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Endogenous security of FQn Networks: Adaptive System-Level Fault Self-Diagnosis

IEEE Transactions on Reliability 2024年第3期73卷 1659-1668页

作者： Lin, Yuhang Lin, Limei Huang, Yanze Xu, Li Hsieh, Sun-Yuan Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

Endogenous security has the ability to discover, eliminate, and solve internal security problems and hidden dangers within the network, and is a superior technology to ensure future network security. The t/k-diagnosis strategy, as a strong and adaptive self-diagnosis strategy, is an important part for ensuring endogenous security. Moreover, the folded hypercube (FQn) as a data transmission network (e.g., optical network) topology offers new potential for the construction of large-scale, high data throughput, and low-latency systems, such as computing network, human-cyber-physical systems, and smart grid. However, there are few studies on endogenous security based on FQn networks. Therefore, this article designs an adaptive system-level fault self-diagnosis strategy, namely Fast t/k-Diagnosis Under Maeng-Malek Model (Ftk-DIAG-MM*) to diagnosis the faulty vertices in FQn network under the Maeng-Malek model (MM∗ mod). Then, we provide a proof of the algorithm correctness theoretically by the fault tolerance of FQn network. It is derived by theoretical derivation that the t/k-diagnosability inherent to the FQn network is (n+1)(k+1)-k(k+3)/2. The simulation experiments demonstrate that the designed Ftk-DIAG-MM∗ strategy can correctly diagnose all vertices within the range allowed by the diagnosability, and still has a great performance when it exceeds the range allowed by the diagnosability. It greatly enhances the fault diagnosis capability of FQn network in the circumstance of misdiagnosing a few vertices, which provides an important theoretical basis for the reliability and endogenous security of FQn networks. © 1963-2012 IEEE.

关键词： Topology

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Electromagnetic Radiation Effects in Human and Animal Health 14

Electromagnetic Radiation Effects in Human and Animal Health

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Singh, Jagjot Kumar, Sanu Johnson, Derick Mathew Savita Department of Computer Science Engineering Spl. Digital Forensics and Cyber Security India

Electromagnetic (EM) radiations are the waves of electromagnetic and magnetic field which are generated from electromagnetic devices. These radiations are dangerous to humans as well as animal health. Talking medically, in the human and animal body, these bioelectric signals cause different processes to occur. Exposing to larger EMradiations can cause disrupt in the natural work of the human body. These harmful radiations harm the body at the cellular level. According to the World Health Organization (WHO), the EMF radiations released by mobile phones and otherdevices are hazardous to humans. The objective of this review paper is to carry out a survey to know about the usage of mobile phones and laptops by people and will aware about the harmful impact of them. © Grenze Scientific Society, 2023.

关键词： Cellular telephones

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Exploring the landscape of network security: a comparative analysis of attack detection strategies

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3211-3228页

作者： Rajesh Kanna, P. Santhi, P. Department of Computer Science and Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India

The field of computer networking is experiencing rapid growth, accompanied by the swift advancement of internet tools. As a result, people are becoming more aware of the importance of network security. One of the primary concerns in ensuring security is the authority over domains, and network owners are striving to establish a common language to exchange security information and respond quickly to emerging threats. Given the increasing prevalence of various types of attacks, network security has become a significant challenge in the realm of computing. To address this, a multi-level distributed approach incorporating vulnerability identification, dimensioning, and countermeasures based on attack graphs has been developed. Implementing reconfigurable virtual systems as countermeasures significantly improves attack detection and mitigates the impact of attacks. Password-based authentication, for instance, can be susceptible to password cracking techniques, social engineering attacks, or data breaches that expose user credentials. Similarly, ensuring privacy during data transmission through encryption helps protect data from unauthorized access, but it does not guarantee the prevention of other types of attacks such as malware infiltration or insider threats. This research explores various techniques to achieve effective attack detection. Multiple research methods have been utilized and evaluated to identify the most suitable approach for network security and attack detection in the context of cloud computing. The analysis and implementation of diverse research studies demonstrate that the based signature intrusion detection method outperforms others in terms of precision, recall, F-measure, accuracy, reliability, and time complexity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Network security

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Adapting to Evasive Tactics through Resilient Adversarial Machine Learning for Malware Detection 18

Adapting to Evasive Tactics through Resilient Adversarial Ma...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Krishna, G. Bala Sravan Kumar, G. Ramachandra, Mummadi Sampurnima Pattem, K. Rani, D. Sandhya Kakarla, Geeta Cvr College of Engineering Department of Computer Science Engg. Rangareddy Ibrahimpatnam India Cvr College of Engineering Department of Computer Science Engg.-Cyber Security Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Hyderabad India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

