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A Shared Blockchain Intelligent Public Protection Service Framework 1

A Shared Blockchain Intelligent Public Protection Service Fr...

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1st international conference on advances in Computational science and Engineering, ICACSE 2020

作者： Reddy, Gangireddy Prabhakar Kalaiselvi Geetha, M. Sureshanand, M. Jaanaa Rubavathy, S. Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Telangana Secunderabad India Department of Computer Science and Engineering Annamalai University Tamil Nadu Chidambaram India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

With accelerated technical advancements in the IoT network, an intelligent scheme of public security is possible with the incorporation of heterogeneous data processing systems, with which public security services will collaboratively be provided. While this system design has always been pursued by IoT and Cyber internal environments, the provision of scalable and flexible IoT-based PLC frames in a legacy system is challenging. Traditional security techniques are sometimes founded on a tall structure which can be an in efficiency or a point let-down. Enthused through micro services also blockchain technologies, this article offers the Smart Public Security Architecture of Blockchain-Enabled Decentralized Micro services. A micro service-based authentication framework is implemented within a licence blockchain network for safe monitoring of data access in a PLC environment. Such functionality is divided into a single, intelligently organised package micro service which is dispersed at fog and edge node computing. A comprehensive experimental study found that the Blend MAS proposed would provide decentralised, scalable and secure intelligence gathering and access controls to the purchaser of IoT based PLC systems. © Published under licence by IOP Publishing Ltd.

关键词： Blockchain

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A Methods of Medical Image Security Using Visual-Cryptography Techniques

A Methods of Medical Image Security Using Visual-Cryptograph...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Pallavi R. Waghmare Jaishri. M. Waghmare Dept. of Computer Science & Engg. S. G. G. S. IE & T Nanded India

ISBN: (纸本)9781665484527

Today in this technology era, most of the information is exchanged across open channels, and the security of such information has been a major concern. Medical Image Security has emerged as a new modern study subject as a result of the development of digital health-care data systems. To address medical picture security, the majority of researchers have employed image watermarking and image encryption. Though there are various other methods are also used for securing information in digital images such as steganography, digital signing, the Visual cryptography techniques is still popular among researcher for information security. The Visual Cryptography Scheme has the unique virtue of being able to decode visually the secure image by superimposing shares without the need for computation. The public key encryption approach is also used to make the visual cryptography image sharing more safe. In this article we carried the review of all these information hiding methods, types and technologies available in the literature along with their need and challenges for implementation.

关键词： Visualization Steganography Privacy Public key Watermarking Robustness Encryption

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Optimal Analysis for Enterprise Financial Management Based on Artificial Intelligence and Parallel computing Method

Optimal Analysis for Enterprise Financial Management Based o...

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international conference on Contemporary computing and Informatics (IC3I)

作者： M. Rajeswari N. Kumar P. Raman Pramod Kumar Patjoshi Vaishali Singh Sumit Pundir Department of Management Studies SRM Institute of Science and Technology Vadapalani Chennai TamilNadu College of Management SRM Institute of Science and Technology Chennai Kattankulathur TamilNadu Department of MBA Panimalar Engineering College Chennai TamilNadu India School of Management Centurion University of Technology and Management Bhubaneswar Odisha Department of Computer Science and Engineering Ambalika Institute of Management & Technology Luknow Uttar Pradesh India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India

To function in today’s society, people need access to information technology for every aspect of their lives, from personal interactions to the creation of goods and services. As the Internet and the In tandem with the expansion of the country’s information industry, an increasing number of businesses are opting to use an Automation of Financial data Management with AI. Artificial intelligence (AI) is a new technological science that aims to mimic, extend, and increase human intellect via the study and development of ideas, methodologies, technologies, and application systems. Artificial intelligence (AI), which may be seen as a method of teaching computers to replicate and simulate human thinking patterns and behaviours, has been the primary focus of data processing in recent years. To better grasp the complexity of many challenges in the financial services industry, AI has become a significant solution for creating accuracy and accountability. This paper begins with a discussion of basic technology’s blueprint and shape, optimises it, investigates the real-world requirements of SMEs in terms of financial management, concludes with system architecture and development architecture, and finally concludes with performing hypothetical tests testing the system’s functional performance. The test results show that the system accomplishes its intended purposes and performs its primary operations, but that it still needs some tweaks and additions. Because of this, it is very important that all the information used in managing money is exact, correct, and reliable.

