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12th International Symposium on Digital Forensics and security, ISDFS 2024

作者： Rehman, Fazalur Hashmi, Joudat Abdullah, Moiz Zaman, Hafsah School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan College of Engineering Georgia Tech University Department of Analytics United States Air University Faculty of Computing and Artificial Intelligence Department of Cyber Security Islamabad Pakistan

ISBN: (纸本)9798350330366

The evolution of technologies in the last decades has transformed the whole human interaction experience with the digital world. Integrated into users' home voice assistant devices has rationalized personal interaction with the technology however convenience of intelligent voice assistant presents security and privacy risks. This paper investigates the security vulnerabilities of smart voice assistant devices specifically focusing on Google Home Mini. Through a series of simulated attacks and comprehensive analysis, this study uncovers potential risks ranging from unauthorized access to user data privacy compromise within home networks. The objective is to raise awareness about these vulnerabilities and their implication urging the need for robust security measures and providing valuable observation for future research. As voice devices are becoming an integral part of daily life addressing these challenges is paramount to ensuring user privacy data integrity and overall security of the smart home ecosystem. © 2024 IEEE.

关键词： Risk assessment

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The mass,fake news,and cognition security

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Frontiers of computer science 2021年第3期15卷 1-13页

作者： Bin GUO Yasan DING Yueheng SUN Shuai MA Ke LI Zhiwen YU School of Computer Science Northwestern Polytechnical UniversityXi'an 710072China School of Cyber Security Tianjin UniversityTianjin 300350China School of Computer Science and Engineering Beihang UniversityBeijing 100191China

The widespread fake news in social networks is posing threats to social stability,economic development,and political democracy,*** studies have explored the effective detection approaches of online fake news,while few works study the intrinsic propagation and cognition mechanisms of fake *** the development of cognitive science paves a promising way for the prevention of fake news,we present a new research area called Cognition security(CogSec),which studies the potential impacts of fake news on human cognition,ranging from misperception,untrusted knowledge acquisition,targeted opinion/attitude formation,to biased decision making,and investigates the effective ways for fake news *** is a multidisciplinary research field that leverages the knowledge from social science,psychology,cognition science,neuroscience,AI and computer *** first propose related definitions to characterize CogSec and review the literature *** further investigate the key research challenges and techniques of CogSec,including human-content cognition mechanism,social influence and opinion dif-fusion,fake news detection,and malicious bot ***,we summarize the open issues and future research directions,such as the cognition mechanism of fake news,influence maximization of fact-checking information,early detection of fake news,fast refutation of fake news,and so on.

关键词： cyberspace cognition security fake news crowd computing human-content interaction

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Neuroadaptive Incentivization in Healthcare using Blockchain and IoT

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SN computer science 2024年第1期5卷 1-12页

作者： Kumar, Ajay Singh, Rajiv Ranjan Chatterjee, Indranath Sharma, Nikita Rana, Vaishali Department of Computer Science and Informatics Central University of Himachal Pradesh Himachal Pradesh Dharamshala India Department of Cyber Security and Networks Glasgow Caledonian University Glasgow Scotland United Kingdom Department of Computer Engineering Tongmyong University Busan South Korea School of Technology Woxsen University Hyderabad India

Financially incentivizing health-related behaviors can improve health record outcomes and reduce healthcare costs. Blockchain and IoT technologies can be used to develop safe and transparent incentive schemes in healthcare. IoT devices, such as body sensor networks and wearable sensors, etc. connect the physical and digital world making it easier to collect useful health-related data for further analysis. There are, however, many security and privacy issues with the use of IoT. Some of these IoT security issues can be alleviated using Blockchain technology. Incorporating neuroadaptive technology can result in more personalized and effective therapies using machine learning algorithms and real-time feedback. The research investigates the possibilities of neuroadaptive incentivization in healthcare using Blockchain and IoT on patient health records. The core idea is to incentivize patients to keep their health parameters within standard range thereby reducing the load on healthcare system. In summary, we have presented a proof of concept for neuroadaptive incentivization in healthcare using Blockchain and IoT and discuss various applications and implementation challenges. © 2023, The Author(s).

关键词： Blockchain Healthcare IoT Neuroadaptive Smart contracts

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural Networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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security and Threat Detection through Cloud-Based Wazuh Deployment 1

Security and Threat Detection through Cloud-Based Wazuh Depl...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Moiz, Syed Majid, Abdul Basit, Abdul Ebrahim, Mansoor Abro, Abdul Ahad Naeem, Mehwish Iqra University Dept. of Computer Science Karachi Pakistan Dept. of Cyber Security Karachi Pakistan Indus University Dept. of Computer Science Karachi Pakistan Iqra University Dept. of Software Engineering Karachi Pakistan University Libre de Bruxelles Brussels Belgium

