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Circumventing Google Play vetting policies: a stealthy cyberattack that uses incremental updates to breach privacy

Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4785-4794页

作者： Muhammad, Zia Amjad, Faisal Iqbal, Zafar Javed, Abdul Rehman Gadekallu, Thippa Reddy Department of Computer Science North Dakota State University Fargo United States Department of Cyber Security PAF Complex E-9 Air University Islamabad Pakistan Islamabad Pakistan School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Today digital technologies are evolving to accommodate small businesses and young entrepreneurs by reducing their time-to-market while encouraging rapid innovation in mobile, Extended Reality (XR), Internet of Things (IoT), cloud, and edge devices. The leading operating system Android typically takes one to a few days to perform application vetting and go to production by leveraging code analysis technologies in their Play Protect anti-malware program. However, developers with malicious intent are looking to circumvent this detection mechanism by exploiting Google’s relatively lenient trust policies that allow for package distribution and feature updates. This paper develops a proof-of-concept malware that exploits customers’ trust and Google’s policies to circumvent popular voice search applications. Our results show that attackers can initially circumvent Play Protect by uploading benign applications to build trust and then add malicious feature updates incrementally to distribute highly intrusive malware into user systems. This malware can scan and collect private user data from the device and exfiltrate it to the command-and-control server. The contributions are three-fold. (1) A proof-of-concept stealthy malware and publishing mechanism has developed that highlights the relative ease with which Google Play Protect policies may be subverted. (2) a comprehensive evaluation has been performed using major publicly available anti-malware solutions. (3) Recommendations and policies have been suggested to prevent this attack and ensure users’ privacy concerns (IMUTA is a novel attack in which malicious functionality is slowly added to a benign application through updates. This attack evades malware detection tools and exploits user trust. The attack can be launched against any application distribution platform like the Play Store). © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Mobile security

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Bridging Visual Affective Gap: Borrowing Textual Knowledge by Learning from Noisy Image-Text Pairs 24

Bridging Visual Affective Gap: Borrowing Textual Knowledge b...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Daiqing Yang, Dongbao Zhou, Yu Ma, Can School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China TMCC College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9798400706868

Visual emotion recognition (VER) is a longstanding field that has garnered increasing attention with the advancement of deep neural networks. Although recent studies have achieved notable improvements by leveraging the knowledge embedded within pre-trained visual models, the lack of direct association between factual-level features and emotional categories, called the ''affective gap'', limits the applicability of pre-training knowledge for VER tasks. On the contrary, the explicit emotional expression and high information density in textual modality eliminate the ''affective gap''. Therefore, we propose borrowing the knowledge from the pre-trained textual model to enhance the emotional perception of pre-trained visual models. We focus on the factual and emotional connections between images and texts in noisy social media data, and propose Partitioned Adaptive Contrastive Learning (PACL) to leverage these connections. Specifically, we manage to separate different types of samples and devise distinct contrastive learning strategies for each type. By dynamically constructing negative and positive pairs, we fully exploit the potential of noisy samples. Through comprehensive experiments, we demonstrate that bridging the "affective gap'' significantly improves the performance of various pre-trained visual models in downstream emotion-related tasks. Our code is released on https://***/wdqqdw/PACL. © 2024 Owner/Author.

关键词： Contrastive Learning

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Intelligent Deep Learning Model for Securing KMS Through CSF Deviation Detection 13

Intelligent Deep Learning Model for Securing KMS Through CSF...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Chauhan, Raj Ahmad, Shahnawaz Onyarin, Ogala Justin Kumar, Rajendra Noida Institute of Engineering and Technology Department of CSE-AIML Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India University of Delta Cyber Security Department Delta State Nigeria Jamia Millia Islamia Department of Computer Science New Delhi India

