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检索条件"机构=Dept. of Computer Information Technology and Graphics"

共 7141 条记录，以下是91-100 订阅

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Reconsider Time Series Analysis for Insider Threat Detection

Reconsider Time Series Analysis for Insider Threat Detection

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Chen, Chia-Cheng Pao, Hsing-Kuo National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350362480

Insider threat detection (ITD) presents a significant challenge in cybersecurity, particularly within large and complex organizations. Traditionally, ITD has been overshadowed by the focus of external threats, resulting in less attention and development in this critical area. Conventional ITD approaches often rely heavily on event-driven approaches. On top of that, researchers developed various rule-based methods to conquer the tasks. Based on that, we often ignore the intrinsic temporal relationships that are naturally built in between events that occur in different moments. For instance, we may easily understand events with causality such as one anomalous event followed by another specific event to complete a malicious action;however, may not be aware of events that occur around 9 am every morning during working hours. In our opinion, we attempt to re-consider the temporal behavior to extract the information hidden in cyberspace activities. Specifically, some effective sentence embeddings can assist us in providing informative internal representations to summarize temporal behaviors in the temporal activity sequences to make the right judgment on insider threat detection. In this paper, we propose a novel methodology for insider threat detection that emphasizes temporal relationship modeling on top of already-matured event sequence analysis to effectively catch insider threats. The proposed approach leverages contrastive sentence embeddings to learn users' intentions in sequences, followed by the deployment of a user-level and event-level Contrastive Learning (euCL) model to incorporate temporal behaviors with user behavior embeddings. To validate the proposed methodology, we conduct extensive analyses and experiments using the publicly available CERT dataset. The results demonstrate the effectiveness and robustness of the proposed method in detecting insider threats and identifying malicious scenarios, highlighting its potential for enhancing cybersecurity measur

关键词： Contrastive Learning

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Enhanced Adaptive Video Streaming for Resource-Constrained UAVs through Dynamic Region-of-Interest Tile Swapping 10

Enhanced Adaptive Video Streaming for Resource-Constrained U...

引用

10th International Conference on Applied System Innovation, ICASI 2024

作者： Hsu, Tz-Heng Ju, Rui-Han Horng, Gwo-Jiun Southern Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350394924

Real-time video transmission via unmanned aerial vehicles (UAVs) is significantly impacted by latency issues. Using Region of Interest (ROI) tile segmentation methods, video streaming techniques can dynamically adjust the encoding bitrate, assign various priorities to distinct video tiles, and specify different quality quantization factors. High-efficiency video tile encoding techniques, combined with ROI-based streaming methods, can meet various Quality of Service (QoS) criteria. However, dynamically assigning quantization factors for different tiles has the disadvantage of increasing the encoding system's complexity. This study presents a method to dynamically swap ROI tiles to lower the complexity of the encoding system, making it suitable for UAVs with limited computational capacities. By adopting the proposed adaptive video streaming transmission method with tile-swapping strategy, it is feasible to enhance the visual quality in critical areas for resource-constrained UAV devices. © 2024 IEEE.

关键词： Video streaming

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Navigating the Horizon Towards 6G Wireless Communication Networks

Navigating the Horizon Towards 6G Wireless Communication Net...

引用

2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Akinwunmi, Damilare Uche, Obinna Sokunbi, Onevemi Akinwumi, Hannah Awodele, Oludele Ajaegbu, Chigozirim Babcock University Dept. of Computer Science Ilishan-Remo Nigeria Babcock University Dept. of Information Technology Ilishan-Remo Nigeria

