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检索条件"机构=Cyber Security in Computer Science and Engineering"

共 2506 条记录，以下是71-80 订阅

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Anomaly-Based Detection of Log4j Attack in SDN

Anomaly-Based Detection of Log4j Attack in SDN

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Deshmukh, Pruthviraj Nitin Harish, R. Sangeetha, V. Praveen, K. TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Information Systems BITS Pilani India

ISBN: (纸本)9789819613472

The widely used Java library Log4j logging framework has lately come under attack from a major vulnerability known as CVE-2021–44228, also known as Log4Shell. Through the use of malicious log messages, the vulnerability enables attackers to compromise vulnerable systems by executing remote code. A key component of SDN systems is the RYU controller, an open-source software-defined networking (SDN) controller that provides a wide range of functionalities. It can communicate and interact with SDN-capable switches and other network elements. It is used in SDN installations for managing, controlling, and supporting network automation and orchestration. This paper proposes a new anomaly-based Log4j detection approach in SDN networks. We have used multiple machine-learning classifiers to analyze and detect anomalies in network traffic generated by Log4j attacks to get the best achievable accuracy for real-time model development. The conducted experiment results that the proposed approach can give high accuracy up to 98% in detecting Log4j attack in SDN environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： E-learning

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CNN Based Model for Underwater Image Classification and Enhancement

CNN Based Model for Underwater Image Classification and Enha...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Jamandlamudi, Sahit Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350371567

The challenge of enhancing underwater images is difficult because of several issues, including color distortion, inadequate illumination, and dispersion from suspended particles in the water. Conventional picture enhancing methods frequently find it difficult to resolve these problems. Thanks to its ability to learn intricate representations and pick up on minute features, deep learning has become a potent tool for picture improvement tasks in recent years. In this work, we suggest a novel deep learning-based method for improving underwater images. To automatically learn and improve the visual quality of underwater scenes, we use a convolutional neural network architecture that is specifically made to handle underwater photos. A sizable dataset of underwater photos is used to train the network, with an emphasis on contrast improvement and color correction. We compare our method with many state-of-the-art techniques and undertake extensive tests on a benchmark dataset to evaluate our approach. The outcomes show that our strategy performs much better than current approaches both in terms of subjective visual quality and objective criteria. Our technique improves visibility, minimizes noise, and successfully restores colors in underwater photos. By presenting a deep learning-based method that enhances the visual quality of underwater photographs, this work advances the field of underwater image processing and aids in marine research, underwater exploration, and surveillance. © 2024 IEEE.

关键词： Convolutional neural networks

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Advancing Alzheimer's Diagnosis Through Transfer Learning with Deep MRI Analysis

Advancing Alzheimer's Diagnosis Through Transfer Learning wi...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Adityasai, B. Babu, Nirmal Varghese Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India

ISBN: (纸本)9798350371567

Alzheimer's disease (AD) poses a significant health-care challenge, necessitating early and accurate diagnosis for effective management. This study presents a novel approach aimed at enhancing AD diagnosis through the integration of transfer learning, deep learning techniques, and meticulous data preprocessing methodologies. Leveraging a rich dataset from the Open Access Series of Imaging Studies (OASIS), comprising MRI scans from both healthy individuals and those with AD, the proposed method seeks to identify subtle abnormalities indicative of the disease. The methodology encompasses several key steps. Initially, MRI data undergoes rigorous cleaning and standardization using histogram equalization to ensure compatibility and remove irrelevant information. Subsequently, a two-step feature extraction process involving an autoencoder neural network is employed to identify significant AD-related features. These features are then combined with other relevant data to provide a comprehensive understanding of the disease. To optimize model efficiency, three algorithms iteratively refine the feature set, focusing on the most informative features associated with AD. Finally, a pretrained deep learning model, VGG19, is fine-tuned using the AD-specific dataset. Results demonstrate a significant improvement in diagnostic accuracy post-hyperparameter tuning, with accuracy increasing from 76.5% to 89.5%. The enhanced model performance across various metrics underscores the potential of the proposed approach to revolutionize AD diagnosis, offering promise for earlier detection and more accurate prognostication. This research contributes to advancing the field of neuroimaging-based diagnostic techniques for neurodegenerative diseases like Alzheimer's. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Enhanced Deep Learning Approaches for Robust Darknet Traffic Classification 3

Enhanced Deep Learning Approaches for Robust Darknet Traffic...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Etyang, Felix Pavithran, Pramod Mwendwa, Gideon Mandela, Ngaira Hillary, Musiime School of Engineering Cochin University of Science & Technology Department of Computer Science Cochin India School of Cyber Security and Digital Forensics National Forensics Sciences University Gandhinagar India School of Cyber Security and Digital Forensics National Forensics Sciences University Jinja Uganda

ISBN: (纸本)9798331518592

The classification of darknet traffic remains a pivotal task in modern cybersecurity efforts, necessitating advanced analytical techniques to address malicious activities pre-emptively. This study introduces and compares the efficacy of several deep learning models for classifying darknet traffic, including a Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and a novel deep learning model designed to enhance classification performance. Utilizing the CIC-Darknet2020 dataset, we implement a comprehensive evaluation using accuracy, precision, recall, and F1 score metrics. The results reveal that the proposed model surpasses traditional architectures, significantly improving classification accuracy and reliability, thereby contributing valuable insights to cybersecurity. © 2024 IEEE.

