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Exploring Approaches for Teaching Cybersecurity and AI for K-12

Exploring Approaches for Teaching Cybersecurity and AI for K...

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IEEE international conference on data Mining Workshops (ICDM Workshops)

作者： Yu Cai Drew Youngstrom Wenbin Zhang College of Computing Michigan Technological University Houghton MI USA School of Computing & Information Science Florida International University Miami FL USA

As cybersecurity and AI become increasingly important, introducing these subjects to younger learners is critical. However, limited attention spans pose challenges for primary and secondary school students when learning these complex topics. By employing tools such as drones and Raspberry Pis, students can actively engage in learning cybersecurity and AI knowledge. This paper investigates the instructional benefits of Raspberry Pi and Drone platforms in K-12 education. The integration of hands-on activities through Raspberry Pi and Drone for Cybersecurity and AI content was implemented and evaluated at GenCyber summer camps in Michigan Technological University. The findings highlight the GoPiGo drone and Raspberry Pi as efficient instructional tools for teaching cybersecurity and AI to this age group. Additionally, hands-on tasks are essential for reinforcing understanding and maintaining student interest.

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Exploiting MIMO and cognitive Radio for Improved Performance in Indoor Communication Systems

Exploiting MIMO and Cognitive Radio for Improved Performance...

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Multimedia computing, Networking and Applications (MCNA), international conference on

作者： Ahmed Musa Rami Halloush Haythem Bany Salamah Zaid Bataineh Rahaf Al Suleibi Department of Telecommunications Engineering Yarmouk University Irbid Jordan College of Engineering Al Ain University Al Ain UAE Department of Electronics Engineering Yarmouk University Irbid Jordan

cognitive radio (CR) technology is essential in solving the radio spectrum shortage. It enables unlicensed users to opportunistically share wireless channels with licensed users, which can enhance the performance of emerging wireless networks. To further improve wireless network performance, multiple-Input multiple-Output (MIMO) technology has also been utilized. MIMO antennas can transmit data over multiple independent fading paths, resulting in transmission diversity and multiplexing gains. In this paper, we propose a novel MIMO-aware channel assignment protocol for CR networks (CRNs) with the objective of enhancing the achieved network through-put. The proposed channel assignment selects the best idle channel and MIMO antenna mode for each CR transmission. The proposed protocol aims to increase the number of per-user transmitted packets by jointly utilizing the multiplexing and diversity gains. The proposed protocol's performance is compared to reference protocols, including spatial multiplexing mode (SMM) and spatial diversity mode (SDM). The results indicate that the proposed CR-enabled MIMO-aware channel assignment protocol has significantly enhanced the overall network throughput.

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Analysis and Implementation of Markov Chain-Based Model for Spectrum Defragmentation in cognitive Radio-Enabled IoT Networks in 5G

Analysis and Implementation of Markov Chain-Based Model for ...

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Quantum Technologies, Communications, computing, Hardware and Embedded Systems Security (iQ-CCHESS), international conference on

作者： Subrat Kumar Sethi Arunanshu Mahapatro Dept. of Electronics and Telecommunication Engineering Veer Surendra Sai University of Technology Burla India

In the context of high demand for high-traffic applications and the increasing need for Internet of Things (IoT) in 5G and beyond 5G networks, cognitive Radio Networks (CRNs) have emerged as a potential solution due to the scarcity of RF spectrum. However, implementing IoT faces various challenges, including vulnerabilities to changing environmental conditions, efficient bandwidth distribution and utilization, and the high cost of purchasing RF spectrum. To address these challenges, effective spectrum management is crucial, and one aspect of it is spectrum defragmentation. Although it is impossible to completely avoid spectrum fragmentation, efficient spectrum allocation policies can minimize its impact. This paper suggests utilizing Discrete Time Markov Chain (DTMC) models to analyze the busy and idle times of primary users (PUs) in CRNs. The goal is to study the effect of defragmentation on the blocking probability of connections and propose a hybrid proactive-reactive defragmentation model that prioritizes secondary user (SU) traffic while ensuring the confidentiality and integrity of packet data delivery in IoT implementation in 5G. The analytical and simulation results demonstrate the positive impact of defragmentation in reducing the blocking probability and supporting SU traffic.

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Multiple Features-Aided Malicious Users Detection in OFDM-based cognitive Radio-Internet of Things

Multiple Features-Aided Malicious Users Detection in OFDM-ba...

