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检索条件"机构=Cyber Security in Computer Science and Engineering"

共 2596 条记录，以下是421-430 订阅

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Face Detection in Video Live Stream Using Deep Learning 2

Face Detection in Video Live Stream Using Deep Learning

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Amrita Kumar, Rajiv Joshi, Shivani Raj, Daksh Centre for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Nvidia Ai Learning & Research Center U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9798350371055

With the increasing popularity of live streaming platforms, there is an imperative need for robust methods for identifying individuals in real-time. The proposed system utilizes Convolutional Neural Networks (CNN) computer vision algorithms to identify faces in live streams and match them against a pre-existing database of known individuals. Additionally, the system employs deep learning and supervised learning techniques along with various types of CNN to achieve robust facial recognition in diverse lighting and environmental conditions. The system's accuracy is evaluated using various performance metrics, including precision, recall, and F1-score. The results demonstrate that the proposed system achieves high accuracy in person identification during real-time live streams. This system holds potential applications in a variety of domains, including security, entertainment, and social media. © 2024 IEEE.

关键词： computer vision Convolutional neural networks (CNN) deep learning F1-score Live streaming precision recall supervised learning

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Xabcd: A Better Model for Named Entity Recognition in cyber Threat Intelligence 9

Xabcd: A Better Model for Named Entity Recognition in Cyber ...

引用

9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Wang, Ruiqi Zheng, Zhiqiang Ao, Shengqin Fu, Yuxia Wang, Xuren Jiang, Zhengwei Xie, Gang School of Big Data and Computer Science Guizhou Normal University Guiyang China Information Engineering College Capital Normal University Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350376548

Due to the ambiguity between naming entity nouns and daily usage nouns in cyber threat intelligence, such as hacker organizations, attack tools, and attack techniques, the understanding of their content heavily relies on cyber security knowledge. How to quickly and accurately extract named entities related to network security from multi-source texts has become a hot and difficult research topic in the current threat intelligence field. In this paper, a named entity recognition framework from cyber threat intelligence unstructured text is designed. For the business requirements in the field of cyber threat intelligence, this framework completely covers data acquisition, text classification, entity and relationship definition, entity and relationship analysis and modeling and ability application. The framework is available for experimental exploration of entity recognition technology for threat intelligence. In addition, this paper proposes an entity recognition model for cyber threat intelligence, Xabcd, based on data and feature knowledge. Based on open network security text corpus, Xabcd model obtained the key indicators such as precision, recall, and F1-score as 97.35%, 97.15% and 97.23% respectively, which are all higher than the baseline model levels, so the experimental results demonstrate the feasibility and effectiveness of the model. © 2024 IEEE.

关键词： cyber attacks

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Bitcoin Price Prediction Using Cuckoo Search Algorithm for Feature Selection with LSTM Model 2

Bitcoin Price Prediction Using Cuckoo Search Algorithm for F...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Radmand, Elnaz Pirgazi, Jamshid Odeh, Mahmoud Sorki, Ali Ghanbari University of Science and Technology of Mazandaran Faculty of Electrical and Computer Engineering Behshahr Iran School of Information Technology Zarqa University Cyber Security Department Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798350373363

Deep learning is now a top method for predicting the price of Bitcoin, providing an excellent and well thought out strategy. This technique makes use of a wealth of historical data from reliable sources covering the first ten years of Bitcoin. Technical data and complex patterns are deeply integrated into the dataset to enhance the model's comprehension of complex Bitcoin price patterns. These additions greatly deepen our understanding of the various factors affecting the price of Bitcoin. This method is based on a strategic procedure that involves the careful selection of certain high-performing characteristics. These traits go through a stringent screening process that uses advanced algorithms such as the Cuckoo algorithm. The chosen features are then smoothly incorporated into an encoder-decoder model of LSTM (Long Short-Term Memory), improving the model's ability to predict Bitcoin prices. In this case, it is critical to apply deep learning methods, especially the LSTM encoder-decoder model. Through the integration of technical indicators and sophisticated selection techniques with historical Bitcoin data, this approach leverages deep learning capabilities to identify intricate patterns in Bitcoin pricing movements. In the end, this gives the model the ability to forecast future changes in the price of Bitcoin with accuracy. © 2023 IEEE.