关键词： Computers Shape Systems architecture Production Financial management Software Reliability

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Analyzing the Behavioral Artifacts for Malicious Software Detection Using Machine Learning Algorithms

Analyzing the Behavioral Artifacts for Malicious Software De...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Nikkam Chandini C. Kiran Mai Department of Computer Science and Engineering VNRVJIET Hyderabad India

Malicious software intentionally affects your computer system. Malware is analyzed using static or dynamic analysis techniques. These techniques are used to classify and predict unique patterns for correctly identifying malware. Over the last decade, many malware detection techniques have been proposed using a variety of techniques. In most cases, network-based attacks such as DDoS attacks are known network security risks. These causes usually cause network devices to overflow with more requests than they can handle, preventing the server from responding to legitimate requests. The biggest security risk is related to software. Software attacks can exploit the entire system, steal information, modify data, deny services, and endanger or damage devices. In particular, I am interested in dynamic analysis for developing malware detection systems that utilize machine learning techniques. This document proposes behavior-based malware detection techniques. To develop this model, we arrange a dynamic analysis approach and run malware samples using classifiers. To extracted for the malware detection system. To develop this technique, we create a dynamic analysis approach and run malware using classification algorithms. Computer programs connected to Internet are increasingly needed to analyze and prevent malware. The software exploits system vulnerabilities to unknowingly steal sensitive information and secretly send it to an attacker-controlled remote server. This approach uses the ML classifier to detect malware and identify the type of malware attacked in the dataset.

关键词： Machine learning algorithms Computer hacking Heuristic algorithms Software algorithms Prediction algorithms Malware Classification algorithms

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Location-aware Task Offloading in Mobile Edge computing

Location-aware Task Offloading in Mobile Edge Computing

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IEEE international conference on Big data and Cloud computing (BdCloud)

作者： Yongqiang Gao Jixiao Li College of Computer Science Inner Mongolia University Huhhot China

As one of the core technologies of 5G, mobile edge computing (MEC) has attracted wide attention from the academic community in recent years. In the mobile edge computing environment, users can choose to offload tasks to edge servers that are closer to the user. But during task processing, the user is likely to move out of the coverage of the edge server currently serving him, which will result in additional transmission delay. Therefore, achieving low task processing delay becomes increasingly challenging due to user mobility in mobile edge computing environment. In this paper, we aim to address this challenge and propose a location-aware task offloading strategy based on deep reinforcement learning algorithm in a mobile edge computing environment. The proposed strategy jointly considers user mobility and allocation of bandwidth and computing resources to minimize the average delay of tasks completion. Experimental results show that our method reduces the average delay of tasks completion by about 10% compared to algorithms that do not predict user motion trajectories.

关键词： Deep learning Multi-access edge computing Reinforcement learning Prediction algorithms User experience Delays Trajectory

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Towards intrusion detection of previously unknown network attacks 35

Towards intrusion detection of previously unknown network at...

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35th ECMS international conference on Modelling and Simulation, ECMS 2021

作者： Alzubi, Saif Stahl, Frederic Gaber, Mohamed Medhat Department of Computer Science University of Reading Reading United Kingdom Oldenburg Germany School of Computing and Digital Technology Birmingham City University United Kingdom Faculty of Computer Science and Engineering Galala University Galala City43511 Egypt

advances in telecommunication network technologies have led to an ever more interconnected world. Accordingly, the types of threats and attacks to intrude or disable such networks or portions of it are continuing to develop likewise. Thus, there is a need to detect previously unknown attack types. Supervised techniques are not suitable to detect previously not encountered attack types. This paper presents a new ensemble-based Unknown Network Attack Detector (UNAD) system. UNAD proposes a training workflow composed of heterogeneous and unsupervised anomaly detection techniques, trains on attack-free data and can distinguish normal network flow from (previously unknown) attacks. This scenario is more realistic for detecting previously unknown attacks than supervised approaches and is evaluated on telecommunication network data with known ground truth. Empirical results reveal that UNAD can detect attacks on which the workflows have not been trained on with a precision of 75% and a recall of 80%. The benefit of UNAD with existing network attack detectors is, that it can detect completely new attack types that have never been encountered before. © ECMS Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela (Editors)

关键词： Intrusion detection

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Healthcare Design Based on Virtual Reality Technology: Enhance the User's Engagement in the Rehabilitation Process

Healthcare Design Based on Virtual Reality Technology: Enhan...

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10th international conference on Applied Human Factors and Ergonomics / AHFE international conference on Human Factors and Wearable technologies / AHFE international conference on Game Design and Virtual Environments

作者： Qiao, Yu Yang, Siyi Cent S Univ Dept Ind Design Acad Arts & Architecture Changsha Hunan Peoples R China Cent S Univ Sch Publ Adm Changsha Peoples R China

ISBN: (纸本)9783030204761;9783030204754

This study on VR technology in healthcare design aims to enhance the engagement and provide an interactive way for the whole rehabilitation process. By presenting the real-time data to the users and collecting their feedback, users' participation and achievements increase. From the study and analysis on the existing postoperative knee rehabilitation training, we establish typical user profiles, analyze their pain points and specific needs, test it by employing the users' data we collected, build the data modeling and then propose corresponding solutions. A working prototype of the device is used to test and illustrate the device's availability in the generation and calculation of motion data. Based on this, in the research, the further application of VR in the field of medical rehabilitation is promising, and a variety of possibilities concerning the combination of VR as a technology and a kind of future life style from the essence of things are put forward.

