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Research on the Application of Artificial Intelligence in Cybersecurity: Integrating Advanced Technologies to Improve threat Detection and Response

Research on the Application of Artificial Intelligence in Cy...

引用

Global conference for Advancement in Technology (GCAT)

作者： T.S.Balaji R.Nadanasabai Rajesh Jagadeesan Ravikumar S.Sharanyaa R.Meenal Nandigam Poornima Deepthi Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Department of MBA St.Josephs College of Engineering Chennai Software Engineering Senior Advisor Evernorth Health Services Chattanooga TN Department of Information Technology Panimalar Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B Engineering College India Department of ECE MLR Institute of Technology Hyderabad

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

In the current age of digital change, the field of cybersecurity is faced with progressively advanced and enduring dangers. Conventional security measures are finding it difficult to keep up with the fast-changing nature of cyber threats. Artificial intelligence (AI) offers a significant chance to enhance threat detection and response systems by leveraging its sophisticated data processing and pattern recognition abilities. this study explores the incorporation of artificial intelligence (AI) technology in the field of cybersecurity, specifically aiming to improve the effectiveness of systems that identify and respond to threats. the paper commences by examining the present difficulties encountered by cybersecurity experts, which encompass the large number and diverse range of potential threat vectors, the covert and flexible nature of contemporary malware, and the limitations imposed by resource constraints on the efficacy of human-driven analysis. the text explores the current cutting-edge AI strategies used to tackle these difficulties, including anomaly identification through machine learning algorithms, predictive analysis using deep learning models, and threat intelligence collecting through natural language processing. the research also examines the distinct factors of AI in the cybersecurity field, such as the requirement for immediate decision-making, the significance of comprehending the model, and the possibility of hostile attacks on AI systems. this study seeks to analyse current literature and emerging trends to gain a deep understanding of how artificial intelligence (AI) can enhance cybersecurity. Additionally, it aims to identify the obstacles and deficiencies that must be overcome to effectively incorporate AI technologies into this crucial domain. To summarise, the research highlights the considerable capacity of AI to transform cybersecurity practices, providing a way to develop more robust and flexible defence methods against the numerous danger

关键词： Deep learning Ethics Machine learning algorithms Transforms Semisupervised learning threat assessment Vectors Safety Artificial intelligence computer security

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Employee Fingerprint Identification Management System Based on Bidirectional ResNext Network with Triplet Loss

Employee Fingerprint Identification Management System Based ...

引用

Management engineering, software engineering and Service Sciences (ICMSS), International conference on

作者： Yuezhi Yang Qian Yang Zhihao Su Wenlong Wu Computer Science College Guangzhou College of Applied Science and Technology Guangzhou China La Consolacion University Philippines

ISBN: (数字)9798350393705

ISBN: (纸本)9798350393712

With the increasing digitization and modernization of enterprise management, employee fingerprint identification management systems have become a key tool to ensure the safety and efficiency of the office space. Such a system not only accurately verifies the identity of employees but can also be used for attendance, access control and other related applications. However, improving the accuracy of fingerprint recognition remains a challenge. In this paper, we propose a fingerprint identification method based on a bidirectional ResNext network and combined with triplet loss. Compared with the traditional ResNext network, our method captures richer fingerprint features through bidirectional structure and triplet loss, thereby achieving higher matching accuracy. We conducted extensive experiments on the public FVC (Fingerprint Verification Competition) dataset and our own collected dataset CSFI (Company staff fingerprint identification). Experimental results show that compared with the original ResNext network, our method achieves approximately 3% and 5% performance improvements on the two data sets respectively. these results verify the effectiveness of our proposed method on fingerprint identification tasks and provide strong technical support for the further development of employee fingerprint identification management systems.

关键词： Deep learning Access control software algorithms Companies Fingerprint recognition Aerospace electronics Safety

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Constructing a Mobility and Acceleration Computing Platform with NVIDIA Jetson TK1

Constructing a Mobility and Acceleration Computing Platform ...

