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Cybersecurity Threat Detection Using Deep Learning and Anomaly Detection Techniques

Cybersecurity Threat Detection Using Deep Learning and Anoma...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Rajendran, T. Mohamed Imtiaz, N. Jagadeesh, K. Sampathkumar, Balaji Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Nagarjuna College of Engineering and Technology Dept. of Information Science and Engineering Bangalore India Sri Venkateswaraa College of Technology Dept. of Computer Science and Engineering Chennai India Akshaya College of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359688

Through the use of combined deep learning and anomaly detection approaches, this research investigates the area of cybersecurity threat detection. The study proves the framework's extraordinary success in recognizing as well as mitigating diverse cyber dangers, including insider threats and zero-day attacks, through practical testing and case studies. The framework establishes itself as a proactive cybersecurity solution adaptive to the changing threat landscape due to its high accuracy of 95%, low amount of false positives 5%, and quick reaction times. The recommendations cover user training, scalability, regulatory compliance, including cross-disciplinary collaboration. Behavioral analysis, AI explainability, IoT security, and countering quantum computing concerns should be the main areas of future research. These revelations help the cybersecurity industry become more resilient and well-prepared. In this research investigation, experiments on threat detection were conducted using deep learning algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The findings of the study reveal that a detection accuracy of 93% was achieved by CNN, while a higher accuracy of 96% in threat detection was exhibited by RNN. © 2024 IEEE.

关键词： Zero-day attack

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IMAGE QUALITY ENHANCEMENT AND CAPTION GENERATION

IMAGE QUALITY ENHANCEMENT AND CAPTION GENERATION

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Saravanan, K. Swetha, M. SwethaSri, S. Renuka, N. Dept. of Information Technology RMK Engineering College Chennai India

ISBN: (纸本)9798350305708

Our brain is capable of labeling or annotating any image that is visible to us. But the machine does not have the capability of thinking on its own. To bringing it possible, we are employing Deep Learning Techniques. The aims of image captioning are to produce natural language expressions, to capture the relationship between the objects in the image, and to evaluate the credibility of the constructed descriptions. Uprooted features of the image are handled by Xception. Xception is a CNN model. These extracted features will be permitted by the LSTM model, and it will allow for the image subtitle. When the descriptions provided are just one word long, the object detection task for image captioning becomes more generic. But the problem arises when the image is of poor quality. The Images taken by a user under various lighting circumstances, Dots Per Inch (DPI), and different angles may have worse quality. We are utilizing several techniques, object identification, image classification, CNN, and OCR, to enhance the quality of *** Noise, Rescaling, Binarization/Thresholding, Removing Skewness/Deskew, and Morphological Operations are a few actionstaken during the image quality enhancement process. © 2023 IEEE.

关键词： Convolutional Neural Network Deep Learning Long Short-term Memory (LSMT) Optical Character Recognition (OCR)

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Enhanced Network Stability and Reliability using Software-Defined technology 8

Enhanced Network Stability and Reliability using Software-De...

引用

8th IEEE International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Nandhakumar, G. Arunkumar, M. Ifet College of Engineering Dept. of Information Technology Villupuram India

ISBN: (纸本)9798350347791

One of the most important qualities that a reliable network should have is the ability to withstand errors without being disrupted, as this is one of the most important aspects of the network. It is necessary for systems to have fault-tolerance solutions in order for such systems to have high availability and dependability. The proliferation of software-defined networking has led to the emergence of both new possibilities and new challenges for the development of novel fault-tolerance solutions, topologies, and standards. This has resulted in the emergence of both new possibilities and new challenges for the development of software-defined networking (SDN). In this research, we talk about the fault-tolerance that is offered by SDN as well as the assistance that OpenFlow offers for failure recovery and how it is related to the fault-tolerance that is supplied by SDN. During the process of failure recovery, the stages of detection and recovery that are used by carrier-grade networks are where the major focus of our attention is placed. In addition, we focus on the fault-tolerance challenges that are unique to SDNs and provide a complete account of the research that is presently being done on fault tolerance in SDNs. This research is being carried out in the United States. After that, discussing fault tolerance in SDN research organised according to the three unique levels of SDN. This follows the previous step (i.e., application,data and control). In conclusion, we will review the conversation by outlining some potential next steps for the development of fault-tolerant SDNs within the context of the research community. © 2023 IEEE.

