检索结果-内蒙古大学图书馆

CAED: A Comprehensive Android Emulator Detection Framework with Data Augmentation

IEEE Internet of Things Journal 2025年第13期12卷 22847-22861页

作者： Yan, Ran Niu, Weina Hou, Qinsheng Su, YuChi Gong, Jiacheng Zhang, Xiaosong School of Computer Science and Engineering Institute for Cyber Security Chengdu611731 China Shanghai Jiao Tong University School of Cyber Science and Engineering China

Anti-emulation is crucial for Android and IoT security as it helps apps determine whether they are running on a real mobile device or in an emulation environment. This prevents apps from being analyzed, debugged, or reverse-engineered in emulators, ultimately stopping criminals from making illegal profits. Current emulator detection methods cannot balance accuracy, universality, robustness, and compatibility. Their universality is often hindered by limited data diversity and accessibility. To address these issues, we propose the Comprehensive Android Emulator Detection (CAED) framework. The Preprocessing Module of CAED collects and normalizes data from both phones and emulators. We propose the first data augmentation method for emulator detection, EDA-GAN, which is tailored to the characteristics of our data and effectively enhances data diversity. The classifier module MFBoost employs an adaptive imputation algorithm and multiple Classification and Regression Trees (CART) for precise classification. Experiments on 324 devices show that CAED improves detection rate by at least 12.5% and up to 44.71% over state-of-the-art (SOTA) methods. The EDA-GAN data augmentation method boosts classifier accuracy, achieving a performance of up to 99.62%. Additionally, CAED’s unique loss function and imputation algorithm enhance the robustness and compatibility of CAED, with a 24% smaller accuracy drop than other methods when features are modified or unavailable. This study presents the CAED framework as an effective solution for protecting apps against real-world security threats in Android and IoT environments. © 2014 IEEE.

关键词： Crime

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Based Side-Channel Attack Detection for Mobile Devices security in 5G Networks

引用

清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

来源：评论

学校读者我要写书评

暂无评论

High-Secured Image LSB Steganography Using AVL-Tree with Random RGB Channel Substitution

引用

computers, Materials & Continua 2024年第10期81卷 183-211页

作者： Murad Njoum Rossilawati Sulaiman Zarina Shukur Faizan Qamar Center of Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Computer Science and Cyber Security Faculty of Engineering and Information TechnologyBirZeit UniversityBirZeitRamallahP.O.Box 14Palestine

Random pixel selection is one of the image steganography methods that has achieved significant success in enhancing the robustness of hidden *** property makes it difficult for steganalysts’powerful data extraction tools to detect the hidden data and ensures high-quality stego image ***,using a seed key to generate non-repeated sequential numbers takes a long time because it requires specific mathematical *** addition,these numbers may cluster in certain *** hidden data in these clustered pixels will reduce the image quality,which steganalysis tools can ***,this paper proposes a data structure that safeguards the steganographic model data and maintains the quality of the stego *** paper employs the AdelsonVelsky and Landis(AVL)tree data structure algorithm to implement the randomization pixel selection technique for data *** AVL tree algorithm provides several advantages for image ***,it ensures balanced tree structures,which leads to efficient data retrieval and insertion ***,the self-balancing nature of AVL trees minimizes clustering by maintaining an even distribution of pixels,thereby preserving the stego image *** data structure employs the pixel indicator technique for Red,Green,and Blue(RGB)channel *** green channel serves as the foundation for building a balanced binary ***,the sender identifies the colored cover image and secret *** sender will use the two least significant bits(2-LSB)of RGB channels to conceal the data’s size and associated *** next step is to create a balanced binary tree based on the green *** the channel pixel indicator on the LSB of the green channel,we can conceal bits in the 2-LSB of the red or blue *** first four levels of the data structure tree will mask the data size,while subsequent levels will conceal the remaining digits of secret *** embedding the bits i

关键词： Image steganography pixel random selection(PRS) AVL tree peak signal-to-noise ratio(PSNR) imperceptibility capacity

来源：评论

学校读者我要写书评

暂无评论

Literature Review on Packet Sniffing: Essential for cybersecurity & Network security 5

Literature Review on Packet Sniffing: Essential for Cybersec...

