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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

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Approaches for Identifying Suicide Ideation in Social Media Texts: Comprehensive Review 3

Approaches for Identifying Suicide Ideation in Social Media ...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Sonawane, Jayshri Suresh Jain, Dinesh Sage University Dept. of Computer Science and Engineering Indore India

ISBN: (纸本)9798350384598

In contemporary society, Suicide has become a notable concern within the realm of public health. Suicidal ideation (SI) encompasses thoughts about ending one's life. Factors contributing to suicidal thoughts and actions are diverse, ranging from personal life events to enduring emotional distress. We investigate the issue of identifying such ideation on Twitter, emphasizing the usefulness of a collection of characteristics taken from the literature as well as the function of discussion context in this process. Numerous machine learning algorithms have been put forth to identify SI from internet channels, but because feature engineering and classification techniques are still lacking, the accuracy of these algorithms is still low. Therefore, by using novel deep learning and natural language processing-based feature engineering technique accuracy can be increased. © 2024 IEEE.

关键词： Long short-term memory

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KoDialogBench: Evaluating Conversational Understanding of Language Models with Korean Dialogue Benchmark 30

KoDialogBench: Evaluating Conversational Understanding of La...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Jang, Seongbo Lee, Seonghyeon Yu, Hwanjo Dept. of Computer Science and Engineering POSTECH Pohang Korea Republic of

ISBN: (纸本)9782493814104

As language models are often deployed as chatbot assistants, it becomes a virtue for models to engage in conversations in a user's first language. While these models are trained on a wide range of languages, a comprehensive evaluation of their proficiency in low-resource languages such as Korean has been lacking. In this work, we introduce KoDialogBench, a benchmark designed to assess language models' conversational capabilities in Korean. To this end, we collect native Korean dialogues on daily topics from public sources, or translate dialogues from other languages. We then structure these conversations into diverse test datasets, spanning from dialogue comprehension to response selection tasks. Leveraging the proposed benchmark, we conduct extensive evaluations and analyses of various language models to measure a foundational understanding of Korean dialogues. Experimental results indicate that there exists significant room for improvement in models' conversation skills. Furthermore, our in-dept. comparisons across different language models highlight the effectiveness of recent training techniques in enhancing conversational proficiency. We anticipate that KoDialogBench will promote the progress towards conversation-aware Korean language models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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EVALUATION OF SEAQUAKE RESPONSE AND STRUCTURAL FAILURE OF PONTOON-TYPE FLOATING OFFSHORE WIND TURBINES

Journal of Structural and Construction Engineering

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Journal of Structural and Construction engineering 2024年第826期89卷 1471-1482页

作者： Fujita, Ken-Ichi Yashiro, Harumi Dept. of Engineering Faculty of Engineering Nagasaki Institute of Applied Science Graduate School of Engineering Nagasaki University Japan School of Systems Engineering National Defense Academy of Japan Japan

An evaluation method of seaquake response of pontoon-type offshore wind turbines using stationary random vibration theory is presented. Seabed motion in the vertical direction due to an earthquake is modeled as a stationary random process. A BE-FE hybrid model is used for the response analysis. The excitation surface on the seabed is discretized by boundary elements. The response results are used to structural failure probability and the probability is analyzed using a first-order reliability method. Numerical examples show the variety of the responses and the failure probability of structures for class of soil, seismic acceleration and epicentral distance. © 2024 Architectural Institute of Japan. All rights reserved.

关键词： Offshore wind turbines

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The Development of a Digital Forensic Framework for Ease of Forensic Analysis 12

The Development of a Digital Forensic Framework for Ease of ...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Suvarna, Dhruv Km, Mahesh Gupta, Mahika Gabburi, Sonal Honnavalli, Prasad Vm, Sapna Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350330366

With the growing use of digital devices and technologies, the volume of digital forensic evidence in need of analysis also increases. This results in a need for more effective methods to conduct analysis of this evidence in an investigation. This paper discusses the development of a framework for digital forensic processes. The framework will utilize various tools and techniques to enhance the process of collecting, analyzing, and preserving digital evidence. A survey of existing digital forensic tools, including FTK Imager, Autopsy, and The Sleuth Kit was conducted to help identify various pain points faced by investigators throughout the forensic lifecycle. Based on this study, a framework for the analysis of storage data and memory dumps in digital forensics is proposed, incorporating industry best practices and tools such as The Sleuth Kit and Volatility, enhancing ease of use, and thereby promoting more accurate resolutions. © 2024 IEEE.

关键词： Digital forensics

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Breast Cancer Detection Based on Histopathological Images Using Vision Transformers

Breast Cancer Detection Based on Histopathological Images Us...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Alkhater, Rozah Hassan M. Al-Maadeed, Somaya Saleh, Moutaz Akbari, Younes Qatar university Dept. Computer Science and Engineering Doha Qatar

ISBN: (纸本)9798350367560

Breast cancer is a prevalent and potentially fatal malignancy in women, requiring continual improvements in diagnostic techniques. In order to help medical personnel accurately detect dangerous tumors, automated medical image analysis is essential. This paper presents a unique breast cancer detection model that makes use of deep learning and transfer learning approaches in order to improve the accuracy of diagnostics. The pre-trained ViTb-32 model is fine-tuned on a pre-processed dataset to produce cutting-edge results. The model was trained with a public histopathologic breast cancer dataset. The dataset is pre-processed to resize the images and normalize them to enhance the performance during the training and achieve convergence faster. The ViTb-32 model is optimized by fine-tuning the hyperparameters to achieve the best possible accuracy. The proposed model achieves an accuracy rate of 96.92%. The application of this paradigm in the clinical setting has the potential to improve patient outcomes by facilitating early detection and timely intervention, which could reduce the mortality rate of breast cancer patients by detecting the disease at an early stage. © 2024 IEEE.