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Next-Generation computing, IoT and Machine Learning (NCIM), international conference on

作者： Md Sipon Miah Md. Mahbubur Rahman Mingbo Niu Ana Garcia Armada Department of Information and Communication Technology Islamic University Kushtia Bangladesh Department of Signal Theory and Communications Universidad Carlos III de Madrid Leganes Madrid Spain Internet of Vehicle2 to Road Research Laboratory Chang'an University Shaanxi China

Cooperative Spectrum Sensing (CSS) is a beneficial approach to alleviate the spectrum scarcity issue by making it possible for the unlicensed users including normal and Malicious Users (MUs) to share the allocated available spectrum in a cognitive Radio-based Internet of Things (CR-IoT). Nevertheless, the detection performance has been compromised due to this approach being responsive to malicious attacks where each MU sends their fake sensing data to a Fusion Center (FC). In this paper, we propose multiple features-aided Machine Learning (ML) to accurately detect MUs in Orthogonal Frequency Division Multiplexing (OFDM)-based CR-IoT networks. Moreover, we solved the sum rate maximization problem for both primary and secondary networks using the golden section search method. Comparing the proposed approach to other conventional schemes, the numerical results demonstrate that it achieves superior detection gains, lower error rate, better system sum rate, and enhanced security where MUs are accurately detected.

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Trusted Channel Selection in cognitive Radio Network using VIKOR method

Trusted Channel Selection in Cognitive Radio Network using V...

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cognitive computing and Engineering Education (ICCCEE), international conference on

作者： Jitesh Kumar Bhatia Praveen Mittal Department of Computer Engineering & Applications GLA University Mathura India

cognitive radio networks referred as CRNs use opportunistic spectrum access to improve the utilization of wireless resources. However, the selection of channels with high quality and trust is a critical issue in CRNs. This paper proposes a novel channel selection method using the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method to enhance the trustworthiness of CRNs. The VIKOR method is used to solve the multi-criteria decision-making problem by ranking alternatives and selecting the most appropriate channel for transmission. The channel selection process is based on various criteria such as channel availability, channel quality, channel reliability, and channel security. The paper discusses the problem of sensitivity regulation in multi-channel cognitive medium access. The availability probability of each channel is known, and the paper considers the situation where the probable channels are the equal modeled while independent when present, and not employing adaptive modulation, and instinctive sensitivity issues of the line sequence. The paper proves that the VIKOR method is a great way to optimize this process. The proposed method is particularly useful in avionics networks that require strict reliability and security requirements. The paper proposes a hardware layer reliable channel model that creates a reliable system with a reliable channel model. The layer of hardware is entrenched in hardware equipment via measurement of agents with control. This ensures reliable operation and system control in terms of volume. The channel model can monitor the data of this system, preventing intermediaries and malicious fraud attacks from compromising its security. The paper provides selection algorithms for wireless networks of ad-hoc with several priorities including multi functions for multiple task processes. The main objectives of the algorithms include effective performance with utilization of ideal resource through the balancing of load as well as

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Chimp Optimization Algorithm with Fuzzy cognitive Map for Vibration-based Damage Detection

Chimp Optimization Algorithm with Fuzzy Cognitive Map for Vi...

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Sustainable computing and data Communication Systems (ICSCDS), international conference on

作者： Uppara Raghu Babu Tarun Gehlot S. Thenmozhi P. Saravanakumar A Ravitheja Nishant Anantrao Upadhye Civil Engineering Department Srinivasa Ramanujan Institute of Technology Rotarypuram Village BK Samudram Mandal Anantapur District India Department of Civil Engineering College of Technology and Agriculture Engineering Agriculture University jodhpur Rajasthan India Department of Civil Engineering St. Joseph’s College of Engineering Chennai Tamilnadu India Civil Engineering Department Sri Krishna College of Engineering and Technology Coimbatore Tamilnadu India Department of Civil Engineering SVR Engineering College Ayyalur Nandyal Kurnool Andhra Pradesh India Department of Civil Engineering Bharati Vidyapeeth’s Jawaharlal Nehru Institute of Technology Pune Mahrashtra India

In the past, Structural health monitoring (SHM) and vibration-oriented structural damage detection gain the great attention of mechanical, aerospace, and civil engineers. Initial and meticulous damage recognition has been one main aim of SHM applications. One key difficulty for structural damage detection utilizing observing dataset was to gain features that are delicate to damage but insensitive to noise (for example sensor measurement noise) along with the operational and environmental effects (for instance temperature effect). The performance of traditional damage detection techniques mainly relies upon the choice of the classifier and the features. Therefore, this study develops a Chimp optimization Algorithm with Fuzzy cognitive Map for Vibration-based Damage Detection (COAFCM-VDD) technique. The presented COAFCM-VDD technique determines cross-correlation functions of vibration data as fundamental features as input. The proposed COAFCM-VDD technique intends to derive damage features from the field measurement under the impact of noisy uncertainty. For detecting the damages, the FCM model is exploited in this work. At last, the performance of the FCM model can be improvised by the COA. The experimental result analysis of the COAFCM-VDD technique is tested using vibration dataset and the obtained outcomes signify the improved performance of the COAFCM-VDD technique.