ISBN: (纸本)9798350376838

Sixth generation (6G) wireless communication is the next frontier. Researchers and business professionals are already focusing on the sixth generation of wireless technology, even though 5G is still being deployed globally. What can we anticipate from 6G, then? It's anticipated to be a dramatic advancement over 5G, providing previously unheard-of speeds, extremely low latency, and connectivity that extends well beyond laptops and smartphones. In our future, almost everything will be effortlessly connected;consider augmented reality, IoT gadgets, and more. Artificial intelligence integration is a crucial component of 6G. More effective resource management, improved user experiences, and network performance optimisation are all anticipated to be greatly aided by AI. The integration of AI with wireless communication is predicted to bring in a new age of intelligent, adaptable, and responsive networks. The pathway towards 6G wireless communication networks is thoroughly examined in this study, with an emphasis on possible innovations, difficulties, and game-changing potential. Also, this paper proposes an AI-driven framework for 6G networks, focusing on optimizing resource management, self-healing, and network security. It aims to improve real-time decision-making, reduce latency, and enhance fault recovery using machine learning. Simulations validate the framework's effectiveness and offer scalable solutions for 6G deployment. © 2024 IEEE.

关键词： 5G mobile communication systems

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Unleashing Password Potential: VAEs vs GANs in a Password Guessing Duel

Unleashing Password Potential: VAEs vs GANs in a Password Gu...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Salve, Siddhant Punn, Narinder Singh Dewangan, Deepak Kumar Agarwal, Sonali ABV-IIITM Dept. of Computer Science and Engg. Gwalior India Iiit Allahabad Dept. of Information Technology Prayagraj India

ISBN: (纸本)9798350376876

Insecure passwords remain a persistent vulnerability in cybersecurity. This study investigates the potential of employing Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) as tools for generating synthetic passwords, with the aim of enhancing existing password cracking methodologies. These models, trained on a dataset of leaked RockYou passwords, are evaluated based on their capacity to produce passwords that replicate the complexity and diversity observed in real-world passwords. Through a thorough assessment of the quality and characteristics of these synthetic passwords, this research reveals the strengths and weaknesses of each model. Additionally, the study examines the effectiveness of utilizing synthetic passwords in password cracking endeavors, comparing their performance to established methods and actual breached passwords. In conclusion, this research seeks to deepen our comprehension of password creation patterns and fortify our ability to counter the risks associated with weak passwords. By delving into the potential of VAEs and GANs, we contribute to the ongoing pursuit of robust cybersecurity measures and the safeguarding of sensitive information. © 2024 IEEE.

关键词： Generative adversarial networks

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Expressing Liminality in Data within the Context of Deep Learning 5

Expressing Liminality in Data within the Context of Deep Lea...

引用

5th International Conference on Computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2024

作者： Mehmeti, Fatjon Elmazi, Donald Kulla, Elis IT University Metropolitan Tirana Dept. of Computer Science Tirana Albania Fukuoka Institute of Technology Dept. of Information Management Fukuoka Japan

ISBN: (纸本)9798331534530

In this paper we describe the concept of Liminality and how it manifests in Feature Spaces of Data during processes of Deep Learning, with particular focus on data during transition, transformation and other states where Liminality manifests in Data Feature Spaces and between various dimensions, Hyper-planes and Decision Boundaries. To go about this, we approached a decision boundary and gave it a threshold, which would behave as an event horizon, that way data can be measured to be in that threshold or out of it. In our work, we discovered that deploying solutions, by consulting a metric of liminality leads to an ability to understand whether a model being used is appropriate, or not, by seeing how its rearrangement, affects the data's proximity to the threshold. In summary, this paper addresses the problem of lack of dept. in the study of the evaluation of a Deep Learning algorithm, through the use of a liminality metric which measures which data is subject to certain techniques like feature selection, and pruning, or models that select progressively narrower sections of data from a given feature space. © 2024 IEEE.

关键词： Adversarial machine learning

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LSB based Image Steganography with Password-Protected SMTP Transmission 2

LSB based Image Steganography with Password-Protected SMTP T...