ISBN: (纸本)9798350380583

Cloud computing has revolutionized data content stock and administration but concurrently it has increased the vulnerability factors especially Key Management System (KMS), which is crucial since it is used to protect cypher key. Typically, there are several limitations to anomaly detection methods used currently: poor precision and insufficient speed to detect threats. This research presents an intelligent model that integrated deep learning technique into existing Cloud security Framework (CSF) to conduct proactive identification on the deviation from the baseline behavior of interactions between the applications and the KMS. This is achieved through continually accruing KMS log data, defining behavioral profiles for the applications and using clustering to categorize them appropriately. The model also continuously monitors the system for any deviations, making anomalies easily discernible for quick action either through raising alerts to stakeholders or when using the KMS API to automatically respond to the flagged issues. Studies in the multitenant cloud scenario indicate that implementation of LSTMbased model is more effective than conventional approaches, enhancing the CSF's capability in threat detection thus making it possible to quickly mount defensive measures to safeguard crucial data and cryptographic keys. © 2024 IEEE.

关键词： Cloud security

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Cloud Based AIVR Training System for Emergency Rescue 12

Cloud Based AIVR Training System for Emergency Rescue

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Dong, Meiya Zhu, Biaokai Zhao, Jumin Song, Jie School of Computer Science Peking University Beijing China Suzhou Industrial Technology Institute Zhejiang University Suzhou China Cyber Security Shanxi Police College China College of Information and Optical Engineering Taiyuan University of Technology Taiyuan China Cyber security Intelligent Policing Key Laboratory of Sichuan Province Luzhou China

ISBN: (纸本)9798331511074

This paper mainly discusses the current situation and existing problems of training exercises in emergency medicial scenarios, as well as the importance and necessity of developing an AI training system for emergency medical scenarios. First, we introduce the current status of emergency medical training and emphasize the potential value of AI technology in this field. Then, the goal of developing an AI training system is defined as improving the efficiency and quality of medical education and training. We summarize the advantages of AI technology in emergency medical exercises and make a review and selection analysis of its related technologies. In the design and implementation stage, we describe in detail the requirement analysis, function design, system architecture, and core module development of the emergency medical AI training system. After system integration and testing, we verified the feasibility and effect of the system. In the process of evaluation and optimization, we evaluated the performance and user experience of the system and put forward optimization strategies and security considerations for the problems. In the application case analysis, we collect user feedback to verify its efficiency and carry out an in-depth analysis of the advantages and disadvantages of the system. Finally, in the conclusion and prospect part, we summarize the research results, point out the existing problems and improvement directions and look forward to the future development trends and challenges of AI training systems for emergency medical scenarios. © 2024 IEEE.

关键词： AI training AIVR system Emergency Rescue

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Conformal Graph-level Out-of-distribution Detection with Adaptive Data Augmentation 25

Conformal Graph-level Out-of-distribution Detection with Ada...

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34th ACM Web Conference, WWW 2025

作者： Lin, Xixun Cao, Yanan Sun, Nan Zou, Lixin Zhou, Chuan Zhang, Peng Zhang, Shuai Zhang, Ge Wu, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Wuhan University Wuhan China Academy of Mathematics and Systems Science CAS School of Cyber Security University of Chinese Academy of Sciences Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Academy of Mathematics and Systems Science CAS Beijing China School of Computer Science and Technology Donghua University Shanghai China Department of Computing Macquarie University Sydney Australia

ISBN: (纸本)9798400712746

Graph-level out-of-distribution (OOD) detection, which attempts to identify OOD graphs originated from an unknown distribution, is a vital building block for safety-critical applications in Web and society. Current approaches concentrate on how to learn better graph representations, but fail to provide any statistically guarantee on detection results, therefore impeding their deployments in the scenario where detection errors would result in serious consequences. To overcome this critical issue, we propose the Conformal Graph-level Out-of-distribution Detection (CGOD), extending the theory of conformal prediction to graph-level OOD detection with a rigorous control over the false positive rate. In CGOD, we develop a new aggregated non-conformity score function based on the proposed adaptive data augmentation. Through the guidance from two designed metrics, i.e., score consistency and representation diversity, our augmentation strategy can generate multiple non-conformity scores, and aggregating these generated non-conformity scores together is robust to the misleading information. Meanwhile, our score function can perceive the subsequent process of conformal inference, enabling the aggregated non-conformity score to be adaptive to different input graphs and deriving a more accurate score estimation. We conduct experiments on multiple real-world datasets with different empirical settings. Extensive results and model analyses demonstrate the superior performance of our approach over several competitive baselines. © 2025 Copyright held by the owner/author(s).

