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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Wang, Jiangjing Fang, Hui Li, Shengqing Jiang, Jiulei School of Computer Science and Engineering Changshu Institute of Technology Suzhou China School of Computer Science and Engineering North Minzu University Yinchuan China

ISBN: (纸本)9798350304602

The study of influence maximization is an important theme in the research of social network information dissemination. Most of the previous research results are based on static social networks. Taking into account the temporal characteristics of real social networks, this paper focuses on the problem of influence maximization of temporal networks;that is, finding k nodes in the temporal network to maximize the spread of information. First, we define a method for calculating the propagation probability between nodes based on the eigenvector centrality. Secondly, we calculate the influence of nodes based on the local information and propagation probability of the nodes in the temporal network. Then, the independent cascade (IC) model is improved for the information propagation problem in the temporal network. On this basis, a temporal network influence maximization algorithm combining greedy and heuristics strategies (TCHG) is proposed. The algorithm selects the candidate seed node set in the heuristic stage, then selects the target seed node set from the candidate seed node set in the greedy stage. The experimental results show that compared with the existing heuristic algorithm, the TCHG algorithm has both a larger influence range and a reduced runtime, which reflects the accuracy and efficiency of the TCHG algorithm. © 2023 IEEE.

关键词： Heuristic algorithms

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Network Abnormal Traffic Detection Approach Based on Multi-Scaled Deep-CapsNet 5

Network Abnormal Traffic Detection Approach Based on Multi-S...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Tanwar, Shashi Thokala, Rishi Reddy Thapak, Sonika Maranan, Ramya Kumar, Sudheer Sravanthi, S. Aravali College of Engineering and Management Department of Computer Science and Engineering Haryana Faridabad India Srm Institute of Science and Technology Computer Science Engineering India Ies Collage of Technology Computer Science and Engineering India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India Piet Parul University Computer Science & Engineering Gujrat Vadodara India St. Martin's Engineering College Department of Computer Science Engineering secunderabad India

ISBN: (纸本)9798350321425

It is crucial for network security to be able to detect a variety of threats and unusual network traffic. In order to accomplish aberrant traffic detection, existing detection methods required enormous volumes of data. However, there are rare circumstances in which even a fewshot attack sample can be stopped. The shift in feature attitude will also have an impact on traffic attribute discrimination. However, the standard neural network model is unable to recognize this shift in perspective. As a result, few-shot sample anomalous traffic detection has poor accuracy and efficiency. This research has proposed a strategy for detecting unusual network traffic with a small number of samples. Multi-scale Deep-CapsNet and adversarial-reconstruction (MDAR) made up the system. To begin, the Deep-CapsNet's EM vector clustering is revamped. The prediction is finalized by transforming low-level to high-level features using the attitude transformation matrix. Second, the Deep-CapsNet was improved with the help of a multi-scale convolutional capsule. Finally, a reconstruction classification network (ARCN) that is vulnerable to adversarial input is proposed. Classification of the supervised source data and reconstruction of the unsupervised target data are both completed. In addition, an adversarial training technique that reduced the impact of noise on the reconstruction process is suggested. Using a mixture of MDAR, classification is obtained from a small number of samples (point 4). The results are tested on the ICSX2012 and CICIDS2017 datasets. The experimental outcomes validate the superior training performance of the approach. Additionally, its accuracy is unparalleled in both two- and multi-classification tasks. It is especially useful for real-time few-shot aberrant network traffic detection because to its good anti-noise performance and quick operating time. © 2023 IEEE.

关键词： Network security

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Exploration of frame animation technology based on WinCE platform 8

Exploration of frame animation technology based on WinCE pla...

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8th International Symposium on Advances in Electrical, Electronics, and computer engineering, ISAEECE 2023

作者： Deng, Ying Yu, Yuanhui Computer Science and Information Engineering School Xiamen Institute of Technology China Computer Engineering College Jimei University China

ISBN: (纸本)9781510666290

The exploration of frame animation technology based on WinCE platform frame animation is a common animation form that decomposes animation actions in consecutive key frames, that is, draws different content on each frame of the timeline and makes it play continuously into animation. This paper introduces the basic concept of WinCE, shows in detail how to build the development environment of WinCE, and studies the implementation technology of frame animation based on WinCE embedded operating system, and gives a detailed schematic diagram. Finally, the main process of realizing frame animation is introduced in detail with a complete example. Finally, a complete example is used to introduce the main process of frame animation in detail. © 2023 SPIE. All rights reserved.