This paper presents the Adaptive Resilience-based Convolutional Network (ARCNet), a sophisticated machine learning framework specifically designed to detect advanced, evasive malware. ARCNet combines convolutional and recurrent neural networks, making it highly adaptable to changing cyber threats. Its core components, the Adversarial Learning Module (ALM), Predictive Analysis Engine (PAE), and Dynamic Adaptation System (DAS), significantly boost its detection power. Tests using a synthetic dataset show ARCNet's superiority over traditional models like the Support Vector Machine (SVM). It achieved 95.2% accuracy under normal conditions (compared to SVM's 89.4%) and maintained 92.5% accuracy even during adversarial attacks (against SVM's 80.3%). Notably, ARCNet's detection rates improved from 78.5% to 86.7% in five months after integrating the DAS. These results confirm ARCNet's efficiency in tackling complex malware challenges, contributing greatly to cybersecurity. The study underscores the importance of evolving and enhancing machine learning methods to keep pace with the rapidly changing landscape of cyber threats. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Block chain Blockchains Challenges of Block chain Consensus algorithm Data privacy Digital signatures Distributed ledger Faces Organizations Smart contracts

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IDCE:Integrated Data Compression and Encryption for Enhanced security and Efficiency

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computer Modeling in engineering & sciences 2025年第4期143卷 1029-1048页

作者： Muhammad Usama Arshad Aziz Suliman A.Alsuhibany Imtiaz Hassan Farrukh Yuldashev Department of Cyber Security Pakistan Navy Engineering CollegeNational University of Sciences and TechnologyKarachi75350Pakistan Department of Computer Science Main CampusIqra UniversityKarachi75500Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100170Uzbekistan

Data compression plays a vital role in datamanagement and information theory by reducing ***,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and *** the exponential growth of digital data,robust security measures are *** encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key *** their individual benefits,both require significant computational ***,performing them separately for the same data increases complexity and processing *** the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and *** on 128-bit block sizes and a 256-bit secret key *** combines Huffman coding for compression and a Tent map for ***,an iterative Arnold cat map further enhances cryptographic confusion *** analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.

关键词： Chaotic maps security data compression data encryption integrated compression and encryption

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Leveraging OpenStack-based Private Cloud for Intrusion Detection Using Deep Learning Techniques 1

Leveraging OpenStack-based Private Cloud for Intrusion Detec...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Roshan, Rahul G. Salanke, Vishal S Nagasundari, S. Research Center for Information Security Forensics and Cyber Resilience PES University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331518943

OpenStack is a cloud platform that offers infrastructure services, providing access to computing, storage, and network resources. However, due to its open-source nature, the cloud environment can be vulnerable to security threats. OpenStack security measures are generally applied to specific services, rather than being integrated comprehensively across the entire cloud environment. Despite offering flexibility and scalability, the widespread use of OpenStack calls for robust security solutions. Current intrusion detection systems often struggle to adapt to constantly evolving traffic patterns and attack vectors in the OpenStack environment. This study introduces a two-stage deep learning framework to create a robust Intrusion Detection System (IDS) for OpenStack-based private clouds. The proposed framework efficiently analyzes network traffic data in these clouds by implementing dimensionality reduction and anomaly detection techniques. It utilizes a network-based IDS, trained on two publicly available datasets, which enables a broader detection range of known attack patterns. The results indicate that the OpenStack IDS significantly enhances OpenStack's security, addressing the limitations of generic IDS solutions. © 2024 IEEE.

关键词： Network intrusion

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Electronic Medical Records Management and Administration: Current Trends, Issues, Solutions, and Future Directions

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SN computer science 2024年第5期5卷 460页

作者： Abdulkadir, Umar Waziri, Victor Onomza Alhassan, John Kolo Ismaila, Idris Department of Cyber Security Science Federal University of Technology Minna Nigeria Department of Computer Science Federal University of Technology Minna Nigeria