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Shaw, Ranju Parveen, Suraiya Jamia Hamdard University Dept. of Computer Science Engineering with Specialization in Cyber Forensic Information Security Delhi India Faculty at Jamia Hamdard University Sest Department of Computer Science Engineering Delhi India

ISBN: (纸本)9798350385649

In the realm of cybersecurity and network security, the significance of monitoring and analyzing network traffic cannot be overstated Network packet sniffing, a technique employed to capture and inspect data packets traversing a network, has emerged as an imperative tool for understanding network behavior, identifying vulnerabilities, and enhancing overall security. This study analyzes packet sniffing by exploring its role in modern cybersecurity practices. By analyzing packet sniffing mechanisms, including its methodologies and potential applications, this res earch sheds light on its effectiveness as a proactive security solution. Furthermore, the study examines the ethical considerations surrounding packet sniffing, addressing concerns related to user privacy and data protection. It also investigates the legal aspects that govern the use of packet sniffing techniques, ensuring compliance with regulations while maximizing its benefits. Through case studies and real-world examples, this research illustrates how packet sniffing can be leveraged to detect network anomalies, thwart malicious activities, and fortify network infrastructures. By presenting a comprehensive overview of packet sniffing's capabilities and limitations, this study equips cybersecurity practitioners with the knowledge needed to make informed decisions about its integration into their defensive strategies. © 2024 IEEE.

关键词： cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Identifying malicious traffic under concept drift based on intraclass consistency enhanced variational autoencoder

引用

science China(Information sciences) 2024年第8期67卷 238-252页

作者： Xiang LUO Chang LIU Gaopeng GOU Gang XIONG Zhen LI Binxing FANG Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Technology Harbin Institute of Technology (Shenzhen)

Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false ***, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector(ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training:intraclass consistency enhanced(ICE) representation learning and Class-Perception(CP) detector construction. In the first module, we employ a variational autoencoder(VAE) in conjunction with Kullback-Leibler(KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for nondrifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP det

关键词： concept drift malicious traffic identification variational autoencoder intrusion detection cyberspace security

来源：评论

学校读者我要写书评

暂无评论

Shorter ZK-SNARKs from square span programs over ideal lattices

引用

cybersecurity 2025年第1期8卷 208-226页

作者： Xi Lin Heyang Cao Feng‑Hao Liu Zhedong Wang Mingsheng Wang Key Laboratory of Cyberspace Security Defense Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina School of Electrical Engineering&Computer Science Washington State UniversityPullmanWAUSA School of Cyber Science and Engineering Shanghai Jiaotong UniversityShanghaiChina

Zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARKs)are cryptographic protocols that ofer efcient and privacy-preserving means of verifying NP language relations and have drawn considerable atten‑tion for their appealing applications,e.g.,verifable computation and anonymous payment *** with the pre-quantum case,the practicability of this primitive in the post-quantum setting is still unsatisfactory,espe‑cially for the space *** tackle this issue,this work seeks to enhance the efciency and compactness of lat‑tice-based zk-SNARKs,including proof length and common reference string(CRS)*** this paper,we develop the framework of square span program-based SNARKs and design new zk-SNARKs over cyclotomic *** with previous works,our construction is without parallel repetition and achieves shorter proof and CRS lengths than previous lattice-based zk-SNARK ***,the proof length of our scheme is around 23.3%smaller than the recent shortest lattice-based zk-SNARKs by Ishai et al.(in:Proceedings of the 2021 ACM SIGSAC conference on computer and communications security,pp 212-234,2021),and the CRS length is 3.6×*** constructions follow the framework of Gennaro et al.(in:Proceedings of the 2018 ACM SIGSAC conference on computer and com‑munications security,pp 556-573,2018),and adapt it to the ring setting by slightly modifying the knowledge *** develop concretely small constructions by using module-switching and key-switching procedures in a novel way.

关键词： Zk-SNARKs Post-quantum Succinct argument

来源：评论

学校读者我要写书评

暂无评论

GuardGrid: A Queriable and Privacy-Preserving Aggregation Scheme for Smart Grid via Function Encryption

引用

IEEE Internet of Things Journal 2025年第11期12卷 17622-17633页

作者： Yu, Pengfei Huang, Weicong Zhang, Ran Qian, Xinyuan Li, Hongwei Chen, Hanxiao China Electric Power Research Institute State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing210003 China University of Electronic Science and Technology of China School of Computer Science and Engineering School of Cyber Security Chengdu611731 China

Smart grids have revolutionized electricity management by leveraging real-time consumption data, enabling more efficient power control through advanced algorithms. However, this transformation raises significant privacy and security concerns due to the extensive collection of user data. Current solutions face challenges, such as aggregator gateway misbehavior, lack of support for function queries, and the need to balance privacy with efficiency. In this article, we propose FEHH, a novel scheme that ensures both privacy preservation and verifiable aggregation. It allows multiple aggregators to perform inner-product computations on encrypted data while safeguarding the aggregated results from the aggregator. Additionally, it supports verification of aggregated data’s correctness using Linear Homomorphic Hash. Building on FEHH, we introduce GuardGrid, a privacy-preserving aggregation scheme for smart grids that inherits FEHH’s core features and adds support for essential arithmetic operations necessary for function queries. This allows cloud servers to respond to queries from either the control center or users without compromising data confidentiality. Experimental results show that the encryption overhead of GuardGrid is only 7% of that of the PPDA scheme, and its communication overhead is 123x less. These results demonstrate that GuardGrid significantly reduces computation and communication costs, providing a more sustainable and cost-effective smart grid solution. © 2014 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