关键词： Forecasting

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DataJudge: Cross-Chain Data Consistency Verification Based on Extended Merkle Hash Tree 24th

DataJudge: Cross-Chain Data Consistency Verification Based o...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Yuwei Zeng, Junyu Cao, Jie Dai, Shengjiang Xiang, Qiao Cheng, Guang School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Department of Electrical and Computer Engineering Queen’s University Kingston Canada School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9789819615414

Consortium blockchains, with their decentralized characteristics, have found widespread applications across various industries. However, this also forms isolated data islands. These data islands have become obstacles to data sharing between industries, and the issue of cross-chain data consistency verification remains unresolved. Current mainstream cross-chain technologies solve asset transfer issues through complex transaction steps, thus failing to meet the need for efficient data transmission. Existing research has proposed solutions for cross-chain data consistency verification, but two major issues remain. Firstly, these schemes do not consider data changes or support data dynamic operations. Secondly, these schemes involve a significant amount of complex computations, leading to slow efficiency. To address the above issues, we propose DataJudge, a cross-chain data consistency verification scheme based on an extended Merkle hash tree. This scheme not only verifies cross-chain data consistency but also effectively fills the gap in existing schemes regarding dynamic operations. Additionally, we modify the paillier homomorphic encryption. To improve efficiency, we do not introduce many complex computations. Furthermore, the extended Merkle hash tree structure enhances efficiency. Finally, we implement a prototype system and evaluate it. The results demonstrate that DataJudge can effectively verify cross-chain data consistency and outperform existing schemes such as DCIV and BeDCV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data consistency

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An Efficient and Privacy-Preserving Participant Selection Scheme based on Location in Mobile Crowdsensing 23

An Efficient and Privacy-Preserving Participant Selection Sc...

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23rd IEEE International Conference on Trust, security and Privacy in Computing and Communications, TrustCom 2024

作者： Cheng, Yudan Feng, Tao Liu, Zhiquan Guo, Xian Han, Lulu Ma, Jianfeng Lanzhou University of Technology School of Computer and Communication Lanzhou China Jinan University College of Cyber Security Guangzhou China Luoyang Normal University School of Information Science Luoyang China Xidian University School of Cyber Engineering Xi'an China

With the increasing demand for sensing data, the selection efficiency of task participants in mobile crowdsensing (MCS) becomes more and more important. How to efficiently choose eligible task participants while preserving privacy is an urgent issue. In this paper, we propose an efficient and privacy-preserving participant selection scheme based on location in MCS, named EPPS. Specifically, the k-anonymity technique is adopted to generate anonymous location matrices to preserve location privacy of sensing tasks and task participants. To improve selection efficiency, the anonymous location matrices of multiple sensing tasks are consolidated into a matrix called merged location matrix. Similarly, the anonymous location matrices of multiple task participants are also merged. Then, the cloud server computes the Hadamard product for merged location matrices to judge whether task participants locate in the corresponding sensing areas. By judging the merged location matrices of sensing tasks and task participants, multiple participants which satisfy the requirements of sensing tasks can be selected at the same time. Finally, the performance evaluations demonstrate that the proposed scheme outperforms the existing schemes in terms of efficiency, to be specific, the computation overhead of EPPS is decreased by 77% - 85%, the communication overhead of EPPS is decreased by 41%, the selection efficiency of the EPPS is increased by 66% when multiple task participants and one sensing tasks, and the selection efficiency of the EPPS is increased by 96% when multiple task participants and multiple sensing tasks. © 2024 IEEE.

关键词： k-anonymity Mobile crowdsensing participant selection privacy preservation selection efficiency

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Deepfake Image and Video Detection Using Deep Learning Algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Srivanth Srinivasan Nischitha P Akshita Chavan Mohana Artificial Intelligence & Machine Learning Engineering RV College of Engineering® Bengaluru India Computer Science & Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Streaming media Feature extraction Generators Convolutional neural networks Artificial intelligence Research and development

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ChainSafari: A General and Efficient Blockchain Verifiable Query Scheme with Real-Time Synchronization 24th

ChainSafari: A General and Efficient Blockchain Verifiable ...

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Yuwei Dai, Shengjiang Zeng, Junyu Cao, Jie He, Ran Xiang, Qiao School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Department of Electrical and Computer Engineering Queen’s University Kingston Canada School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9789819615414

With the rapid advancement of blockchain technology, its utilization has become widespread across various domains, resulting in the accumulation of significant volumes of valuable data. This trend has created an increasing demand for efficient querying of blockchain data. However, the inherent structure of blockchain leads to low querying efficiency and limited support for complex queries. Therefore, there is a trend toward migrating data to off-chain storage for querying purposes. The untrustworthy nature of the off-chain environment necessitates data validation. However, existing validation schemes incur high costs and fail to ensure the timely updating of the off-chain data. To address the challenges above, we propose ChainSafari, a general, efficient, and real-time synchronized scheme for verifiable querying on the blockchain. In our scheme, we design a general data model for storing transactions of various blockchains and develop an efficient authenticated data structure (ADS) based on this data model, enabling efficient verifiable queries for multiple data types in parallel. Furthermore, we propose a multi-party synchronization detection and recovery protocol to ensure the consistency of on-chain and off-chain data. Finally, we implement a prototype system of ChainSafari and conduct extensive experiments. The experimental results demonstrate the superior query throughput and enhanced data validation efficiency of ChainSafari compared to existing schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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Power Load Forecasting Model Based on LSTM and Prophet algorithm 3

Power Load Forecasting Model Based on LSTM and Prophet algor...