关键词： Animation

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An Intelligent Forecasting Classification Model for Traffic Analysis using Machine Learning Techniques 4

An Intelligent Forecasting Classification Model for Traffic ...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kishor, Bodla Tiwari, Rajesh Patra, Raj Kumar Pinjakar, Latika Singh, Mahesh Cmr Engineering College Dept. of Computer Science and Engineering Hyderabad India Cmr Technical Campus Dept. of Computer Science and Engineering Hyderabad India Symbiosis Institute of Technology Dept. of Computer Science and Engineering Nagpur India Shri Shankaracharya Technical Campus Dept. of Eee Durg Bhilai India

ISBN: (纸本)9798350343670

In addition to taking care of the normal citizens, Road Traffic protection is also one of the main issue for corporations governing the transportation. We use data mining techniques to get the hidden matter from the database. The applications of Data mining is there in various fields such as fraud detection, marketing and many more. Many countries were affected due to many decisions that were taken by the governments one such issue is globalization. This has brought some drastically changes in the intake level and the financial related activities and this has given a boost to transportation department. Understanding the importance of the street protection the authorities are putting effort to know the reasons behind the street accidents. The increase in the street injuries made it difficult to determine the limitations of the street injuries. This paper explains the street injuries frequency from analysing the database. This work is about 95% efficient. © 2024 IEEE.

关键词： classification data mining machine learning traffic analysis Transportation models

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Top-Performing Unifying Architecture for Network Intrusion Detection in SDN Using Fully Convolutional Network 5

Top-Performing Unifying Architecture for Network Intrusion D...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Roy, Bishwarup Acharya, Indranil Papalkar, Darshil Joseph, Mary Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350321425

Internet's omnipresent adoption over the course of many years leading up to the current era along with Wireless Sensor Networks' (WSN) limitations has only increased the security threats to these high-speed networks. As a result, it is critical to deploy Intrusion Detection Systems (IDS) to monitor malicious activities in the network framework. Software Defined Network (SDN) has served its purpose to a certain extent by enabling centralized control and management of WSN and providing ways to implement IDSs more efficiently. However, like any emerging technology, SDN also has its share of vulnerabilities as there exists a gap for contemporary architectures, which can exploit global network assets and balance network resource consumption while providing programmability in terms of security of the network. To tackle this problem, a model has been developed that uses a fitting feature selection method to cut down on the count of features extracted and minimize the data volume relayed over the routes. Conclusions drawn from the scheme's trials demonstrate the proposed IDS technology's potency and effectiveness. The openly accessible University of Nevada - Reno Intrusion Detection Dataset (UNR-IDD) was used to evaluate the intrusion detection algorithm involving the Fully Convolutional Network (FCN) architecture and its effect using a statistical technique in detecting different types of attacks. © 2023 IEEE.

关键词： Deep neural networks

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Machine Learning Algorithmsfor Diagnosis of Parkinson's disease Based on Voice Characteristics 2

Machine Learning Algorithmsfor Diagnosis of Parkinson's dise...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Manoj, Polina Sai Reddy, Kondreddy Sumanth Kumar Sambath, M. Thangakumar, J. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

Bradykinesia, tremors, rigidity, and postural instability are symptoms of a degenerative neurological condition called Parkinson's disease (PD) affecting the motor movements. Deep brain stimulation, medication, and other therapies can all be used to treat theParkinson's disease symptoms, yet as of today, there is no effective treatment. Parkinson's disease must be recognized as early and precisely as feasible for effective disease treatment and the development of new therapies. The goal of this work is to create a model that can detect Parkinson's disease based on relevant clinical and demographic variables. It employs a number of machinelearning techniques, including Logistic Regression, Support Vector Machine (SVM), Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Random Forest Classifier, and ensemble method, which is a voting classifier. A significant dataset that contains data on changes in speaking patterns was used to train the model. Using measures for accuracy, precision, recall, and F1-score, the machine learning model's performance is assessed and contrasted with that of the most widely used techniques for Parkinson's disease diagnosis. © 2023 IEEE.

关键词： Support vector machines

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IoT-Genomics Analysis for Personal Health with Cybergenomics and Cloud Technology 10

IoT-Genomics Analysis for Personal Health with Cybergenomics...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Prabakaran, T. Babu, A. Ramesh Hemalatha, S. Ramasamy, Jayabharathi Kovarasan, Rajesh Kambattan Elango, B. Joginpally B.R. Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Vidya Vikas Institute of Engineering and Technology School of Computer Applications Karnataka Mysore India Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Recent innovations at the convergence of genomics and the Internet of Things (IoT) have opened the way for an innovative strategy for administering individualized health care. This research study combines the potential of wearable health monitoring technologies with genetic insights to analyze the personal healthcare. A comprehensive knowledge of health dynamics may be achieved by seamlessly integrating real-time health data collected from IoT-enabled wearables with individual genetic profiles. Using algorithms for machine learning, connections between genetic differences and instantaneous health responses may be uncovered, paving the way for the development of individualized therapies and lifestyle recommendations. The suggested framework allows people to improve their health and gives medical professionals access to data-driven insights that can be used for the early diagnosis of diseases and the development of individualized treatment plans. The work that has been done sheds light on the revolutionary potential of merging genetics with the Internet of Things to establish a new paradigm of preventative and individualized medical treatment. © 2024 IEEE.