ISBN: (纸本)9798350373042

In today's interconnected business landscape, security is the cornerstone of all organizations, irrespective of their size or industry. This research emphasizes the critical need to safeguard organizational assets from cyber threats and highlights the pivotal role that firewalls play in fortifying network and cyber systems. The challenge lies in maintaining updated firewall defenses to prevent server downtime and the circumvention of security measures. To address these concerns, this study introduces a novel approach by integrating Cloud Wazuh, a user-friendly solution tailored for non-technical personnel, providing streamlined deployment and customizable security rules. Leveraging Wazuh suite of tools, particularly the Host-Based Intrusion Detection System (HIDS), the research detects and mitigates potential threats, including patterns associated with attacks such as Port Scanning, Denial of Service (DoS), and Metasploit. Additionally, Wazuh offers a range of features, from security Analytics to File Integrity Monitoring, to comprehensively protect against cyber threats. While implementing Cloud Wazuh may entail initial costs, it proves cost-effective compared to maintaining a dedicated IT security team. This research underscores how Cloud Wazuh deployment serves as a pivotal tool for organizations looking to strengthen their security measures, safeguard their data, and ensure uninterrupted operations in an increasingly digital and interconnected world. © 2024 IEEE.

关键词： cybersecurity

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TransRPRT: A Knowledge Representation Learning Model Based on Relationship Transfer and Relationship Mapping Properties

TransRPRT: A Knowledge Representation Learning Model Based o...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Yanjun Guo Junlin Zhou School of Computer Science and Engineering(School of Cyber Security) University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Representing learning models can vectorize entities and relationships through vectorization, and can learn the attributes of entities to complete the knowledge graph, which has become a current research hotspot. Some representation learning models rely on the translation rule of TransE. The performance of these models is not ideal when inferring symmetric, reflexive, and synthetic relationship patterns, as well as complex relationships of 1-N, N-1, and N-N relationships simultaneously. For this purpose, we have proposed a knowledge representation learning model based on relationship transfer and relationship mapping attributes, namely TransRPRT. The core idea of the model is to first use the idea of hyperplane projection to embed entities and relationships; Secondly, adding relationship mapping attributes to enhance the model's ability to handle complex relationships; Then, this article proposes a relationship transfer mechanism in order to weaken translation rules; Finally, use probability method to complete the training task. Experiments on five public datasets, FBl5K237, WN18RR, WN11, FB13, and FB15K, have shown that our model has superior performance in link prediction and triplet classification tasks compared to state-of-the-art baselines, verifying the effectiveness of this model.

关键词： Training Representation learning Accuracy Computational modeling Knowledge graphs Predictive models Signal processing

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Optimizing Diagnostic Accuracy in Ultrasound Imaging Through Cloud-Enabled Deep Learning Techniques 4

Optimizing Diagnostic Accuracy in Ultrasound Imaging Through...

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4th International Conference on Soft Computing for security Applications, ICSCSA 2024

作者： Kalpana, V. Girija, M. Tharageswari, K. Yobu, D. Revathi, J. Aruna, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Jeppiaar University School of Engineering and Technology Chennai India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Computer Science and Engineering Cyber Security J.J College of Engineering and Technology Tricy India Department of Mathematics Kongu Engineering College Erode India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331515720

This research aims to study predicting and optimizing the diagnostic accuracy by cloud-enabled deep learning techniques in ultrasound imaging for kidney disease diagnosis. The main findings are that CNN gives 92% accurate outcomes in terms of sensitivity, precision, specificity, AUC-ROC, and other terms used for predictive outcomes in the current research. The other outcomes are 88% accuracy and 91% specificity in predicting kidney diseases by using the RNN model. The other two models used for learning are transfer learning and federated learning which also provide good outcomes which are 94% accurate and 91% accurately in diagnosing with the above-said models. These learning approaches optimize or increase the accuracy rates such as 93% by the transfer learning approach and 90% accuracy outcomes by the federated learning mechanism. These above-given models have been taken based on advanced technologies that are used to predict or forecast the diseases through learning processes. The success of these learning mechanisms is explained by the reliable processes used in the learning of the models that are possible by the neural network used in the learning mechanisms. The high accuracy rates of the models show the successfulness of such clinics, who are in rural areas and are unable to meet their necessary outcome-oriented goals, then measures can be taken for running tele-clinics using the above models of learning techniques to diagnose the diseases at the correct time by 'n' number of people in rural areas. Mine is one of them working as a health worker for which measures need to be taken for finding an accurate solution that operates on cloud-enabled deep learning. This model help run family life and be able to diagnose certain conditions at desired rural clinics out of the above learning models. These tools of learning are studied on cloud-based deep learning techniques and performed well in diagnosing kidney diseases on rural tele-medicine and these learning techni

关键词： Ultrasonic imaging

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Blockchain-Enabled DNS: Enhancing security and Mitigating Attacks in Domain Name Systems 6

Blockchain-Enabled DNS: Enhancing Security and Mitigating At...

