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Opportunities, Challenges, Strategies, and Reforms for ChatGPT in Higher Education

Opportunities, Challenges, Strategies, and Reforms for ChatG...

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2023 International Conference on Educational Knowledge and Informatization, EKI 2023

作者： Xie, Xiaoli Ding, Sheng Wuhan University of Science and Technology Journal Editorial Department of Wuhan University of Science and Technology Wuhan China Wuhan University of Science and Technology College of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350331264

ChatGPT is a powerful large language model developed by OpenAI. It has a profound impact on higher education fields. This technology offers exciting opportunities for students and educators, including personalized feedback, increased accessibility, interactive conversations, lecture preparation, performance evaluation, and new ways to teach complex concepts. At the same time, ChatGPT poses different threats and challenges to the traditional high education system, such as the possibility of cheating on online exams, human-like text generation, diminished critical thinking skills, and difficulties in evaluating information generated by ChatGPT. This paper explores the potential opportunities and challenges that ChatGPT poses in higher education from the perspective of students and educators. In higher education, by understanding these challenges and taking steps to mitigate them, we can then ensure the language models to be used in a responsible and ethical way. © 2023 IEEE.

关键词： Students

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Opportunities, Challenges, Strategies, and Reforms for ChatGPT in Higher Education

Opportunities, Challenges, Strategies, and Reforms for ChatG...

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Educational Knowledge and Informatization (EKI), International Conference on

作者： Xiaoli Xie Sheng Ding Journal Editorial Department of Wuhan University of Science and Technology Wuhan University of Science and Technology Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

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Decision Systems Optimization for Cybersecurity Resilience 12th

Decision Systems Optimization for Cybersecurity Resilience

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Ikhar, Sharayu Mondal, Dipannita Khubalkar, Deepti Yadav, Ranjeev Deshpande, Pallavi Patil, Sonali Vrushabhasagar Yashika Journal Publications Pvt. Ltd Maharashtra Wardha India Artificial Intelligence and Data Science Department Dr. Y. Patil College of Engineering and Innovation Telegaon410507 India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India EnTC VIIT Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601424

The increasing danger in the online environment highlights the necessity for strong and durable cybersecurity solutions. This study aims to improve Intrusion Detection Systems (IDS) by introducing a new hybrid method that merges Variational Autoencoders (VAEs) and Ant Colony Optimization (ACO) to enhance anomaly detection. The main goal is to enhance decision-making systems in IDS to attain a high level of accuracy, with a proven success rate of 99.3%. The Variational Autoencoder (VAE) functions as a tool for extracting features and reducing dimensionality by capturing latent representations of typical network behavior. The model can detect anomalous activities more accurately by using VAE to identify subtle patterns and deviations in network traffic. The Ant Colony Optimization (ACO) algorithm is incorporated into the decision-making process of the IDS to complement the VAE. Ant Colony Optimization (ACO) is used to optimize the parameters of Intrusion Detection Systems (IDS) and adjust the decision boundaries to improve the detection performance. The combined interaction of VAE and ACO forms a robust framework that can accurately detect anomalies and adjust in real-time to changing cyber threats. The hybrid model proposed was assessed with real-world cybersecurity datasets, showing an impressive accuracy of 99.3%. The experimental results highlight the VAE-ACO hybrid’s effectiveness in achieving high detection accuracy while remaining efficient in terms of computational resources. Combining Variational Autoencoders with Ant Colony Optimization shows great potential for enhancing decision systems in Intrusion Detection Systems. The 99.3% accuracy demonstrates the capability of this hybrid approach to greatly enhance cybersecurity resilience against advanced and evolving cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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A Multi-Layered Representation for Intrusion Detection System in Cyber Systems Using CNN Deep Learning Algorithm

A Multi-Layered Representation for Intrusion Detection Syste...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Mehra, Ritika Sohal, Asha Shrotriya, Shikha Gupta, Varun Kumar Kalra, Nuvita Dhabliya, Ritika School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India School of Engineering and Technology K. R. Mangalam University Department of Cse Gurugram India Invertis University Department of Law Bareilly India School of Computer Science and Applications Iimt University Meerut India Pune India Yashika Journal Publications Private Limited India