关键词： Software defined networking

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Generative AI Solution for Lane Departure, Pedestrian Detection and Paving of Autonomous Vehicle 4

Generative AI Solution for Lane Departure, Pedestrian Detect...

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4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Sakure, Aditya Bachhav, Kiran Bitne, Chinmay Chopade, Vaibhav Dhavade, Vaishnavi Sangule, Umesh Sanjivani College of Engineering Dept. of Information Technology Kopargaon India

ISBN: (纸本)9798350393248

This research article offers a thorough analysis of how Deep Learning (DL) might improve the performance and safety of Autonomous Driving (AD) systems. This research paper discusses the development and implementation of a custom object detection algorithm and real-time detection of lane markings in autonomous driving systems. The algorithm incorporates YOLOv8 (You Only Look Once) and ResNet-34 as its backbone, known for their effectiveness in detection tasks. The paper chronicles the training of this algorithms on a diverse dataset, consisting of images captured from unique angles, varied lighting conditions, and disparate road surfaces. Furthermore, it introduces a machine learning model dedicated to the classification of different types of lane markings, as soon as they are detected. The research provides an insightful discourse on the algorithm's design, training, and implementation, paving the way for enhanced safety measures in autonomous driving. © 2023 IEEE.

关键词： Object detection

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Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator 4

Closed-Loop System Identification of an Unstable Nonlinear S...

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4th International Symposium on Instrumentation, control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Maneeloke, Benjaset Boonto, Sudchai Sanposh, Peerayot Tipsuwan, Yodyium Chinthaned, Natthawut Tanaprasitpattana, Warit Kasetsart University Dept. of Electrical Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Dept. of Control Systems and Instrumentation Engineering Bangkok Thailand Kasetsart University Dept. of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies. © 2025 IEEE.

关键词： Dynamic mode decomposition

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Smart Pain Relief Device for Varicose Veins using IoT & Arduino 4

Smart Pain Relief Device for Varicose Veins using IoT & Ardu...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Ananthi, N. Monish, B. Madhummitha Kirthika, P. Easwari Engineering College Dept. of Information Technology Chennai India

ISBN: (纸本)9798350300093

Varicose veins are a common disease that is mostly found in your legs. They become enlarged and twisted. Walking and even simply standing puts pressure on your legs, especially the veins. The majority of people, consider spider and varicose veins are only a concern cosmetically. The remaining portion, consider varicose veins to be painful and cause discomfort. Also, other serious problems can arise because of varicose veins. Close or removing veins are the procedures that can be done by doctors or other home care treatments that can help with varicose veins. Around 40 million people are affected by Varicose veins in the western part of the world. And for half of them, it occurs due to genetic reasons. The pervasiveness of varicose veins is around 55% in women and 45% in men. This research proposes monitoring the varicose patients and automatically giving the treatment whenever the patient needed it. © 2023 IEEE.

关键词： Patient treatment

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Review of S3 Data Summarization and Visualization

Review of S3 Data Summarization and Visualization

引用

2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： More, Krishna Jawale, Prathamesh Francis, Frason Narote, Amit Xavier Institute of Engineering Dept. of Information Technology Mumbai India

ISBN: (纸本)9798350335835

Storing and managing data has always been a challenge, but with the advent of AI and deep learning, the amount of data produced has grown dramatically, known as Big Data. The sheer volume of unstructured data, including files such as audio, video, images, and social media data, can make it difficult to effectively leverage the information. With so much data in S3, it becomes challenging for organizations to handle and process these documents in a timely manner. To effectively organize big data of text files, it is crucial to analyze and sort the data based on their specific domain. For this purpose, we will be using Natural Language Processing (NLP) extensively for preprocessing and analyzing the text data, which will be used in our topic modeling algorithm. With this solution, we aim to provide a text summary and important highlights visualization of data stored in the S3 Bucket. © 2023 IEEE.