关键词： Adversarial machine learning

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Investigation of Handwritten Image-To-Speech Using Deep Learning

Investigation of Handwritten Image-To-Speech Using Deep Lear...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Manju, S. Anitha, J. Sujitha Juliet, D. Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

In the realm of accessibility technology, this paper introduces a pioneering method for converting handwritten images to speech. The work primarily focuses on recognizing handwritten text and subsequently converting it into audible speech. The work comprises two fundamental phases: initially, a deep learning algorithm is employed to recognize and extract text from handwritten images precisely. Following this, the recognized text is converted into speech using the pyttsx3 library which offers flexibility in voice selection, speech rate, and other parameters, making it highly customizable for different applications. To ascertain the most effective algorithm for the text recognition phase, a comparative analysis is conducted among the top three algorithms-Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models - based on their accuracy in recognizing handwritten text. Experimental results indicate that CNNs surpass the other two algorithms, achieving an accuracy of 92%. This work offers a pragmatic approach to converting handwritten content into speech, with potential applications in aiding the visually impaired and enhancing accessibility to written information. © 2024 IEEE.

关键词： Long short-term memory

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A Deepfake Image Classifier System for real and Doctored Image Differentiation 7

A Deepfake Image Classifier System for real and Doctored Ima...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Thomas, Juanith Mathew Ebenezer, V. Richard, Rohan Paul Karunya Institute of Technology And Sciences Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Sciences Division of Data Science And Cyber Security Coimbatore India

ISBN: (纸本)9798350359299

The sudden proliferation of deepfake technology has raised concerns about the authenticity and integrity of digital media. In response to these concerns, this paper presents a Deepfake image classifier system designed to differentiate between real and doctored images using "MesoNet"architecture which utilizes convolutional layers and builds a suitable neural network for classification. Making efficient use of Deep Learning strategies, this system aims to detect subtle manipulations indicative of deepfake alterations. The proposed model is trained on two distinct datasets: Dataset I comprises authentic images while Dataset II consists of images subjected to various levels of manipulation, making them deepfakes. Through extensive training using these images and their labels, we'll train the neural network to adequately predict the authenticity of an image. The model has been trained on exactly 7104 images and judging from the results, the predictions made by the model are fairly accurate. Results yield an accuracy of 88.81% and a precision of 87.93%. © 2024 IEEE.

关键词： Digital storage

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Knee Osteoarthritis Detection Using X-Rays and DNN 3

Knee Osteoarthritis Detection Using X-Rays and DNN

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sinha, Vaishnawi Veemaraj, Ebenezer Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India Division of Data Science and Cyber Security Tamil Nadu Coimbatore India

ISBN: (纸本)9798350375190

Knee osteoarthritis (OA) is a prevalent degenerative joint disease that affects millions worldwide, leading to pain, disability, and reduced quality of life. Early detection and intervention are crucial for effective management and prevention of disease progression. In this paper, we propose a novel approach for knee OA detection using X-ray images and deep neural networks (DNNs). Specifically, we investigate the performance of two popular DNN architectures: EfficientNet-B5 and DenseNet121. We begin by pre-processing a dataset of knee X-ray images to extract relevant features and mitigate noise. Subsequently, we fine-tune the pre-trained EfficientNet-B5 and DenseNet121 models using transfer learning techniques to adapt them to the task of knee OA detection. We then evaluate and compare the performance of these models in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Our experimental results demonstrate the efficacy of both EfficientNet-B5 and DenseNet121 architectures in knee OA detection, outperforming traditional machine learning approaches. However, we observe variations in their performance metrics, indicating nuanced differences in their ability to capture and discriminate pathological features in knee X-ray images. Furthermore, we conduct an in-depth analysis to understand the factors contributing to the performance disparities between the two models. Overall, our study contributes to the growing body of research aimed at leveraging DNNs for medical image analysis tasks, particularly in the domain of musculoskeletal disorders. The insights gained from this research can potentially aid clinicians in early diagnosis and personalized treatment planning for knee OA patients, ultimately improving patient outcomes and healthcare efficiency. © 2024 IEEE.