关键词： Rehabilitation Training Knee joint Healthcare data visualization

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Detection of Malicious Traffic in IoMT Environment Using Intelligent XGboost Approach

Detection of Malicious Traffic in IoMT Environment Using Int...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Yugandhar Manchala Janmenjoy Nayak H. S. Behera Department of Information Technology Veer Surendra Sai University of Technology Burla Odisha India Department of Computer Science Maharaja Sriram Chandra Bhanja Deo University Baripada Odisha India

The paradigm of computing has completely altered as a result of the development of communication and Information technology. The Internet of Things (IoT) is a type of communication environment made up of web-enabled devices that can communicate, gather, assess, and send data across the network without the need for human participation. The IoT idea is widely established in numerous fields of application, and it has also been used in the medical industry. The Internet of Medical Things (IoMT) refers to the combination of IoT with medical technology. Even though IoMT applications have facilitated real time monitoring in the healthcare sector, it suffers from several security and privacy attacks. The presence of such security and privacy attacks can cause the alteration or disclosure of sensitive data or sometimes authorized users cannot access the data also. Hence, it is crucial to secure the IoMT environment from such malicious attacks. Therefore, this study aims in assessing harmful traffic in IoMT environment. In this research, XGBoost classification algorithm is proposed to classify malicious traffic in an IoMT environment. Further, the study has also implemented state-of-art machine learning algorithms such as DT (Decision Tree), RF (Random Forest), LR (Logistic Regression), SVM (Support Vector Machine), NB (Naive Bayes), MLP (Multilayer Perceptron), SGD (Stochastic Gradient Descent) to test the efficacy of the proposed model. The experimental outcomes indicate that the proposed XGBoost algorithm outperformed other conventional machine learning approaches in terms of predicting malicious traffic in an IoMT environment, with a 100-percentage accuracy rate. Furthermore, the XGBoost algorithm exhibits better performance in terms of precision, Fl-score, recall, and AUC as compared with traditional approaches.

关键词： Support vector machines Radio frequency Machine learning algorithms Stochastic processes Prediction algorithms Real-time systems Classification algorithms

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A Generator-based Method for Attacking Embedding-based Text Classifiers

A Generator-based Method for Attacking Embedding-based Text ...

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IEEE international conference on Research, Innovation and Vision for the Future

作者： Kha Do Minh Duc-Anh Nguyen Nguyen Le Minh Pham Ngoc Hung VNU University of Engineering and Technology Japan Advanced Institute of Science and Technology (JAIST)

ISBN: (纸本)9781665461672

This paper proposes a method to attack embedding-based text classifiers, namely GenEmC, to reduce the high computational cost of rule-based replacement. GenEmC includes three main steps including data preparation, embedding decoder training, and generator training. The embedding decoder is learned to convert embedding vectors to texts. The generator is trained by a two-term loss to generate adversarial embedding vectors from original texts. After three steps, the combination of two trained models could generate adversarial texts instantly. To demonstrate the effectiveness of GenEmC, the experiments are conducted on the IMDB dataset. The target classifiers are well-trained GRU, LSTM, and Bert. GenEmC is compared with two well-known rule-based replacement methods known as PWWS and TextBugger. The experiments have demonstrated that for 1,000 texts in test set, while the computational cost of GenEmC is an average of about 6 minutes, the others require at least 10 hours. Besides, the average success rate and semantic-preserving output score of GenEmC is moderately higher than those of PWWS and TextBugger.

关键词： Training Computational modeling Text categorization Generators Communications technology Decoding Computational efficiency

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Towards a System to Monitor the Virus’s Aerosol-Type Spreading 1

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1st IFIP TC 5 international conference on Computer science Protecting Human Society Against Epidemics, ANTICOVID 2021

作者： Arnicans, Guntis Niedrite, Laila Solodovnikova, Darja Virbulis, Janis Zemnickis, Janis Faculty of Computing University of Latvia Raina bulvaris 19 Riga1586 Latvia Institute of Numerical Modelling University of Latvia Riga Latvia

ISBN: (数字)9783030865825

ISBN: (纸本)9783030865818

Recent scientific studies indicate that attention should be paid to the indoor spread of the Covid-19 virus. It is recommended to reduce the number of visitors to the premises and to provide frequent ventilation of the premises. The problem is that it is not known what the risk of infection is in a particular room at a specific time, when and what actions should be taken to reduce the risk. We offer a system that helps monitor the conditions in the premises with the help of sensors, calculate the risk of infection and provide information to reduce the infection risk. We give an insight into the created prototype with data collection from public spaces and data visualization according to user needs. © 2021, IFIP international Federation for Information Processing.

关键词： Visualization

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