引用

IEEE International Symposium on Cyberspace Safety and Security

作者： Min Su Jianjun Tan Chun-Yuan Lin Jin Ye Chung-Hung Wang Che-Lun Hung College of Life Science and Bioengineering Beijing University of Technology Department of Computer Science and Information Engineering Chang Gung University Department of Computer Science and Communication Engineering Providence University

ISBN: (纸本)9781479989386

Current high-end graphics processing units (GPUs), which contain up to thousand cores per-chip, are widely used in the high performance computing community. However, in the past, the cost and power consumption of constructing a high performance platform with graphics cards, such as Tesla and Fermi series, are high. Moreover, these graphics cards all installed in personal computers or servers, and then the immediate and mobility requirements can not be provided by this platform. NVIDIA Jetson TK1 (Tegra K1) is a full-featured platform for embedded applications and it contains 192 CUDA Cores (Kepler CPU). Due to its low cost, low power consumption and high applicability, NVIDIA Jetson TK1 has become a new research direction. In this paper, we construct a mobility and acceleration computing platform with NVIDIA Jetson TK1. Besides, two tools, ClustalWtk and MCCtk are designed based on NVIDIA Jetson TK1. these tools both can achieve 3 and 4 times speedup ratios on single NVIDIA Jetson TK1 by comparing with their CPU versions on Intel XEON E5-2650 CPU and ARM Cortex-A15 CPU, respectively. Moreover, the cost-performance ratio by NVIDIA Jetson TK1 is higher than that by NVIDIA Tesla K20m. In addition, the user friendly interfaces are also provided by these two tools, respectively.

关键词： NVIDIA Jetson TK1 CUDA Mutliple Sequence Alginment Compound Comparison Parallel Processing

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A novel 100 watt High Intensity Focused Ultrasound driving system

A novel 100 watt High Intensity Focused Ultrasound driving s...

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Annual International conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： David A. Steines Kie-Bum Eom thomas Manuccia Vesna Zderic Department of Electrical and Computer Engineering George Washington University Washington D.C. DC USA

Ultrasound B-Mode imaging systems are often used to image tissues to which High Intensity Focused Ultrasound (HIFU) is being applied. Since HIFU operates at frequencies in the same range as a B-mode imager, the two technologies interfere when they are simultaneously applied to the patient. this causes the imaging window to “whiteout” and obscure the target region, thus defeating the purpose of using the ultrasonic imager in the first place. the system in this paper controls the interference window by pulsing the HIFU at a frame rate synchronized to the ultrasonic imager. the frame rate is either approximated by using an on-board oscillator, or it is captured from stray electromagnetic waves received from the ultrasound transducer. the system is capable of delivering 100 W of peak electrical output power to a HIFU transducer. It is controlled by a custom software application, and synchronizes with B-Mode imaging systems with frame rates from 10–100 Hz. An on-board Direct Digital Synthesizer generates HIFU RF from 1–5MHz. the complete system provides precision positioning of the interference window in a small and portable desktop package.

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Design and application of intelligent edge terminals

Design and application of intelligent edge terminals

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Electronic Information engineering and computer Communication (EIECC), International conference on

作者： Yan Liu Enguo Zhu Metering Department China Electric Power Research Institute Beijing China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

With the continuous development of 5G technology and the large-scale connection of new energy, traditional power terminals can no longer meet new business needs such as real-time energy and refined load management of electricity users. therefore, based on the business needs of major scenarios such as smart home appliances, smart buildings, and smart parks, this paper carries out the development of intelligent edge terminals (IEU), realizing high-frequency collection of massive energy consumption data based on 5G communication technology. It introduces D2D technology to build communication links between terminals, which further enhances data transmission rate. At the same time, it realizes load identification and load prediction based on the edge computing capabilities. It also realizes real-time regulation and management of load resources to achieve intelligent monitoring of electrical equipment.