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6th International Conference on Signal Processing and Information security, ICSPIS 2023

作者： Shah, Kaushal Padhya, Mukti Sharma, Sachin Pandit DeenDayal Energy University School of Technology Department of Computer Science and Engineering Gandhinagar India National Forensic Sciences University School of Cyber Security and Digital Forensics Gandhinagar India

ISBN: (纸本)9798350329599

The importance of Internet security is increasing as businesses expand through digitalization. Like in the physical world, data transmitted over the Internet must be safeguarded to ensure confidentiality, integrity, and availability. Among the various security threats, Distributed Denial-of-Service (DDoS) attacks are widely recognized as they can potentially compromise all three aspects of data security. Despite several attempts to address these attacks, the traditional Domain Name System (DNS) has struggled to counter them effectively. In this research, we propose the concept of a Blockchain-based DNS as a solution to mitigate DNS-related threats, particularly DDoS attacks. The suggested architecture encompasses all levels of the DNS, including registrars, intermediaries (if any), and clients. By leveraging the decentralized nature of blockchain technology, this approach provides a fundamental defense against DDoS attacks, as it eliminates the vulnerability of a single point of failure. Furthermore, this work explores the appropriate type of blockchain to be used, estimates the cost of implementing such a service, and offers flexibility for organizations to adopt this solution at their discretion. © 2023 IEEE.

关键词： Blockchain

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Forward and Backward Private Searchable Symmetric Encryption with Efficient Updates 24

Forward and Backward Private Searchable Symmetric Encryption...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Yin, Zhiming Li, Xiong Choo, Kim-Kwang Raymond Liu, Yuzhen Liang, Wei School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249-0631 United States

ISBN: (纸本)9798350319934

In dynamic searchable symmetric encryption, clients can search and update encrypted data stored on the server (assumed to be semi-honest). In such schemes, forward privacy guarantees that prior queries are not related to subsequent updated entries, while backward privacy ensures that the previously deleted files are not retrievable from the subsequent queries. However, most of the existing schemes have poor update efficiency, for example incurring significant computational and communication overheads since the use of the complex structures and cryptographic primitives used. Hence, we propose a Forward and Backward private searchable symmetric encryption scheme with Efficient uPdates, called FBEP. Specifically, FBEP achieves efficient updates based on Bloom filters and lightweight symmetric cryptographic primitives. It enables the client to remove file identifiers without interacting with the server and insert them efficiently, while maintaining high search efficiency. We precisely define the leakage of FBEP, and demonstrate that FBEP supports forward and Type-II backward privacy with formal security analysis. Besides, the experiments show that FBEP is much better than other related schemes in terms of comprehensive performance. Specially, the update efficiency of FBEP outperforms at least 1.6×, 25×, 525× than Mitra, SDd and Janus ++, respectively, while the search time is at least 2×, 702× faster than Mitra and Janus ++, respectively. © 2022 IEEE.

关键词： Efficiency

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OntoCyrene: Towards Ontology-Enhanced Asset Modelling for Supply Chains in the Context of cyber security 1

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8th Workshop on the security of Industrial Control Systems and of cyber-Physical Systems, cyberICPS 2022, 6th International Workshop on security and Privacy Requirements engineering, SECPRE 2022, 4th Workshop on security, Privacy, Organizations, and Systems engineering, SPOSE 2022, 3rd cyber-Physical security for Critical Infrastructures Protection, CPS4CIP 2022, 2nd International Workshop on cyber Defence Technologies and Secure Communications at the Network Edge, CDT and SECOMANE 2022, 1st International Workshop on Election Infrastructure security, EIS 2022 and 1st International Workshop on System security Assurance, SecAssure 2022 held in conjunction with 27th European Symposium on Research in computer security, ESORICS 2022

作者： Heydari, Mohammad Mouratidis, Haralambos Tafreshi, Vahid Heydari Fami Cyber Security Researcher at Stockholm University Stockholm Sweden School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Staffordshire University London London United Kingdom

ISBN: (数字)9783031254604

ISBN: (纸本)9783031254598

A Supply chain in the era of the Internet of Industrial Things faces new challenges in terms of modelling. The challenges stem from a number of characteristics like scalability, dependency and dynamism. In this paper, we introduce an ontology-enhanced method for modelling assets and their dependency in the context of supply chains. This method enables us to infer new insights from the domain. It also provides a dynamic knowledge representation and reasoning by capturing all aspects of supply chains from three different perspectives including business, asset, and sector. The results show that the proposed method can address the challenges by utilizing ontology and synching three relevant perspectives. Moreover, the developed ontology (OntoCyrene) is rich enough to bring light to the dark angles of the modelled scenarios. The theme chosen for this work is cyber security and we used real-world scenarios derived from the Cyrene (Cyrene EU H2020 Project is available at: https://***.) project to populate and evaluate the ontology. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Supply chains

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