关键词： Grading

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Combating Terrorism: Detecting and Analyzing Extremist Content on X (Formerly Twitter) 3

Combating Terrorism: Detecting and Analyzing Extremist Conte...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Mwendwa, Gideon Chouhan, Lokesh Etyang, Felix Mandela, Ngaira Gift, Uwineza School of Cyber Security and Digital Forensics National Forensic Sciences University Goa India Cochin University of Science and Technology School of Engineering Department of Computer Science Cochin India School of Cyber Security and Digital Forensics National Forensic Sciences University Gandhinagar India School of Cyber Security and Digital Forensics National Forensic Sciences University Jinja Uganda

ISBN: (纸本)9798331518592

This study investigates the efficacy of machine learning algorithms in detecting and evaluating extremist content on social media platforms, and we employ X, formerly known as Twitter. We therefore utilize Kaggle's dataset that contains 83,000 entries from both extremist and non-extremist posts to compare the performance of four machine learning models, namely Support Vector Machine (SVM), Random Forest, Gradient Boosting, and Naive Bayes. Moreover, we examine a Stacking Classifier that leverages the advantages of all four models. The models were assessed using accuracy, precision, recall, F1 Score, ROC-AUC Score, training duration, and prediction time. The findings of our study revealed that the Support Vector Machine (SVM) algorithm was the most accurate and resilient, whereas Naive Bayes showcased remarkable computing efficiency, rendering it well-suited for real-time detection. The Random Forest and Gradient Boosting algorithms offered a well-balanced approach. Notably, the novel Stacking Classifier outperformed individual models, demonstrating the strength of a hybrid methodology. Hence our study presents a thorough structure for improving the identification of extremist material on social media, thereby providing essential knowledge for creating efficient, immediate detection systems to combat online extremism successfully. © 2024 IEEE.

关键词： Tweets

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Machine Learning for Detecting Subtle Signs of EyeDisease 4

Machine Learning for Detecting Subtle Signs of EyeDisease

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Dafny Neola, J. Ebenezer, V. Edwin, E.Bijolin Kirubakaran, S.Stewart Thanka, M.Roshni Yona Davincy, R. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Coimbatore India

ISBN: (纸本)9798350385793

Eye disorders can have serious repercussions, such as blindness or visual impairment, if they are not identified or addressed, Treatment that works and preventing permanent harm depend on early diagnosis and prediction. Using cutting-edge clinical observations, this method looks for diseases such diabetic retinopathy, cataracts, myopia, glaucoma, age-related eye illnesses, and hypertension-related eye disorders. This study presents an innovative method for early detection of eye health disorders: The Eye Health Station. It uses machine learning algorithm, by leveraging EfficientNet's superior performance in image classification tasks, our system analyzes ocular images, such as retinal scans, to identify potential indicators of eye disorders at an early stage., The proposed system seeks to discover possible markers of eye problems by evaluating several factors taken from ocular pictures and patient data, including retinal scans and medical history. The system is able to learn from various datasets and gradually increase its accuracy since machine learning techniques are included into the system. © 2024 IEEE.

关键词： Ophthalmology

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Extraction of Actionable Threat Intelligence from Dark Web Data 5

Extraction of Actionable Threat Intelligence from Dark Web D...

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5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Varghese, Varsha Mahalakshmi, S. Senthilkumar, K.B. ER&DCI Institute of Technology Cyber Forensics and Information Security Department of Computer Science & Engineering Thiruvananthapuram India Cyber Security Group Centre for Development of Advanced Computing Thiruvananthapuram India

ISBN: (纸本)9798350334128

Darknet has emerged as an excellent platform for cybercriminals to conduct various illicit activities such as operating a fully functional crypto-currency-based marketplace or maintaining a highly anonymous communication channel. However, it also acts as a source of cyber Threat Intelligence which is the information about the techniques, tactics, or motives of an emerging threat. The life cycle of a Darknet-based cyber Threat Intelligence solution mainly consists of the Collection, Processing, Analysis, and Production of Data from the Darknet. The Collection and Processing stages deal with accumulating information from various dark web pages and transforming the data into some format using a big data system. The Analysis part is integral to the Threat Intelligence schema since it constitutes the extraction of named entities like Organization, Offensive Activity, Tools, etc., and the determination of relationships between these entities. The production stage is like data mining where some knowledge is extracted that can be relevant to an organization under consideration. Such knowledge can mitigate risks and disrupt any targeted cyber-attack campaigns, making cyber Threat Intelligence actionable. In this research, an open-source-intelligence toolset is used to scan and collect data from the dark web forums through crawling and scrapping. The collected data is then ingested into a state-of-the-art NLP model that extracts actionable threat intelligence using Named Entity Recognition. The experimental results indicate that the model could identify HackerIds, tools, software, organizations, and other entities in the discussions of dark web forums with better efficiency and accuracy. This could be used to identify the source of a data leak, the release of new malware, evidence of a new exploit, and other offensive activities. © 2023 IEEE.

关键词： Extraction

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