关键词： Home appliances Smart buildings 5G mobile communication Smart homes Real-time systems Hardware software Regulation Business Edge computing

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Improving Coding Performance and Energy Efficiency of Erasure Coding Process for Storage systems - A Parallel and Scalable Approach

Improving Coding Performance and Energy Efficiency of Erasur...

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IEEE International conference on Cloud Computing, CLOUD

作者： Hsing-Bung Chen Song Fu HPC-5 Group Los Alamos National Lab Los Alamos New Mexico USA Department of Computer Science and Engineering University of North Texas Texas Denton

Erasure code based object storage systems are becoming popular choices for archive storage systems due to cost-effective storage space saving schemes and higher fault-resilience capabilities. Both erasure code encoding and decoding procedures involve heavy array, matrix, and table-lookup compute intensive operations. With today's advanced CPU design technologies such as multi-core, many-core, and streaming SIMD instruction sets we can effectively and efficiently adapt the erasure code technology in cloud storage systems and apply it to handle very large-scale date sets. Current solutions of the erasure coding process are based on single process approach which is not capable of processing very large data sets efficient and effectively. To prevent the bottleneck of a single process erasure encoding process, we utilize the task parallelism property from a multicore computing system and improve erasure coding process with parallel processing capability. We have leveraged open source erasure coding software and implemented a concurrent and parallel erasure coding software, called parEC. the proposed parEC process is realized through MPI run time parallel I/O environment and then data placement process is applied to distribute encoded data blocks to their destination storage devices. In this paper, we present the software architecture of parEC. We conduct various performance testing cases on parEC's software components. We present our early experience of using parEC, and address parEC's current status and future development works.

关键词： Encoding software Multicore processing Testing Parallel processing Bandwidth Parallel programming

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A Novel BCI-Controlled Pneumatic Glove System for Home-Based Neurorehabilitation

A Novel BCI-Controlled Pneumatic Glove System for Home-Based...

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Annual International conference of the IEEE engineering in Medicine and Biology Society

作者： Aodhan L. Coffey Darren J. Leamy Tomas E. Ward Department of Electronic Engineering National University of Ireland Maynooth

ISBN: (纸本)9781424479276

Commercially available devices for Brain-computer Interface (BCI)-controlled robotic stroke rehabilitation are prohibitively expensive for many researchers who are interested in the topic and physicians who would utilize such a device. Additionally, they are cumbersome and require a technician to operate, increasing the inaccessibility of such devices for home-based robotic stroke rehabilitation therapy. Presented here is the design, implementation and test of an inexpensive, portable and adaptable BCI-controlled hand therapy device. the system utilizes a soft, flexible, pneumatic glove which can be used to deflect the subject's wrist and fingers. Operation is provided by a custom-designed pneumatic circuit. Air flow is controlled by an embedded system, which receives serial port instruction from a PC running real-time BCI software. System tests demonstrate that glove control can be successfully driven by a real-time BCI. A system such as the one described here may be used to explore closed loop neurofeedback rehabilitation in stroke relatively inexpensively and potentially in home environments.

关键词： Electroencephalography Band-pass filters Training Medical treatment Robots Real-time systems software

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TrajTrace: Tracing Moving Objects over Social Media 28th

TrajTrace: Tracing Moving Objects over Social Media

引用

28th International conference on Database systems for Advanced Applications, DASFAA 2023

作者： Yang, Zhihao Zhang, Yunqi Li, Songda Chen, Qinhui Zhao, Hui Cai, Wei Lin, Xi Software Engineering Institute East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing Shanghai China Electronics Technology Group Corporation The 51st Research Institue of China Beijing China