ISBN: (纸本)9798350301595

Convolutional Neural Networks (CNNs), a kind of deep learning architecture, have recently shown remarkable progress in the field of computer vision. This study models network traffic as a time-series, in particular transmission control protocol/internet protocols (TCP/IP) packets in a predetermined time range, and applies supervised learning techniques to improve performance for the job of detection of intrusions (ID) in cyber security. Each experiment uses a learning rate between [0.01] and [0.05] to run the models for up to 1000 epochs. When compared to traditional machine learning classifiers, CNN and its different architectures perform very well. This is primarily because CNNs can abstractly represent low-level characteristic sets of traffic on network links in the form of high-level features. © 2023 IEEE.

关键词： Network architecture

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Enhancing IoT Network Security through AI-Driven Intrusion Detection with Hybrid Autoencoder-GAN Fusion 5

Enhancing IoT Network Security through AI-Driven Intrusion D...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Mahajan, Rupali A. Dari, Sukhvinder Singh Dhabliya, Ritika Michael, Gabriela Kumar, Anil Ranjan, Nidhi COMP VIIT Maharashtra Pune India Pune India Yashika Journal Publications Pvt. Limited Maharashtra Wardha India Department of Computer Science & Engineering Poornima Institute of Engineering & Technology Rajasthan Jaipur India Vasantdada Patil Pratishthan's College of engineering and Visual Arts Maharashtra Mumbai India

ISBN: (纸本)9798400709418

The rapid growth of Internet of Things (IoT)IoT devices has changed how we connect and interact with our environment. This advancement has also complicated network security. As IoT networks grow, data integrity and confidentiality become more important. This study integrates a hybrid autoencoder and Generative Adversarial Network (GAN) model to use AI to solve security problems. The UNSW-NB15 dataset, a benchmark for network IDSs, is used to rigorously evaluate the proposed model. An autoencoder and GAN combination offers promising IDS for IoT networks. The autoencoder component excels at capturing concise and illustrative data representations, enabling it to spot network anomalies. The GAN component improves the model's ability to generate synthetic data samples that closely resemble network traffic patterns, making it easier to identify complex and novel attacks. The UNSW-NB15 dataset is known for its extensive network traffic data, making it ideal for testing the proposed artificial intelligence-based IDS. The proposed hybrid model achieves 99.13% accuracy. The result outperforms traditional DL methods. The proposed model's increased precision shows its ability to detect and address network anomaly risks. The proposed system is evaluated using precision, recall, F1-Score, AUC-ROC, MCC, and Cohen's Kappa. The thorough evaluation shows that the Proposed hybrid autoencoder-GAN model provides a complete and efficient IoT network security solution in all dimensions. In conclusion, artificial intelligence-based intrusion detection and a hybrid model that combines autoencoder and generative adversarial network techniques improve IoT network security. The model's UNSW-NB15 dataset accuracy shows its ability to protect IoT ecosystems from many threats. This study helps improve the reliability and robustness of networks, boosting confidence in the growing world of interconnected devices. © 2023 ACM.

关键词： Internet of things

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Improving Smart Grid Cybersecurity by Addressing the Risk of Incorrect Data Injection

Improving Smart Grid Cybersecurity by Addressing the Risk of...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Yadav, Muskan Vyas, Bhavesh Pathak, Paresh Bhatt, Rahul Pareek, Vaidehi Dhabliya, Ritika Invertis University Department of Law Uttar Pradesh Bareilly India School of Engineering and Technology K. R. Mangalam University Department of Electrical and Electronics Engineering Haryana Gurugram India School of Computer Science and Applications Iimt University Meerut India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India Pune India Yashika Journal Publications Private Limited India

ISBN: (纸本)9798350301595

Rapid change is being seen in the electrical grid. Microgrids, renewable energy sources, and the increased automation of grid-level decision-making made possible by sensors have all contributed to the grid's growing complexity. Studies in the field of smart grids have shown the grid's susceptibility to hacking. In example, new research shows how the insertion of erroneous data may cause a wide range of issues in the functioning of smart grids. A sophisticated assault might compromise the power grid's functioning and control without being detected by bad data detection equipment performing state estimation. In this study, we expand upon previous works in the field to detail how safeguarding selected vital sensor in the power system from fake data injection attacks might help mitigate the problem. This article discusses the IEEE 14 bus evaluation system and explains how a fake data injection attack may be carried out given just little information about the network. Different case studies are used to verify the assumptions used to determine the attacking zone. In this study, we see why protecting the electric grid from cyberattacks is so crucial. © 2023 IEEE.