关键词： Vibrations Temperature measurement Temperature sensors Uncertainty Measurement uncertainty Fuzzy cognitive maps Feature extraction

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Development of a Distance Education cognitive Technology Based on a Unified Artificial Immune System

Development of a Distance Education Cognitive Technology Bas...

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2022 international conference on Smart Information Systems and Technologies, SIST 2022

作者： Samigulina, Galina Samigulina, Zarina Institute Of Information And Computing Technologies Lab. 'Intellectual Control Systems And Forecasting' Almaty Kazakhstan Kazakh-British Technical University Faculty Of Information Technologies Almaty Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

Current trends in the development of the digital society show the relevance of distance education using innovative artificial intelligence technologies. The need for highly qualified engineering personnel determines the increased role of training students of technical specialties online in higher education institutions. The use of the latest advances in the field of artificial intelligence in education makes it possible to increase the efficiency and quality of student training and optimize the work of teachers with large student flows. The difficulties associated with distance education lie in the need for a differentiated approach to students with different cognitive and psychophysical abilities, social conditions and material and technical base. On the other hand, there are problems with the introduction of these technologies in the educational process and educational programs. The research is devoted to the development of a cognitive technology for distance learning of students of technical specialties using the promising direction of artificial immune systems on the MS Teams platform. It is proposed to use a unified artificial immune system to analyze and predict the efficiency of students' knowledge acquisition, as well as to promptly correct the learning trajectory. The technology has been tested on real data obtained in the process of training students of Kazakh-British Technical University JSC (KBTU). © 2022 IEEE.

关键词： Knowledge management

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Attention Feature Selection Based on Prior Knowledge Injection

Attention Feature Selection Based on Prior Knowledge Injecti...

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Haifeng Zhao Quanman Chen Lili Huang Yanping Fu Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China

High-dimensional data generally appear in various analysis tasks, but high-dimensional data often causes computational waste and noise interference. Feature selection can make the model achieve better performance by preserving the optimal feature subset in the data to solve the problem of data dimensionality reduction. It is a popular method to select feature subset by combining autoencoder structure reconstruction. However, most autoencoder methods only take reconstruction as the objective function, which will lead to a decrease in the ability of feature discrimination for data with obvious clustering structure. Moreover, only decoder is used as an auxiliary structure, and the performance of the decode cannot be further released, resulting in the sub-optimal feature subset obtained. In this paper, we obtain the internal clustering matrix of the data in advance through manifold learning and spectral analysis, which we call prior knowledge. By constraining the distance between the decoder output and the prior knowledge, the model can force learning the clustering information of the data and improve the ability to identify the feature. Secondly, an attention module is designed to highlight the feature information of key locations. We prove the effectiveness of our method by experiments on several standard datasets.

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A Framework of cognitive Intelligence-Enabled Welding Cyber Physical System

A Framework of Cognitive Intelligence-Enabled Welding Cyber ...

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IEEE international conference on Automation Science and Engineering (CASE)

作者： Tianyuan Liu Yanan Jiang Pai Zheng Jinsong Bao Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong China Aerospace System Engineering Shanghai Shanghai China College of Mechanical Engineering Donghua University Shanghai China

Building a welding cyber physical system (WCPS) is an important way to realize intelligent welding. However, the interaction between the current welding cyber world and the physical world is insufficient. On the one hand, it is reflected in the lack of cognition of the welding process that contains quality information. On the other hand, the interaction efficiency based on manual experience is low. A cognitive intelligence-enabled WCPS is proposed. Firstly, the welding knowledge graph is constructed to model and manage the multi-source data, so as to improve the interaction efficiency between welding cyber space and physical space. Secondly, the cognitive capability of the WCPS is improved through perceptual and cognitive computing. Finally, the welding action formed by each knowledge node is optimized based on reinforcement learning to improve the interactive capability of the WCPS.

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New Artificial Intelligence System Based on Information Processing Technology

New Artificial Intelligence System Based on Information Proc...

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data Analytics, computing and Artificial Intelligence (ICDACAI), international conference on

作者： Ziye Tian School of Information Engineering Shaanxi Institute of International Trade & Commerce Xi'an Shaanxi China

Artificial intelligence (AI) technology is a product of social evolution, an important integration of scientific and technological development, and has a profound impact on people's lives and work. In recent years, countries have increased their attention to AI research. AI is not an isolated technology, and it must be based on the development of information technology. AI encompasses a wider range of knowledge domains, spanning multiple disciplines. The development of AI must be more adaptable to human needs. The networked era continues to develop and must also change. For many people, AI and Knowledge management are interconnected. As information management systems themselves become increasingly complex and contain more and more management content, without intelligent network management as a support, many problems arise in the management process. This article first introduces information processing technology and AI systems, and then explores the construction of a new type of AI system. Finally, through experiments, the power of AI's text recognition function is verified (as long as the data volume is large enough, the recall rate can reach 93.5%, approaching 100%).

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