引用

3rd International Conference on Consumer Electronics and computer engineering, ICCECE 2023

作者： Zhang, Zhiling Wang, Xu Wang, Jing Yuan, Xueyu College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China Institute of Economics and Finance Polessky State University Pinsk Brest Belarus School of Information Engineering Shaoguan University Shaoguan China

ISBN: (纸本)9798350331578

Power system load forecasting in different periods is of great practical significance to improve the economic and social benefits of the power system, while the complex and variable meteorological conditions, social events and many other uncertain factors may have effects on the load of power system, which makes the application of traditional load forecasting models have certain limitations. At the same time, with the diversification of the load structure of power system, the effect of model application reduces gradually, so the problem of power system load forecasting needs to be further studied. In this paper, the combination method based on LSTM neural network and Prophet time series forecasting model is applied in order to forecast the power load with their respective model advantages for the fluctuation pattern of power load time series in different time periods. The proposed model has a certain reference value for improving the forecasting accuracy of power load and achieving accurate power deployment. © 2023 IEEE.

关键词： Long short-term memory

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An Intelligent Method for Credit Card Fraud Detection using Improved CNN and Extreme Learning Machine 8

An Intelligent Method for Credit Card Fraud Detection using ...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Yamini, Kalva Anitha, V. Polepaka, Sanjeeva Chauhan, Rahul Varshney, Yukti Singh, Mukesh Sri Ramachandra Institute of Higher Education and Research Cyber Security in Cse Tamilnadu Chennai India Imayam College of Engineering Department of Computer Science and Engineering Tamilnadu Trichy India Griet Department of Aimle Telangana Hyderabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Moradabad Institute of Technology Department of Computer Science & Engineering Uttar Pradesh Moradabad India Graphic Era University Department of Computer Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350396638

Credit card fraud has been on the rise in recent years. Criminals are taking advantage of the public by pretending to be legitimate businesses or individuals in order to steal their money. So, it is essential to combat this type of fraud. This research study has developed a novel method for spotting suspicious credit card transactions. The proposed approach may provide most of the necessary information to spot illegal or fraudulent financial transactions. As technology advances at an unprecedented rate, it becomes more difficult to monitor the illegal transactions and money transfers. With the recent developments in machine learning, artificial intelligence, and other critical areas of IT, it is now possible to automate this process and save huge amounts of labor involve in recognizing credit card fraud. After receiving an input image, it is preprocessed and features are retrieved by using principal component analysis, and then the data is used for training the CNN-ELM model. The proposed method achieves a higher accuracy (about 98.7%) than other methods like CNN and ELM. © 2023 IEEE.

关键词： Principal component analysis

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Efficient Resource Management for Real-time AI Systems in the Cloud using Reinforcement Learning 7

Efficient Resource Management for Real-time AI Systems in th...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Mallikarjunaradhya, Vinay Sreeramulu, Madhusudhan Dasari Mohammed, Abdul Sajid Boddapati, Nagesh Gupta, Ketan Natarajan, Yuvaraj Thomson Reuters Toronto Canada American Express Department of Cyber Security United States University of the Cumberlands Department of School of Computer and Information Sciences KY United States Microsoft Department of Computer Science CharlotteNC United States University of The Cumberlands Department of Information Technology WilliamsburgKY United States Sri Shakthi Institute of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350350067

The advent of artificial intelligence (AI) has driven an emergence in applications demanding online responses to immense amounts of data. Typically, the cloud-based deployment of these applications requires resource management to be optimized for high performance while meeting cost efficiency. However, conventional static resource allocation methods may only partially apply to the context of real-time AI applications as they are dynamic, unpredictable, and require more efficient & adaptive ways of allocating resources. To address this challenge, and as a solution in this research, we introduce an RL-based method for online resource management of cloud-based AI systems. Reinforcement learning (RL) is a machine-learning algorithm that allows systems to learn how best to perform tasks in changing environments based on feedback from those interactions with the environment. The third way our proposed approach utilizes the weapon of RL entails allowing for dynamic resource allocation so that resources are allocated when needed rather than from a static method as set at design time. © 2024 IEEE.

关键词： Adversarial machine learning

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