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Quantum computing in Cybersecurity and LLM

Quantum Computing in Cybersecurity and LLM

引用

international conference on Web Research (ICWR)

作者： Fazel Mohammad Ali Pour Mohammad Hossein Arastoo Faculty of Technology and Engineering Behbahan Khatam-Alanbia University of Technology Khuzestan Iran Faculty of Technology and Engineering Azad University of Kashan Kashan Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Quantum computing is fundamentally transforming the cybersecurity landscape by simultaneously introducing robust defense mechanisms and sophisticated attack vectors. This paper explores the dual nature of quantum computing in cybersecurity, examining how it enhances artificial intelligence-powered malware detection systems and strengthens encryption protocols, while also enabling the development of adaptive quantum-enhanced threats such as polymorphic malware and quantum key distribution attacks. Additionally, the paper analyzes the convergence of quantum computing with Large Language Models (LLMs) and neural networks, highlighting implications for threat intelligence across social media platforms, cybersecurity operations, and open-source intelligence. Through comprehensive analysis and the proposition of quantum-resilient security frameworks, this research provides cybersecurity professionals with actionable strategies for navigating the emerging quantum computing era.

关键词： Quantum computing Protocols Social networking (online) Navigation Neural networks Vectors Encryption Quantum key distribution Ransomware Computer security

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Voice Pathology Detection Using Decision Tree Classifier 14

Voice Pathology Detection Using Decision Tree Classifier

引用

14th international conference on Information and Communication technology convergence, ICTC 2023

作者： Al-Dhief, Fahad Taha Mu'azzah Abdul Latiff, Nurul Nik Abd. Malik, Nik Noordini Mat Baki, Marina Sabri, Naseer Abbood Albadr, Musatafa Abbas Fariesya Suhaila Md Sazihan, Nurul Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Bahru Malaysia Universiti Kebangsaan Malaysia Medical Centre Faculty of Medicine Kuala Lumpur Malaysia Sohar University Computing and Information Technology Sohar Oman Basrah University for Oil and Gas Department of Oil and Gas Engineering Al Basrah Iraq

ISBN: (纸本)9798350313277

Nowadays, the systems of voice disorder detection obtained considerable attention due to the high importance of this field. However, the assessment of voice pathology requires certain tools and well-trained doctors. Moreover, this assessment can be identified by a group of professionals who listen to a patient in order to assess the patient's speech to identify whether the patient's voice is pathological or normal. Nevertheless, this assessment is based on the listener's experience. Consequently, machine learning is the most suitable technique for the detection of voice pathology, where this technique is a cost-effective and non-invasive method. Therefore, this paper presents Decision Tree (DT) algorithm based on the Mel-Frequency Cepstral Coefficient (MFCC) technique for the detection of voice pathology. The voice samples for pathological and healthy classes are collected and taken from the Saarbrucken Voice Database (SVD). The performance of the DT algorithm is assessed in terms of many evaluation measurements such as accuracy, sensitivity, precision, G-mean, F-measure, and specificity. © 2023 IEEE.

关键词： DT machine learning MFCC SVD voice pathology

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Enhancing In-Vehicle Network Security Through Bitstream Feature Extraction-Based Intrusion Detection 2023

Enhancing In-Vehicle Network Security Through Bitstream Feat...

引用

15th international conference on Contemporary computing, IC3 2023

作者： Islam, Md Rezanur Ugli, Muminov Ibrokhim Botir Oh, Insu Yim, Kangbin Department of Software Convergence Soonchunhyang University Asan-si Chungnam Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan-si Chungnam Korea Republic of Department of Information Security Engineering Soonchunhyang University Asan-si Chungnam Korea Republic of

ISBN: (纸本)9798400700224

The in-vehicle network (IVN) is highly vulnerable due to its inherent structure, and the continuous introduction of new features in next-generation vehicles only exacerbates this issue. To address this problem, a proposed intrusion detection approach for in-vehicle networks is based on bitstream feature extraction and autocorrelation analysis. This approach aims to overcome the limitations associated with memory utilization and processing overhead vulnerability by comparing payload features using nibble. The Controller Area Network (CAN) data is highly susceptible, and organizations worldwide are working on developing Intrusion Detection Systems (IDSs) to protect vehicles. Nibbles are four-bit units of digital information, and autocorrelation measures the correlation of a variable with itself over time. In the proposed approach, the CAN payload is processed using nibble feature extraction and fed into a deep-learning CNN for attack classification. This approach presents a promising solution for enhancing the security of in-vehicle networks, by utilizing advanced machine-learning techniques to improve the security of vehicle systems. © 2023 ACM.