关键词： Smart power grids

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Enhancing IoT Network Security through AIDriven Intrusion Detection with Hybrid AutoencoderGAN Fusion 23

Enhancing IoT Network Security through AIDriven Intrusion De...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Rupali A. Mahajan Sukhvinder Singh Dari Ritika Dhabliya Gabriela Michael Anil Kumar Nidhi Ranjan Computer Associate Professor COMP VIIT Pune Maharashtra India Computer Associate Professor Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pun India Computer Director Yashika Journal Publications Pvt. Limited Wardha Maharashtra India India Computer Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune India India Department of Artificial Intelligence & Data Science Professor Poornima Institute Of Engineering &Technology Jaipur Rajasthan India India Computer Associate Professor Vasantdada Patil Pratishthan?s College of engineering and Visual Arts Mumbai Maharashtra India India

ISBN: (纸本)9798400709418

关键词： Deep Learning

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A Multi-Layered Representation for Intrusion Detection System in Cyber Systems Using CNN Deep Learning Algorithm

A Multi-Layered Representation for Intrusion Detection Syste...

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Data science and Network Security (ICDSNS), International Conference on

作者： Ritika Mehra Asha Sohal Shikha Shrotriya Varun Kumar Gupta Nuvita Kalra Ritika Dhabliya School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India Department of CSE School of Engineering & Technology K. R. Mangalam University Gurugram India Department of LAW Invertis University Bareilly India School of Computer Science and Applications Iimt University Meerut India Symbiosis Law School Symbiosis International (Deemed University) Pune India Yashika Journal Publications Private Limited India

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Improving Smart Grid Cybersecurity by Addressing the Risk of Incorrect Data Injection

Improving Smart Grid Cybersecurity by Addressing the Risk of...

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Data science and Network Security (ICDSNS), International Conference on

作者： Muskan Yadav Bhavesh Vyas Paresh Pathak Rahul Bhatt Vaidehi Pareek Ritika Dhabliya Department of LAW Invertis University Bareilly Uttar Pradesh India Department of Electrical & Electronics Engineering School of Engineering & Technology K. R. Mangalam University Gurugram Haryana India School of Computer Science and Applications IIMT University Meerut India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand Symbiosis Law School Symbiosis International (Deemed University) Pune India Yashika Journal Publications Private Limited India

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Positive-Unlabeled Learning for Sentiment Analysis with Adversarial Training 15th

Positive-Unlabeled Learning for Sentiment Analysis with Adve...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Xu, Yueshen Li, Lei Huang, Jianbin Yin, Yuyu Shao, Wei Mai, Zhida Hei, Lei School of Computer Science and Technology Xidian University Xi’an710071 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China School of Computer Hangzhou Dianzi University Hangzhou310018 China School of Science RMIT University MelbourneVIC3001 Australia Xanten Guangdong Development Co. Ltd. Foshan528200 China Center of Journal Publication Xidian University Xi’anShaanxi710071 China

ISBN: (纸本)9783030301453

Sentiment classification is a critical task in sentiment analysis and other text mining applications. As a sub-problem of sentiment classification, positive and unlabeled learning or positive-unlabeled learning (PU learning) problem widely exists in real-world cases, but it has not been given enough attention. In this paper, we aim to solve PU learning problem under the framework of adversarial training and neural network. We propose a novel model for PU learning problem, which is based on adversarial training and attention-based long short-term memory (LSTM) network. In our model, we design a new adversarial training technique. We conducted extensive experiments on two real-world datasets. The experimental results demonstrate that our proposed model outperforms the compared methods, including the well-known traditional methods and state-of-the-art methods. We also report the training time, and discuss the sensitivity of our model to parameters. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Long short-term memory

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