Electronic Medical Records (EMR) is often used to refer to as electronic personal health (EPH) records or electronic healthcare records (EHR). These are considered vivacious assets of health facilities and patients. The relevance of the EMRs has motivated diverse innovations in the collecting, organizing, managing and administering for purpose of treatment primarily and other reasons. There are various concerns raised about legitimate usages, reproducibility, accuracy, and privacy breaches of EMRs across majority of health and medical facilities globally. This phenomenon became popular due to its support of electronic devices, wireless links, transmission and storage of data in the cloud, and communication across a gateway (or central-point). Therefore, physicians and medical facilities were empowered to undertake virtual consultations to patients through telemedicine applications for the remote treatment and diagnosis. Internet of Things (IoT) systems, medical wearable objects, and sensors were the basic components that collect and transmit patient data on real-time basis to a base station or centralized servers managed by hospitals. However, there is the need to improve creation and adoption of EMRs not without understanding their roles and shortfalls as well possible means of improvement. Consequently, this study conducts a systematic literature review on electronic medical records management and administration under current trends, issues, solutions and future directions. To this end, eighty-one (81) peer-reviewed articles including conferences and journals papers were included in the final stage of the study after applying Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRIMA-P) methodology of records selection. The contributions of this study include: the recognition of the informed consent strategy as the best solution to illegitimate access to patient electronic records;the use of permissioned access strategies provided by blockc

关键词： Access permission scheme Data administration Medical records Privacy security

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AttrLeaks on the Edge: Exploiting Information Leakage from Privacy-Preserving Co-inference

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Chinese Journal of Electronics 2023年第1期32卷 1-12页

作者： WANG Zhibo LIU Kaixin HU Jiahui REN Ju GUO Hengchang YUAN Wei School of Cyber Science and Engineering Wuhan University School of Cyber Science and Technology Zhejiang University Department of Computer Science and Technology Tsinghua University

Collaborative inference(co-inference) accelerates deep neural network inference via extracting representations at the device and making predictions at the edge server, which however might disclose the sensitive information about private attributes of users(e.g.,race). Although many privacy-preserving mechanisms on co-inference have been proposed to eliminate privacy concerns, privacy leakage of sensitive attributes might still happen during inference. In this paper, we explore privacy leakage against the privacy-preserving co-inference by decoding the uploaded representations into a vulnerable form. We propose a novel attack framework named AttrL eaks, which consists of the shadow model of feature extractor(FE), the susceptibility reconstruction decoder,and the private attribute classifier. Based on our observation that values in inner layers of FE(internal representation) are more sensitive to attack, the shadow model is proposed to simulate the FE of the victim in the blackbox scenario and generates the internal ***, the susceptibility reconstruction decoder is designed to transform the uploaded representations of the victim into the vulnerable form, which enables the malicious classifier to easily predict the private attributes. Extensive experimental results demonstrate that AttrLeaks outperforms the state of the art in terms of attack success rate.

关键词： Deep learning Privacy Collaboration Transforms Feature extraction Prediction algorithms Iron

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Blockchain-Empowered Asynchronous Federated Reinforcement Learning for IoT-Based Traffic Trajectory Prediction

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IEEE Internet of Things Journal 2025年第11期12卷 17095-17109页

作者： Wang, Bin Tian, Zhao Tang, Fengxiao Pan, Heng She, Wei Liu, Wei Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450002 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China Central South University School of Computer Science and Engineering Changsha410083 China Zhongyuan University of Technology School of Cyber Space Security Zhengzhou450007 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou Key Laboratory of Blockchain and Data Intelligence Zhengzhou450002 China

Vehicle trajectory prediction plays a crucial role in IoT-based intelligent transportation systems, which can effectively address key issues, such as driving safety and multivehicle collaboration. However, the sensitivity of trajectory data and the reluctance of data holders to share it constrain the prediction model’s ability to capture vehicle behavior patterns in different scenarios. To address the above problems, we propose a blockchain-enabled asynchronous federated proximal policy optimization framework (BE-AFPPO) for the trajectory prediction of self-driving vehicles. First, we propose a curiosity proximal policy optimization (C-PPO) algorithm. The method utilizes a driven exploration strategy to actively motivate the intelligent agent to explore the unknown state space. The avoidance policy model reaches a local optimum when processing trajectory data. In addition, we design historical gated recurrent unit (GRU) and future GRU as input layers. The target’s historical motion features and future trajectory features are extracted, respectively. Then, various data is received through asynchronous federated learning. This model can fully learn the vehicle’s behavior patterns in different scenarios, which improves prediction accuracy. Based on this, we develop a blockchain-based dynamic group practical Byzantine fault tolerance (DG-PBFT) consensus algorithm. This enhances the credibility and integrity of the data while enriching the sources of trajectory data. Finally, we perform the experiments on the publicly available dataset nuScenes. The results demonstrate that the proposed method improves the robustness and accuracy of trajectory prediction. © 2014 IEEE.

关键词： Federated learning

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