关键词： Network theory (graphs)

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IceCache: Recommendation-Based Edge Caching for Life Cycle of Video

IceCache: Recommendation-Based Edge Caching for Life Cycle o...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Cao, Shaohua Xing, Wanli Zheng, Quancheng Lv, Huaqi Yuan, Xuyang Zhan, Zijun Fang, Kai Hasan, Mohammad Kamrul Zhang, Weishan Qingdao China School of Science and Engineering The Chinese University of HongKong Shenzhen China University of Houston Electrical and Computer Engineering Department HoustonTX United States College of Mathematics and Computer Science Zhejiang A&f University Hangzhou China Universiti Kebangsaan Malaysia Center for Cyber Security Selangor Bangi Malaysia

ISBN: (纸本)9798350368369

The surge in Internet video traffic driven by 5G advancement strains network infrastructure. Edge computing emerges as a solution for video distribution, yet faces challenges from limited cache capacity and dynamic user requests. To address these challenges, we propose IceCache - a recommendationdriven edge Caching architecture for the life cycle of video streaming. IceCache enhances Quality of Experience (QoE) while reducing backhaul traffic through two-stage caching: cache placement before playback, dynamic prefetching and cache admission during playback. A user behavior simulation integrating recommender systems was developed to evaluate the proposed caching strategy. Experiments on real-world MovieLens and synthetic datasets validated the strategy's performance. © 2025 IEEE.

关键词： Cache memory

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Integrating Consortium Blockchain and Attribute-Based Searchable Encryption for Automotive Threat Intelligence Sharing Model 1

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6th International Conference on science of cyber security, SciSec 2024

作者： Xie, Tiange Liu, Feng Gao, Jiechao Wang, Yinghui Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Department of Computer Science University of Virginia CharlottesvilleVA United States School of Transportation Science and Engineering Beihang University Beijing China

ISBN: (数字)9789819624171

ISBN: (纸本)9789819624164

Sharing cyber threat intelligence (CTI) enhances organizations’ abilities in threat detection and emergency response, fostering a proactive defense approach of "predict and prevent". Given the sensitive nature of automotive CTI and trust issues inherent in its sharing process, this paper presents an automotive CTI security sharing model that integrates blockchain and attribute-based searchable encryption. Leveraging consortium blockchain for CTI sharing, our model addresses the single point of failure and distrust in centralized systems through blockchain’s decentralization and immutability. Furthermore, by combining attribute-based searchable encryption algorithms with smart contracts, we achieve fine-grained access control and ciphertext retrieval for automotive CTI data. This enables data users to independently search CTI on the blockchain, mitigating the risk of sensitive information disclosure. Additionally, we store encrypted CTI data off-chain in the InterPlanetary File System (IPFS) to alleviate blockchain’s storage burden. Finally, we develop an automotive CTI sharing prototype system to demonstrate the feasibility and effectiveness of our proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Access control

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Machine Learning Approach for Detection of DDOS Attack in Cloud Environment

Machine Learning Approach for Detection of DDOS Attack in Cl...

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Emerging Trends in engineering and Medical sciences (ICETEMS), International Conference on

作者： Harshala Shingne Kartik Vhonale Pratham Shrivastava Purva Gholse Rahul Barlinge Mohammad Amash Alam Computer Science and Engineering-Cyber Security Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