关键词： Lung cancer

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Systematic Analysis of Underwater Image Dehazing and Object Detection 6

Systematic Analysis of Underwater Image Dehazing and Object ...

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6th Asia Symposium on Image Processing, ASIP 2024

作者： Ghei, Anushka Krishnan, Dhriti Rajesh Santhosh, Gautam Natarajan, S. PES University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376159

Dehazing underwater images is crucial for several underwater applications, including marine biology, exploration, and monitoring. Because of colour distortion, light attenuation, and scattering, underwater imaging presents particular difficulties. This research presents a systematic survey on using transformers and different Generative Adversarial Networks (GANs) for dehazing underwater photos. This survey study looks into transformer-based models, analyzes GAN designs (CycleGAN, Pix2Pix, and UWGAN) in detail, and compares them using a range of metrics, such as Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), Frechet Inception Distance (FID), and F1-score. Further analysis was carried out using CLAHE on the best-performing models. Our findings emphasize the benefits and drawbacks of the different designs that were employed as well as prospective directions for further study and advancement. © 2024 IEEE.

关键词： Underwater photography

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Deep Dive into Predictive Modeling for Autism Spectrum Disorder using Facial Features and Machine Learning

Deep Dive into Predictive Modeling for Autism Spectrum Disor...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Bhat, Chaitra Goutham, Gagan Bannur, Charvi Kakarla, Sada Mamatha, H.R. Pes University Dept. Of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350352689

Autism Spectrum Disorder is a neuro-development behavioural disorder that is classified as a chronic disability leaving individuals unable to accurately grasp their environment and can lead to limited development of communication skills, thinking and other social skills. Prediction of ASD using video recordings is an open-ended critical problem in machine learning to facilitate the process of early intervention which requires high computational resources. This research aims to predict ASD in children using videos by extracting features and converting them into a binary classification problem thereby eliminating the need for high computational resources. This research postulates a study of machine learning techniques like Logistic Regression, Decision Trees and Neural Networks in comparison for the prediction of ASD as well as a novel approach to reduce the computational requirements for the analysis of videos. This research also addresses the lack of publically available video datasets for the analysis of Autism. The highest accuracy metric for the models was using the Neural Network architecture closely followed by the Decision Tree Classifier which yielded accuracies of 99.8% and 99.65% respectively. In this article, we offer an overview of predicting Autism Spectrum Disorder whilst focusing on the importance of video datasets. © 2024 IEEE.

关键词： Forecasting

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Call Translator with Voice Cloning Using Transformers 9

Call Translator with Voice Cloning Using Transformers

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Khan, Nawaz Fateen Hemanth, Nandan Goyal, Navyae Pranav, K.R. Agarwal, Pooja Pes University Computer Science Engineering Dept. Bangalore India

ISBN: (纸本)9798350394474

Voice Cloning employs technology and algorithms to create an artificial or synthetic reproduction of a person's voice. To understand and mimic the distinct vocal qualities of the target speaker, including tone, pitch, cadence, and pronunciation, a machine learning model must be trained on audio samples of the speaker. This project presents itself as a revolutionary approach to the enduring problem of successful cross-lingual interactions, the paper's novel approach to speech-to-speech machine translation that incorporates voice cloning. This method allows us to synthesize speech in the target language while maintaining the original speaker's vocal characteristics. The model algorithms such as Whisper AI(WER 5% for transcription, No-Language-Left-Behind-200(44% more accurate than other models) for translation, Tacotron for speech synthesis and multiple transformers for voice cloning will be incorporated. The Voice Cloning model operates effectively on unseen voices, transcending the limitations of voice cloning will be incorporated. The voice cloning model operates effectively on unseen voices, transcending the limitations of relying solely on pre-trained models. The model successfully clones users' voices in a different language with an approximate latency of 10 seconds. © 2024 IEEE.

关键词： Speech synthesis

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Preprocessing and Feature Extraction based Deepfake Detection on Combined dataset 2

Preprocessing and Feature Extraction based Deepfake Detectio...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Thejas, N.U. Nayak, Hithesh D. Siddique, Abbu Bucker Anas Danish, Mohammed Mamatha, H.R. Pes University Computer Science & Engineering Dept. Bengaluru India

ISBN: (纸本)9798350360523

There are many deepfake detection models available, each with their own limitations. This research concentrates on one such limitation and addresses it. That research gap would be the inefficient behavior of the deepfake detection model on the orientation of faces in the input image or video. The architecture starts by combining the data sets of DFDC, Celeb-DF, and Face Forensics++. Then frame-extraction and pre-processing on the frames take place. The next stage is crucial and differs from other models;it is the feature extraction stage, where specific face-orientation-related features are extracted from each frame. The features that are extracted are a histogram of oriented gradients, local binary patterns, and scale-invariant feature transforms. Later on in the architecture, normalization of features happens in independent neural network pipelines. Then features are combined and passed onto the next neural network, which will classify the videos as deepfake or not. The validation accuracy of this architecture is 95.89%. © 2024 IEEE.

关键词： Feature extraction

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