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2nd IEEE Global Conference on Communications and information Technologies, GCCIT 2024

作者： Prakash, Mangali Bhanu Kumar, E. Ravi Anitha, Banavath Bansilal, Boda Mukkamall, Babu Reddy Sanjana, Gudipati Pranaya Vardhaman College Of Engineering Dept. of Information Technology Hyderabad India Krishna University Machilipatnam Dept. of Computer Application Machilipatnam India

ISBN: (纸本)9798350388916

Picture steganography plays a crucial role in secure communication by discreetly altering images for various purposes such as data authentication, privacy protection, and watermarking. However, traditional methods often lack resistance to manipulation. To address this, we propose a solution that merges automated password generation with the Least Significant Bit (LSB) approach. This method involves embedding text within images and automatically generating an encryption password to safeguard data security. By employing Simple Mail Transfer Protocol (SMTP) credentials, encrypted photos can be directly sent to recipients, significantly expediting the process. This automated approach eliminates the necessity for separate transfers of the image and decryption password, thereby enhancing simplicity and security. This new technology seamlessly integrates encryption, ensuring a trustworthy and secure communication channel. With this innovative solution, users can transmit sensitive information confidently, knowing that it is shielded from unauthorized access or manipulation. By combining the strengths of automated password generation, LSB steganography, and SMTP transmission, our approach provides a robust framework for safeguarding digital communication in various context. © 2024 IEEE.

关键词： Authentication Protocol

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An Efficient Detection of Floating Marine Debris using Deep Learning Techniques

An Efficient Detection of Floating Marine Debris using Deep ...

引用

2024 IEEE Bangalore Humanitarian technology Conference, B-HTC 2024

作者： Pandey, Prachi Guru Prasad, M.S. Mayank, Kumar Gowda, Mahima M Prashanth Kumar, S.P. Karthik, D.U. Dept. of Computer Science and Engg. Dehradun India REVA University School of Computing and Information Technology Bengaluru India Acharya Institute of Technology Dept. of Computer Science and Engg. Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Information Science and Engg. Bengaluru India

ISBN: (纸本)9798350344509

Growing concerns about the environmental consequences of floating debris in aquatic ecosystems have underscored the need for the development of efficient and automated methods for debris classification and monitoring. This research paper presents a comprehensive investigation into the application of neural networks, specifically Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and CNN-LSTM architectures, for this purpose. Our study entails the creation of a diverse dataset encompassing various debris types and environmental conditions, ensuring its real-world relevance and generalizability. Thorough exploration is conducted of the three neural network models to evaluate their effectiveness in classifying floating debris from images captured in aquatic environments. CNNs are chosen for their established image recognition capabilities, while LSTMs are incorporated to capture sequential information and potential temporal dependencies within debris trajectories. The outcomes of our study unveil the strengths and limitations of CNN, CNN-LSTM, and LSTM architectures in the context of floating debris classification. Proposed work provides insights into the suitability of each model in various real-world scenarios, including riverine systems, oceans, and coastal regions. Furthermore, we discuss the implications of our findings for environmental monitoring, debris removal strategies, and policy development. Throughout our research, each model's performance is assessed in terms of accuracy, precision, recall, and F1-score, considering the unique challenges posed by the inherently noisy and dynamic nature of aquatic environments. This research contributes to the evolving body of knowledge regarding the application of neural networks in environmental monitoring, with a particular focus on the critical domain of floating debris classification. Our findings are expected to guide the development of automated systems that can aid in mitigating the environmental i

关键词： Debris

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Harnessing Big Data for Economic Transformation in a Digitalized World 3

Harnessing Big Data for Economic Transformation in a Digital...