The cloud computing realm faces constant security threats like DDoS attacks, affecting service availability. Existing solutions struggle with efficiency in detection due to their emphasis on accuracy over speed. This inefficiency arises because these solutions prioritize accuracy over speed. While they may accurately identify DDoS attacks, they do so at the cost of speed, resulting in delays in detecting and mitigating these attacks. Proposed model (ensemble): Achieved an accuracy of 0.95 with a detection time of 12 milliseconds. These values demonstrate not only the improved accuracy of the proposed solution but also its enhanced efficiency in detecting DDoS attacks compared to individual models. The reduced detection time is crucial for timely mitigation, ensuring minimal disruption to service availability in cloud environments. This paper proposes a novel approach: integrating KNN, Random Forest, and AdaBoost into DDoS detection models. By blending diverse machine learning methods and refining parameters, we aim to boost accuracy and efficiency. Leveraging labeled datasets and ensemble learning enhances the system's ability to differentiate normal and malicious activity. We emphasize seamless integration with cloud monitoring services for scalability and resource optimization. Our solution, through iterative optimization and model hybridization, fortifies defenses against evolving DDoS threats, ensuring timely mitigation and service availability. This underscores the urgency for innovative detection methods to bolster cloud security. Our study, by prioritizing efficiency and practical implementation, advances cybersecurity research and fortifies cloud infrastructures against cyber threats.

关键词： Accuracy Machine learning algorithms Prevention and mitigation Nearest neighbor methods Denial-of-service attack Ensemble learning computer crime Protection Random forests Optimization

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Secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks

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Alexandria engineering Journal 2025年 128卷 127-143页

作者： Islam, Shayla Topazal, S.M. Hasan, Mohammad Kamrul Kolandaisamy, Raenu AL Abbas, Huda Saleh Vo, Nguyen Thakkar, Rahul Khan, Muhammad Attique Gupta, Deepak AlHammadi, Dina Abdulaziz Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology the National University of Malaysia Selangor Bangi43600 Malaysia Department of computer science College of Computer Science and Engineering Taibah University Madina Saudi Arabia Victorian Institute of Technology Melbourne Australia Center of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Maharaja Agrasen Institute of Technology Delhi India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh11671 Saudi Arabia

The emergence of 5 G and 6 G networks promises significant advancements in the Internet of Medical Things (IoMT) by enabling real-time medical data transmission and continuous connectivity for mobile healthcare devices. However, traditional Handover (HO) mechanisms face latency, security, energy consumption, and scalability challenges when supporting fast-moving and dense IoMT environments. This paper presents a secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks, incorporating a predictive algorithm, Blockchain-Based (BC-B) decentralized authentication mechanism, and AI-driven anomaly detection. The predictive algorithm anticipates device movement and pre-allocates resources at the target Base Station (BS), reducing HO latency by 20–30 % in 5 G and up to 40 % in 6 G. Meanwhile, blockchain-based authentication ensures tamper-proof and decentralized security, reducing vulnerability to cyberattacks by 90 % compared to traditional methods. Additionally, the energy consumption during HO events is reduced by 20–35 %, dropping from 15 mJ in traditional schemes to 12 mJ in 5 G and further to 10 mJ in 6 G. The scheme also incorporates AI-driven anomaly detection, achieving an accuracy of 96.7 %, ensuring proactive identification and mitigation of potential security threats. The analyzed scheme maintains consistent throughput with less than 5 % reduction and scales effectively for up to 300 devices per cell. The simulation results demonstrate significant improvements across key performance metrics, including latency, security, energy efficiency, and scalability, making this approach highly suitable for critical IoMT applications such as telesurgery, remote patient monitoring, and innovative healthcare environments. These advancements position the analyzed framework as a cornerstone for next-generation (NG) healthcare networks. © 2025 The Authors

关键词： Remote patient monitoring

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Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

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computer Modeling in engineering & sciences 2023年第10期137卷 813-825页

作者： Shayla Islam Anil Kumar Budati Mohammad Kamrul Hasan Saoucene Mahfoudh Syed Bilal Hussian Shah Institute of Computer Science&Digital Innovation ICSDIUCSI UniversityKuala Lumpur56000Malaysia Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Selangor43600Malaysia School of Engineering Computing and InformaticsDar Al-Hekma UniversityJeddah22246Saudi Arabia

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientRadio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the *** Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.

关键词： Ambient backscatter communication cognitive radio MFDI CFDI HFDI

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