关键词： Feature extraction

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Smart Teacher - An AI-Based Self-Learning Platform for Grade 10 ICT Students 7

Smart Teacher - An AI-Based Self-Learning Platform for Grade...

引用

7th IEEE international conference for convergence in technology, I2CT 2022

作者： Jathushan, V. Senthujan, B. Ajiharan, B. Kasthury, M. Gamage, Anjali Ganegoda, Devanshi Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9781665421683

E-learning is a method of learning that uses electronic resources but is based on institutionalized education. While education can take place in or outside of the classroom, E-learning relies heavily on computers and the Internet. The smart Teacher is a self-learning platform for ICT students that is powered by artificial intelligence. The study's purpose is to enhance teaching and learning functions by allowing for the creation and storage of learning materials, as well as their availability and accessibility. We concentrated our research on the Grade 10 ICT topic. We created a web application that Grade 10 students can use to self- We created a web application that Grade 10 students can use to self-teach and self-assess their ICT skills, and it will be free. It will result in a higher pass rate for fundamental ICT *** can ask questions on the main page (Image). We assigned ratings to the responses provided by different users and automatically ordered the responses based on the ratings. We'll offer real-time cautions about unrelated questions. Simultaneously, we added a chatbot that will provide related site recommendations as well as real-time website instructions. It will listen to the students' queries (voice or sound) and then propose relevant questions in text form that the students can utilize. © 2022 IEEE.

关键词： Students

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Skeleton Based Periodicity Analysis of Repetitive Actions 7

Skeleton Based Periodicity Analysis of Repetitive Actions

引用

7th IEEE international conference for convergence in technology, I2CT 2022

作者： Wedasingha, Nushara Samarasinghe, Pradeepa Seneviratne, Lasantha Puiatti, Alessandro Papandrea, Michela Dhanayaka, Dulangi Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka Switzerland Switzerland University of Colombo Faculty of Medicine Colombo Sri Lanka

ISBN: (纸本)9781665421683

This paper investigates the problem of detecting and recognizing repetitive actions performed by a human. Repetitive action analysis play a major role in detecting many behavioral disorders. In this work, we present a robust framework for detecting and recognizing repetitive actions performed by a human subject based on periodic and aperiodic action analysis. Our framework uses focal joints in the human skeleton for the analysis of repetitive actions which are substantiated by the principles of human anatomy and physiology. Using Non-deterministic Finite Automata (NFA) techniques, in this paper, we introduce a novel model to transform repetitive action count to differentiate the periodicity in human action. Experimental results on a dataset consisting of 371 video clips show that our algorithm outperforms the state-of-art (RepNet) [1] in simultaneous multiple repetitive action counts. Further, while the proposed model and RepNet give comparable results in counting periodic repetitive actions, our model performance surpass RepNet significantly on analysing non-periodic repetitive behavior. © 2022 IEEE.

关键词： Musculoskeletal system

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Surveillance based Child Kidnap Detection and Prevention Assistance 7

Surveillance based Child Kidnap Detection and Prevention Ass...

引用

7th IEEE international conference for convergence in technology, I2CT 2022

作者： Kodikara, K.A.O.V. Hettiarachchi, Pramodya Prathapa, D.M.J. Jayakody, J.M.A.M.S. Haddela, Prasanna S. Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka

ISBN: (纸本)9781665421683

This research paper is based on child kidnap detection and prevention to identify susceptible child kidnap by unauthorized persons. The intelligent surveillance system proposed for this is known as "AICare". The purpose behind developing a proper kidnap detection methodology is to enhance and strengthen the existing child security systems. The key is to identify the main characteristics of a kidnapper in real-time, which follows face recognition, speed detection and object detection theories. Face recognition is used to identify whether the outlined individual has covered his body, especially his face, to hide the true identity, or else the person's face is directly processed for authentication. Speed detection is helpful in calculating movement speed and capturing whether the targeted individual is moving in a hurry. Finally, the stranger is subjected to object detection in order to classify whether he/she is handling a sharp object or not. The captured outcomes are subjected to a decision tree to resolve the person as a kidnapper suspect. The system results in an overall accuracy that is above 90%. As this solution is child-sensitive and responsive, it provides a long term platform that can real-time monitor for potential kidnap based on the kidnapper characteristics to support the working from home parents to take care of their children during crucial times. © 2022 IEEE.

关键词： Object detection

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Nonmonotone Accelerated Manifold Proximal Gradient with Adaptive Stepsize Method for Sparse PCA with Application to Dimensionality Reduction

Nonmonotone Accelerated Manifold Proximal Gradient with Adap...

