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Secure container Orchestration: A framework for detecting and mitigating Orchestrator - level vulnerabilities

Multimedia Tools and Applications 2024年 1-21页

作者： Mahavaishnavi, V. Saminathan, R. Prithviraj, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Chidambaram India

This paper presents Secure Orchestration, a novel framework meticulously planned to uphold rigorous security measures over the profound security concerns that lie within the container orchestration platforms, especially in Kubernetes. The widespread adoption of containerized environments has raised concerns about the vulnerabilities at the level of the orchestrator. Secure Orchestration delivers a comprehensive approach by leveraging cutting-edge methods to detect and prevent the exposure of vulnerabilities from orchestrator misconfigurations, privilege escalation exploits that can thwart the orchestration security layer, and unauthorized access attempts targeting the orchestration system itself. In addition, it is further strengthened by deploying an Intrusion Detection and Prevention System (IDPS), which is responsible for actively monitoring the orchestration infrastructure. The IDPS utilizes sophisticated algorithms and mechanisms for detecting anomalies to continuously assess the orchestration environment for indications of intrusion, vulnerabilities, and anomalous activities. By using this proactive approach, the framework is able to rapidly identify potential threats and respond through countermeasures in a timely manner, thus greatly reducing the risks associated with orchestrator level vulnerabilities. Conducting a set of real-world experiments that closely simulate a variety of threat scenarios is imperative for empirically validating the efficiency of this framework. The obtained empirical results yield insightful revelations on the efficacy of the framework in accurately identifying and mitigating vulnerabilities, thereby ensuring the privacy and integrity of container orchestration systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Containers

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Devnagari Character Recognition using Optical Character Recognition (OCR) 1

Devnagari Character Recognition using Optical Character Reco...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Shukla, Ananya Sharma, Ayush Aggarwal, Akriti Jain, Shikha Abes Engineering College Computer Science Engineering India Abes Engineering College India

ISBN: (纸本)9798350338027

In the age of digital applications, optical character recognition (OCR) is a useful tool. There are many different languages and scripts used around the world, with more than 300 million individuals in India speak Hindi as their first language. OCR previously created for the Devnagari script has a very low recognition rate. In this paper, we propose an OCR system based on a Convolutional Neural Network (CNN) model. It increases the handwritten data's efficiency or accuracy by 98.32%. We do several steps for getting this efficiency like preprocessing of data, grayscale images, binarization, reshaping, normalization, modelling, and Evaluation of the data with the help of plotting. © 2023 IEEE.

关键词： Optical character recognition

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PG Radar

PG Radar

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Grover, Yash Aditya Agarwal, Kadambari Department of Computer Science and Engineering ABES Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9781032669663

The usage of mobile technology and applications in the accommodation industry has grown in popularity, and the PG Radar app is an example of an android-based paying guest app that tries to meet this requirement. PG Radar is an Android software that assists users in locating suitable and authentic paying guest rooms. Additionally, paying guest owners can promote their lodgings, and users can search for paying guest accommodations, apartments, and mansions in Ghaziabad’s Crossing Republik. Users can look for lodging based on certain parameters such as reviews, meals, rent, security, and so on. Overall, the goal of PG Radar is to assist paying guest owners in better managing their communities and related activities, as well as to provide a convenient alternative for persons seeking temporary housing. This app simplifies the process of searching for housing and eliminates the need to spend money on housing brokers and aims to make the process of finding temporary housing more convenient and cost-effective. [8]. © 2024 The Author(s).

关键词： Housing

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MLRT-UNet:An Efficient Multi-Level Relation Transformer Based U-Net for Thyroid Nodule Segmentation

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computer Modeling in engineering & sciences 2025年第4期143卷 413-448页

作者： Kaku Haribabu Prasath R Praveen Joe IR Department of Computer Science and Engineering RMK College of Engineering and TechnologyTiruvallur601206India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.

