations show that EvolveDroid outperforms stateof-the-art (sota) solutions in detection performance and slowing model aging.

关键词： LLM security LLM ecosystem android malware detection Contrastive learning Model aging

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