关键词： Big data

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SAR Target Recognition Based on Decoupling and Reconstruction Learning Using Complex-Valued Convolutional Neural Networks 8

SAR Target Recognition Based on Decoupling and Reconstructio...

引用

8th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2023

作者： Qin, Jiang Zou, Bin Zhang, Lamei Ma, Zihao Dept. of Information Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350393590

Recent years, the deep convolutional neural networks (CNNs) based SAR target recognition methods are springing up. However, existing CNN-based SAR target recognition methods are limited by down-sampling caused information loss and feature redundancy, leading to learn shallow data correlations and bringing the performance collapse in practical applications of SAR target recognition. In this paper, a complex-valued decoupling and reconstruction learning (DRL-Net) based method is proposed to improve the SAR target recognition performance. DRL-Net learns the decoupling representation from the complex-valued images to make sure the learned latent feature is sparse and informative. The learned latent feature is then fed to reconstruction learning to keep the information completeness. Finally, the discriminative information is powered into the latent features to gain the semantic for SAR target recognition. Compared with baseline method, DRL-Net gains higher accuracy and faster convergence with the same number of samples. Additionally, DRL-Net exhibits a good potential for SAR target recognition with limited samples. ©2023 IEEE.

关键词： Semantics

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Classification of Chest X-Ray Images using Various Deep Learning Techniques to Identify Covid-19

Classification of Chest X-Ray Images using Various Deep Lear...

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2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Ananthi, N. Anjana, S. Dhanu Nagarajan, P. Barath Raj, D. Easwari Engineering College Dept. of Information Technology Chennai India

ISBN: (纸本)9798350348910

The healthcare community is deeply concerned about the novel coronavirus (2019-nCoV) that is spreading throughout the world. Since its discovery in late December 2019, the number of cases transferred from Wuhan, China to other countries has increased, causing ongoing changes in the medical environment. With the rapid surge of confirmed cases worldwide, initial focus centered on a Wuhan food market as the outbreak's source. However, a recent overview of primary clinical cases published in The Lancet challenges this hypothesis. Among the diverse strategies employed to curb the virus, closely monitoring its progression through international genomic sequencing has emerged as a crucial measure. controlling the virus's spread stands as an immensely challenging task, underscoring the necessity for highly accurate health surveillance to enable prompt responses. © 2023 IEEE.

关键词： Deep learning

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Detection and Classification of Aggressive Comments and Hate Speech

Detection and Classification of Aggressive Comments and Hate...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Nemade, Srushti Mane, Sunil B. Nandgaonkar, Sushma Dept. of Computer Engineering and Information Technology Maharashtra Pune India

ISBN: (纸本)9798350323917

The prevalence of social media has led to an increase in aggressive online behaviour such as cyberbullying, trolling, hate speech, and flaming. The anonymity and convenience of social media platforms have allowed individuals to express their aggressive tendencies without fear of retaliation. This issue is not limited to certain sections of society and has impacted people globally. Cyberbullies now have the power to affect the lives of millions of people worldwide, which can be detrimental to the internet's importance as a communication and networking tool. Therefore, it is critical to recognize and restrict this negative behaviour. It is essential to detect and prevent cyberbullying and hate speech in Indian native languages, considering India's diverse population and multiple languages. Social media platforms have brought about significant benefits, including improved communication and networking, but the increased use of these platforms has led to an increase in harmful behaviour that can negatively impact mental health. Additionally, misinformation and propaganda can cause severe consequences for society. This paper discusses the detection and classification of aggressive comments in Hindi-English code-mixed and Hindi data from popular social media platforms such as Facebook, Twitter, and Instagram. © 2023 IEEE.

关键词： Social networking (online)

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