ArchSentry: Enhanced Android Malware Detection via Hierarchical Semantic Extraction

引用

IEEE Transactions on Network and Service Management 2025年第3期22卷 2822-2837页

作者： Wang, Tianbo Liu, Mengyao Li, Huacheng Zhao, Lei Jiang, Changnan Xia, Chunhe Cui, Baojiang Beihang University School of Cyber Science and Technology Beijing100191 China Beihang University School of Computer Science and Engineering Beijing China Wuhan University School of Cyber Science and Engineering Wuhan China Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing China

Android malware poses a significant challenge for mobile platforms. To evade detection, contemporary malware variants use API substitution or obfuscation techniques to hide malicious activities and mask their shallow semantic characteristics. However, existing research lacks analysis of the hierarchical semantic associated with Android apps. To address this problem, we propose ArchSentry, an enhanced Android malware detection via hierarchical semantic extraction. First, we select entities and their relationships relevant to Android software behavior through the software architecture and represent them using a heterogeneous graph. Then, we structure meta-paths to represent rich semantic information to achieve semantic enhancement and improve efficiency. Next, we design a meta-path semantic selection method based on KL Divergence to identify and eliminate redundant features. To achieve a comprehensive representation of the overall software semantics and improve performance, we construct a feature fusion approach based on Restricted Boltzmann Machines (RBM) and AutoEncoder (AE) during the pre-training phase, while preserving the probability distribution characteristics of various meta-paths. Finally, Deep Neural Networks (DNN) process fusion features for comprehensive feature sets. Experimental results on real-world application samples indicate that ArchSentry achieves a remarkable 99.2% detection rate for Android malware, with a low false positive rate below 1%. These results surpass the performance of current state-of-the-art approaches. © 2004-2012 IEEE.

关键词： Android malware

来源：评论

学校读者我要写书评

暂无评论

Verifiable and Forward-Secure Multi-Keyword Query in Internet of Medical Things

引用

IEEE Internet of Things Journal 2025年第13期12卷 23809-23822页

作者： Li, Hongjun He, Debiao Feng, Qi Luo, Min Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China National Key Laboratory of Security Communication Chengdu China

The Internet of Medical Things (IoMT) plays a pivotal role in modern healthcare systems, enhancing patients' medical experiences and improving the efficiency of public medical services. However, concerns regarding security and privacy may hinder the widespread implementation and development of IoMT in practical applications. Dynamic Searchable Symmetric Encryption (DSSE) can maintain search capabilities on encrypted data, even when files are dynamically added or deleted. Earlier DSSE schemes typically support only single keyword query and provide forward security under the assumption of semi-honest servers, which significantly limits their applicability in real-world scenarios. To resolve these limitations, we propose a forward-secure and verifiable DSSE scheme that supports multi-keyword conjunctive query. Our scheme ensures forward security by utilizing a chain structure and guarantees correctness and completeness through RSA accumulator and Homomorphic Message Authentication Code (HMAC). Furthermore, we integrate the forward index and inverted index to enable efficient retrieval. Through comprehensive security and performance analysis, we demonstrate that our scheme effectively protects users' privacy while maintaining low computation and communication overheads. Finally, some experimental evaluations are conducted to verify both the correctness and efficiency of the proposed scheme. © 2014 IEEE.

关键词： Structured Query Language

来源：评论

学校读者我要写书评

暂无评论

Energy Efficiency Maximization for Cooperative NOMA with Hardware Impairments

引用

China Communications 2024年第12期21卷 80-91页

作者： Wang Zhengqiang Chang Ruifei Wan Xiaoyu Fan Zifu Duo Bin School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer Science and Cyber Security Chengdu University of TechnologyChengdu 610059China

The massive connectivity and limited energy pose significant challenges to deploy the enormous devices in energy-efficient and environmentally friendly in the Internet of Things(IoT).Motivated by these challenges,this paper investigates the energy efficiency(EE)maximization problem for downlink cooperative non-orthogonal multiple access(C-NOMA)systems with hardware impairments(HIs).The base station(BS)communicates with several users via a half-duplex(HD)amplified-and-forward(AF)***,we formulate the EE maximization problem of the system under HIs by jointly optimizing transmit power and power allocated coefficient(PAC)at BS,and transmit power at the *** original EE maximization problem is a non-convex problem,which is challenging to give the optimal solution ***,we use fractional programming to convert the EE maximization problem as a series of subtraction form ***,variable substitution and block coordinate descent(BCD)method are used to handle the ***,a resource allocation algorithm is proposed to maximize the EE of the ***,simulation results show that the proposed algorithm outperforms the downlink cooperative orthogonal multiple access(C-OMA)scheme.

关键词： block coordinate descent cooperative non-orthogonal multiple access energy efficiency hardware impairments resource allocation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：