关键词： Thyroid nodules endocrine system multi-level relation transformer U-Net self-attention external attention co-operative transformer fusion thyroid nodules segmentation

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Enhanced Clickbait Detection through Ensemble Machine Learning Techniques

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Procedia computer science 2025年 258卷 599-608页

作者： Neetu Sardana Deepika Varshney Seema Luthra Department of Computer Science & Engineering-Data Science ABES Engineering College Ghaziabad India

Nowadays online news websites are one of the quickest ways to get information. However, the credibility of news from these sources is sometimes questioned. One common problem with online news is the prevalence of clickbait. Clickbait uses exaggerated headlines to lure people to click the suspected link, but the content often disappoints the reader and degrades user experience it may also hamper public emotions. The proposed work aims to examine diverse set of models for clickbait detection. The models are formed by integration of Machine learning (ML) and Ensemble learning methods (EL) with Term Frequency and Inverse Document Frequency (TF-IDF) & Embedding technique. Five ML and three EL are analysed &compared. Random Forest along with TF-IDF gave the best results of 85%. The resultant model shows significant improvements with a minimal false-positives.

关键词： Clickbait Detection Machine Learning text classification NLP Ensemble learning Stacking classifir

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Enhancing Malware Detection Using Machine Learning: A Comparative Study of Classification Algorithms 4

Enhancing Malware Detection Using Machine Learning: A Compar...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Singh, Shalini Singh, Suraj Pal Singh, Manu Abes Engineering College Department of Information Technology UP Ghaziabad India Chandigarh University Department of Computer Science and Engineering Punjab Mohali India Abes Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350364729

This research investigates the efficacy of various machine learning algorithms in detecting malware. Utilizing a dataset of 20,000 observations with 33 features,28 classification algorithms are applied and evaluated their performance based on accuracy, balanced accuracy, ROC AUC, F1 score, and execution time. Our results indicate that Extra Trees Classifier, XGB-Classifier, Bagging Classifier, and DecisionTreeClassifier achieved perfect accuracy, with Decision Tree Classifier also demonstrating optimal execution efficiency. This study underscores the potential of decision tree-based models for efficient and accurate malware detection and suggests further exploration with larger datasets and diverse malware types for more generalized findings. © 2024 IEEE.

关键词： Adversarial machine learning

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A Privacy-Preserving Graph Neural Network Framework with Attention Mechanism for Computational Offloading in the Internet of Vehicles

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computer Modeling in engineering & sciences 2025年第4期143卷 225-254页

作者： Aishwarya Rajasekar Vetriselvi Vetrian Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India

The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle ***,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of *** recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a *** this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is *** Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as ***,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)***,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading ***,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.

关键词： Internet of vehicles vehicular ad-hoc networks(VANET) multiaccess edge computing task offloading graph neural networks differential privacy

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Predictive Model for Student Career Success Using Blockchain Technology and Machine learning 2

Predictive Model for Student Career Success Using Blockchain...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Aggarwal, Mansi Vyas, Vaibhav Department of Computer Science Banasthali Vidyapith Rajasthan India Department of Computer Science and Engineering- AIML ABES Engineering College UP Ghaziabad India

ISBN: (纸本)9798331530389

Using blockchain for student career prediction offers key benefits, including enhanced data security and privacy through encryption and immutability, ensuring personal information protection. It provides transparency and trust with a tamper-proof record of achievements and skills, fostering confidence among employers and educational institutions. Decentralization eliminates the need for a central authority, reducing risks of data loss or manipulation. Additionally, blockchain enables accurate traceability of student progress, ensuring accountability in career prediction data. Using machine learning to develop a prediction model for student career prediction offers substantial benefits by enabling personalized, data-driven insights that can significantly enhance career guidance. With this vast amount of data from various sources, identifying complex patterns and correlations that may not be evident through traditional analysis. This allows for highly accurate, individualized career recommendations, reducing biases and ensuring fair evaluations. Moreover, this model will continuously improve over time, adapting to new data and refining predictions, ultimately leading to more informed and effective career planning for students. Applying machine learning algorithms on student data stored on blockchain will provide exact information about students including their strengths and weaknesses both. This will lead to a better prediction and effecting planning. © 2025 IEEE.

关键词： Prediction models

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Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

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computers, Materials & Continua 2024年第4期79卷 735-759页

作者： Wael M.S.Yafooz Abdullah Alsaeedi Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user ***, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

关键词： Mobile apps issues play store user comments deep learning LSTM bidirectional LSTM

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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