关键词： Genome

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Damaged Image Repair using Masks with computer Vision Inpaint Method 2

Damaged Image Repair using Masks with Computer Vision Inpain...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Ramakrishna, Doddi Purna Gana Emmanuel, G. S. Michael Selvan, Mercy Paul Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665456302

Image inpainting is the technique used to automatically fix damaged areas using data from sections that have been saved. With the development of deep learning in recent years, image drawing performance has substantially increased. This research study reviews the main methods used for automating image inpainting. This research study provides a brief overview of traditional techniques while concentrating on deep learning-based inpainting techniques, covering model categorization, strengths and drawbacks, scope of application, and performance comparison. Finally, the challenges and trends surrounding automated image inpainting are examined and foreseen. A tool called image inpainting uses the data from the remaining components to repair damaged areas. With the advancement of society, image inpainting has become a vital research area in the field of computer vision. It is extensively used in culture, daily life, and security, including object removal and the preservation of digital cultural assets. Conventional methods build geometric models based on geometric consistency and image content similarity, or they use texture generation to patch up small sections of damaged images. It partially solves the problem of loose coupling between high-level semantics and low-level image properties, enabling deep learning to gradually overtake traditional methods in computer vision. © 2023 IEEE.

关键词： computer vision

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Med-DDoS-SDN: A Kafka Streams-Based Distributed Approach for Protecting Healthcare SDN Environments Against DDoS Attacks

Med-DDoS-SDN: A Kafka Streams-Based Distributed Approach for...

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Congress on Smart Computing Technologies, CSCT 2023

作者： Kaur, Amandeep Krishna, C. Rama Patil, Nilesh Vishwasrao Department of Computer Science and Engineering National Institute of Technical Teachers Training and Research Chandigarh India Department of Computer Engineering Government Polytechnic Chhatrapati Sambhaji Nagar Aurangabad India

ISBN: (纸本)9789819780952

The healthcare sector, dealing with vast and sensitive data, faces an ever-expanding need for efficient data management and security. Automated healthcare devices generate crucial information, and Software-Defined Networking (SDN) has become integral to healthcare systems due to resource efficiency, robust monitoring, and centralized control. However, in this data-centric environment, SDNs are vulnerable to various cyber threats, including Distributed Denial of Service (DDoS) attacks and probing attempts. These attacks can harm network performance, potentially endangering lives, especially with the rise of portable healthcare integrating mobile services and programmable devices. This article proposes a Kafka Streams-based distributed approach to protect SDN-based healthcare systems from DDoS attacks, named Med-DDoS-SDN. The Med-DDoS-SDN comprises two modules: (i) Real-time Attack Traffic Classification Module (RACM) and (ii) Preprocessed Data Storage Module (PDSM). The RACM categorizes incoming network traffic into four real-time classes. PDSM collects 21 essential features with outcomes into the Hadoop Distributed File System (HDFS) to maintain the classification model with the range samples. Med-DDoS-SDN is formulated and verified using the CICDDoS2019 dataset. The average classification accuracy of the distributed Med-DDoS-SDN solution stands at 92.75%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information management

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A user requirements-oriented privacy policy self-adaption scheme in cloud computing

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Frontiers of computer science 2023年第2期17卷 33-44页

作者： Changbo KE Fu XIAO Zhiqiu HUANG Fangxiong XIAO School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210003China College of computer science and technology Nanjing University of Aeronautics and AstronauticsNanjing 210016China School of Software Engineering Jinling Institute of TechnologyNanjing 210000China

In an ever-changing environment,Software as a Service(SaaS)can rarely protect users'*** able to manage and control the privacy is therefore an important goal for *** the participant of composite service is substituted,it is unclear whether the composite service satisfy user privacy requirement or *** this paper,we propose a privacy policies automatic update method to enhance user privacy when a service participant change in the composite ***,we model the privacy policies and service variation ***,according to the service variation rules,the privacy policies are automatically generated through the negotiation between user and service ***,we prove the feasibility and applicability of our method with the *** the service quantity is 50,ratio that the services variations are successfully checked by monitor is 81%.Moreover,ratio that the privacy policies are correctly updated is 93.6%.

关键词： cloud computing SaaS service privacy protec-tion privacy policies update

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