ISBN: (纸本)9783031306778

Online social media has lots of moving object information. Extracting movement information is a challenging work. It suffers from spatio-temporal information extraction, vague time, and vague location. Previous information extraction methods merely focus on the trajectory extraction. In this demonstration, we develop a web-based application, TrajTrace, to track moving objects. TrajTrace extracts triple from social media text employing our proposed span-level joint entity and relation extraction model, OMLer. OMLer casts joint extraction as a token pair multi-categories classification task. It predicts the triple list corresponding to the input sequence. We employ BERT to encode the input sentence word by word. the self-attention mechanism and BiLSTM are applied to learn sequence features. then, an order-first time matching algorithm is designed to solve the lacking temporal information problem in the extracted triples. Utilizing the proposed TF-IDF based clustering algorithm, we make the vague time accurate. the vague geographic location is converted to accurate latitude and longitude using the Bezier geodetic coordinate conversion algorithm. Toward aircraft and ships, besides the keyword search and the trajectories visualization, TrajTrace provides the historical activity area search of a specific object and the spatio-temporal distribution of moving objects at a given time or location. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

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Improvement the Performing of Spectrum Distinguishing in Cognitive Radio using MIMO-Alamouti Scheme

Improvement the Performing of Spectrum Distinguishing in Cog...

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International conference on computer engineering and systems

作者： Mohamed Ismail Ibrahim Dina M. Ellaithy Modern Academy for Engineering and Technology Cairo Egypt Electronics Research Institute (ERI) Cairo Egypt

ISBN: (数字)9781728152608

ISBN: (纸本)9781728152615

this paper exploits the efficient performing of the Multiple Input Multiple Output (MIMO)-Alamouti scheme for spectrum sensing in cognitive radio (CR). Consequently, enhancement in the overall performance and the detection probability by using the MIMO-Alamouti scheme is achieved. Moreover, at low signal-to-noise ratio (SNR), the cooperative spectrum distinguishing algorithm among the different spectrum distinguishing techniques is employed to raise the probability of detection and also solving the hidden node problem. Matlab software is used to simulate the detection probability versus SNR for different schemes. Up to 50% enhancement in detection probability (Pd) as compared with the conventional technique under signal to noise ratio (SNR) equals -15 dB and false alarm probability (Pf) equals 0.1. As compared with the common spectrum sensing approach in case of the majority rule, at least 10% advance in the probability of detection at false alarm probability equals 0.1 under SNR equals -10 dB and the number of secondary user (SU) equals 5.

关键词： Sensors Signal to noise ratio MIMO communication Cognitive radio Transmitting antennas Receiving antennas

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Deep Learning Empowered High Accuracy and Low Complexity Indoor Channel Prediction for Wireless Communication systems

Deep Learning Empowered High Accuracy and Low Complexity Ind...

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IEEE Annual International computer software and Applications conference (COMPSAC)

作者： Rong-Terng Juang Tong-Wen Wang Jun-Xiang Cao Hsin-Piao Lin Ding-Bing Lin Institute of Aerospace and Systems Engineering National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering Feng Chia University Taichung Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

the rapid expansion of wireless communication mandates the development of efficient and computationally light solutions for base station deployment. Despite the manifold advantages of the latest 5G networks, challenges such as limited coverage and signal attenuation persist, underscoring the need for swift and precise estimation of base station coverage. To address this imperative, this paper introduces a deep learning-based indoor channel prediction model. through comparative analysis with a traditional high-precision ray-tracing model, the paper showcases the computational prowess of the proposed deep-learning model. Once trained, it achieves an impressive 99.7% reduction in computation time while maintaining comparable accuracy to the ray-tracing model. the proposed method promises expedited and accurate evaluation of optimal base station placements, particularly beneficial for indoor networks such as 5G small cells or wireless LAN. By integrating into handheld platforms, users can swiftly input indoor layouts and base station locations to obtain signal coverage. Such integration not only reduces power consumption but also drives advancements in energy-efficient practices within telecommunication infrastructure.

关键词： Wireless communication Base stations Analytical models Wireless LAN Accuracy 5G mobile communication Computational modeling

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