引用

2022 international conference on Image Processing and Media computing, ICIPMC 2022

作者： Zhang, Xinyu Yan, Tao Wang, Kai Nanjing University of Science and Technology School of Mathematics and Statistics Nanjing China

ISBN: (数字)9781665468725

ISBN: (纸本)9781665468725

Sparse principal component analysis (SPCA) has attracted attention in many areas such as signal/image processing, statistics, bioinformatics and machine learning. In this paper, we develop a new accelerated proximal gradient method on Stiefel manifold for SPCA. We apply the nonmonotone accelerate technique which can reduce the iteration and improve the convergence rate of algorithm. Numerical experiments show that the proposed method can get better results for some test data. © 2022 IEEE.

关键词： Numerical methods

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KnowMore: Social Media based Student Centric E-learning platform with Machine Learning Approaches 7

KnowMore: Social Media based Student Centric E-learning plat...

引用

7th IEEE international conference for convergence in technology, I2CT 2022

作者： Nasome, Vidusha Malavige, Oshani Costa, Maleesha Jayasinghe, Bhagya Karunasena, Anuradha Samarakoon, Uthpala Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9781665421683

Social media has become increasingly popular among the younger generation in the last decade. Students engage with social media on daily basis, and it affects their interests, lifestyle, and attitude. There are many existing e-learning applications used by higher educational institutes, but such applications are mainly focused on delivering teaching content rather than facilitating active and interactive learning. This paper proposes a novel e-learning platform to create an active and interactive learning environment for students leveraging social media strategies, especially those of 'Facebook.' The objective of this platform is to promote self-motivation, self-learning, and interaction. The platform features were built on considering three aspects important for learning, which are personal knowledge management, learning management, and collaborative learning. Features of the proposed platform that it comprises are Newsfeed, Classmates, Profile, Cluster, Repository, Knowledgebase, Bookmark, Topic Map, Search Engine, Test Mark Prediction, and Slide Show Summary generator. Machine Learning techniques and Natural Language Processing were used to build some of the platform features. The feedback collected on the proposed system, 'KnowMore,' shows that the satisfaction of the students has increased with the system. © 2022 IEEE.

关键词： Students

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Decorrelation Proportionate Normalized Least Mean M-estimate Algorithm with Variable Step-Size 22

Decorrelation Proportionate Normalized Least Mean M-estimate...

引用

5th international conference on Signal Processing and Machine Learning, SPML 2022

作者： Huang, Zongxin Yu, Yi Fan, Yongcun Li, Ke School of Information Engineering Robot Technology Used for Special Environment Key Laboratory of Sichuan Province Southwest University of Science and Technology Sichuan Mianyang China

ISBN: (纸本)9781450396912

To improve the performance of the proportionate normalized least mean M-estimate (PNLMM) algorithm, this paper proposes a variable step-size adaptive decorrelation PNLMM (VSS-AD-PNLMM) algorithm. First, an adaptive decorrelation scheme is designed to speed up the algorithm convergence when aiming at highly correlated input signals. Second, by minimizing the a posterior error, we derived a variable step-size mechanism to solve the trade-off between fast convergence rate and low steady-state misadjustment. Computer simulations in impulsive noises are conducted to demonstrate the effectiveness of the proposed algorithm. © 2022 Association for computing Machinery.

关键词： Impulse noise

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Multivariate Deep Learning Model For Workload Prediction In Cloud computing 12

Multivariate Deep Learning Model For Workload Prediction In ...

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12th international conference on ICT convergence (ICTC) - Beyond the Pandemic Era with ICT convergence Innovation

作者： Dang-Quang, Nhat-Minh Yoo, Myungsik Soongsil Univ Dept Informat Commun Convergence Technol Seoul South Korea Soongsil Univ Sch Elect Engn Seoul South Korea

ISBN: (纸本)9781665423830

Many application developers are now choosing to install their Web applications on cloud data centers because of the attractiveness of cloud computing environment. Predicting future resource workload is critical since it allows cloud service providers to automatically modify resources online in order to meet service level agreements (SLA). This paper proposes a multivariate deep learning prediction model to predict future resource workload for cloud computing environment. The prediction model uses a special type of recurrent neural network (RNN) called Bidirectional long short-term memory (Bi-LSTM). This work also explains and shows the advantage by using multivariate data compared to univariate data in time series forecasting. The experiments, using real world workload dataset, show that the proposed multivariate Bi-LSTM model outperforms the univariate Bi-LSTM model in prediction accuracy.

关键词： Workload prediction time series forecasting cloud computing

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