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3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Tarigan, Riswan Efendi Triyono Mahyuni Agustian, Harry Nurfaizi, Rifky Zahra, Achani Rahmania Az Pelita Harapan University Dept. of Information Systems Tangerang Indonesia University of Raharja Dept. of Information Technology Tangerang Indonesia University of Mataram Dept. of Teacher Training and Education Science Mataram Indonesia Sesindo Dept. of Computer Systems Tangerang Indonesia Bosindo Dept. of Information Technology Tangerang Indonesia Pandawan Sejahtera Indonesia Dept. of Information Systems Tangerang Indonesia

ISBN: (纸本)9798350367492

Air is essential to life and health, with oxygen (20.94%) and the ozone layer protecting against ultraviolet radiation. Depletion of oxygen reduces the ozone layer, increasing UV exposure. Good air quality is crucial for living organisms. In 2022, Indonesia's PM 2.5 pollution ranked 26th worst globally, exceeding WHO standards, yet awareness and trust in air quality devices remain low. This study aims to implement and evaluate an AI-based machine learning system to predict the Air Quality Index (AQI) in healthcare services. Using Structural Equation Modeling with Partial Least Square analysis via SmartPLS 4.0, the research involves 120 participants and historical air quality data. Comparative analysis of machine learning algorithms assesses accuracy, robustness, and efficiency, highlighting their potential in forecasting AQI and its implications for public health and urban planning. The study demonstrates the effectiveness of these algorithms in predicting AQI, with a significant value of 0.874 for forecasting accuracy. © 2024 IEEE.

关键词： Ozone layer

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Enhancing Network Intrusion Detection in a Limited Labeled Data Scenario: An Active Learning Approach

Enhancing Network Intrusion Detection in a Limited Labeled D...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Hossain, Md Sharafat Hossain, Md Al Amin Sikdar, Md Shahin Islam, Md Saiful Bangladesh University of Engineering and Technology Institute of Information and Communication Technology Dhaka Bangladesh Selcuk University Dept. of Information Technology Engineering Konya Turkey Prime University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

The Internet was originally developed to facilitate easier communication through digital devices. It has made our lives easier by providing means for communication and delivering digital resources anywhere and at any time in the world. However, the internet also exposes users to the risk of criminals exploiting various vulnerabilities in the internet system, launching sophisticated attacks to gain valuable information and hamper users' lives. Henceforth, an effective intrusion detection system (IDS) is imperative for safeguarding the data and resources of users in the cyber world. This research aims to enhance semi-supervised IDS models by leveraging limited labeled data and unlabeled data and exploring innovative approaches to improve adaptability and accuracy in detecting known and emerging threats, particularly in scenarios with limited labeled data available. Our study uses the UNSW-NB15 dataset, and the results show that our suggested active learning-based semi-supervised model, which was created with 2000 labeled data, is better than the supervised model that uses the same number of labeled data. The proposed model achieved an accuracy score of 97.67% while existing studies achieved an accuracy score of 94.61%. © 2024 IEEE.

关键词： Network intrusion

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Assessing the Accuracy of Regression and Classification Models in Air Quality Prediction 5

Assessing the Accuracy of Regression and Classification Mode...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Vishwakarma, Harsh Kumar Guru Prasad, M.S. Mittal, Vanshika Mittal, Mudit Naik, PavanKumar Chandrappa, S. Dept. of Computer Science and Engg. Dehradun India School of Computing and Information Technology Reva University Bangalore India Dept. of Computer Science and Engg. Bengaluru India

ISBN: (纸本)9798350356236

This research paper addresses the pressing issue of air quality monitoring and prediction using machine learning algorithms. With a dataset comprising pollutant levels from various states, we employed a comprehensive approach to data analysis, visualization, and preprocessing. The study aimed to predict the Air Quality Index related to key pollutants such as SO2, NO2, RSPM, and SPM. We utilized linear regression, decision tree regression, and random forest regression for prediction tasks and logistic regression, decision tree classification, random forest classification, and k-nearest neighbors for classification tasks. The proposed method was evaluated using different performance matrixes for regression and classification tasks, respectively. The results demonstrated the usefulness of the proposed models in predicting and classifying air quality levels. The proposed model provides a robust framework for air quality management and can be a valuable tool for environmental monitoring and policymaking. © 2025 IEEE.

关键词： Random forests

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