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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Harsh Parekh Aparna Kumari Dilip Yadav Sudeep Tanwar Prasun Kumar Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Computer Science and Engineering SCSET Bennett University Greater Noida India Department of Biotechnology SSET Sharda University Greater Noida India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

This study aims to enhance the security of communication between aeroplanes and ground control from potential risks. We introduce a novel framework, i.e., NeuroSkyWatch to protect aeroplanes from interference attacks like jamming and Distributed Denial of Service (DDoS). NeuroSkyWatch utilizes Bidirectional Long Short-Term Memory (Bi-LSTM), a neural network (NN), to detect and prevent threats proactively. Operating as an intelligent system, NeuroSkyWatch acts as a smart radar, continuously analyzing digital communication patterns in aerial systems using the Bi-LSTM model. Moreover, the Bi-LSTM’s ability to encompass past and future contextual information enables proactive threat detection. Additionally, NeuroSkyWatch leverages 6G technology to strengthen the security of airborne platforms in real time. This approach supports terabit-level data rates, ultra-low delay in the microsecond range, and extensive sensor/actuator data support. By combining NN algorithms with 6G networks, NeuroSkyWatch strengthens defences against evolving cyber threats in aerial environments. NeuroSkyWatch demonstrates its effectiveness across diverse scenarios, achieving a prediction accuracy of 95%, a communication delay of 245 milliseconds, and a validation loss of 0.31, surpassing existing approaches in security and performance.

关键词： 6G mobile communication Technological innovation Atmospheric modeling Prevention and mitigation Radar Threat assessment Real-time systems Delays Testing Resilience

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Enhancing Quality of Service in IoT-WSN through Edge-Enabled Multi-Objective Optimization

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IEEE Transactions on Consumer Electronics 2025年

作者： Singh, Shailendra Pratap Kumar, Naween Kumar, Gyanendra Balusamy, Balamurugan Bashir, Ali Kashif Al Dabel, Maryam M. Department of Computer Science and Engineering MMMUT UP Gorakhpur India SCSET Bennett University Greater Noida India Department of IoT and Intelligent Systems Manipal University Jaipur Jaipur India India Networks and Security Manchester Metropolitan University United Kingdom Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia

The demand for real-time, high-quality services (QoS) is increasing with the proliferation of the resource-constrained nature of edge devices that facilitate the Internet of Things (IoT) and wireless sensor network (WSN) applications. Several existing multi-objective algorithms, such as MOPSO, Elitism MOGA, MODE, and others, are capable of balancing exploration and exploitation;they assist in efficient QoS management for WSN-IoT applications, address resource limitations, and align with the objectives of the applications. However, they suffer from showing robustness in the solution and efficient convergence rates on benchmark functions impacting overall QoS. This paper proposes a multi-objective optimization and edge-intelligent adaptation-based strategy to address QoS management issues, jointly optimize several competing objectives, like energy and latency, and maximize localization and coverage rates while considering the limitations of edge devices. The proposed work uses a novel Grey-wolf optimizer (GWO) Algorithm with an innovative bird-edge-computing adaptation approach to analyze the complex connections between input parameters, edge resources, and QoS indicators to generate Pareto-optimal solutions. The evaluation of the proposed edge intelligence technique with IoT applications demonstrates its effectiveness compared to conventional heuristic-based approaches. This approach enhances the QoS in IoT applications and improves resource utilization and scalability in edge computing environments. © 2025 IEEE.

关键词： Quality of service

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Enhancing Security and Privacy of Healthcare Data Using Hybrid Cryptographic Scheme for Cloud-Based Environment

Enhancing Security and Privacy of Healthcare Data Using Hybr...

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Disruptive Technologies (ICDT), International Conference on

作者： Sanjay Kumar Ajay Kumar Kumar Abhishek Pradeep Kumar Arya Department of Computer Science and Engineering National Institute of Technology Patna Bihar India Dept of Computer Science Engineering and Technology Bennett University Greater Noida UP India Department of SCSET Bennett University Greater Noida UP India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Hospitals are progressively adopting cloud computing to store and manage patient data, enhancing accessibility and collaboration. The sensitive nature of health information necessitates robust security measures to avert unauthorized access and potential breaches. This study introduces a hybrid methodology that combines the Blowfish algorithm with Elliptic Curve Cryptography (ECC) to protect data during cloud transmission. ECC is used as an asymmetric encryption technique for key exchange, whereas Blowfish serves as a symmetric encryption strategy for data encryption. Combining asymmetric and symmetric cryptography makes sure that security is higher and key management is more efficient. We commend ECC for its exceptional performance and enhanced security. Blowfish is optimal for cloud data encryption because of its swift encryption and decryption rates. It employs various key lengths, rendering it resilient against brute force assaults and safeguarding the secrecy of stored data. Blowfish enables robust encryption mechanisms to protect data during cloud transport from unauthorized access. We assessed the efficacy of this hybrid encryption method by conducting various tests across numerous data clouds. The findings indicate that the suggested hybrid security scheme outperforms the current schemes regarding computing time.

关键词： Cloud computing Hospitals Force Collaboration Memory Organizations Elliptic curve cryptography Rendering (computer graphics) Encryption Eavesdropping

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APMVU: Exploit Pentesting of User Account Settings with Arduino-Based Pro Micro USB and Microcontroller

APMVU: Exploit Pentesting of User Account Settings with Ardu...

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Innovations in Communications, Electrical and computer engineering (ICICEC), International Conference on

作者： Peddinti Lalithaditya Mendu Venkat Somanadh Pritam Rani Prity Rani Rohit Kumar Sachan SCSET Bennett University Greater Noida India Department of Computer Science and Engineering GL Bajaj Institute of Technology & Management Greater Noida India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

In today’s technological landscape, where reliance on digital tools and the internet is pervasive, safeguarding data is imperative. As digitalization permeates every aspect of daily life, the demand for cybersecurity solutions is soaring. This increased dependency has correspondingly led to a surge in security breaches, including unauthorized access, phishing, and other cyberattacks. The safest system is the one that is not used-unplugged, locked in a safe, or buried underground. To overcome these concerns, this work proposes an Arduino based Pro Micro Versa USB, which utilizes the Arduino ATmega32u4 chip, a versatile 8-bit microcontroller from Microchip Technology, housed within the Arduino Pro Microchip. We used the Arduino IDE with version 2.3.2 for the Windows 64-bit OS to implement the proposed APMVU. It has an ATmega32U4 microcontroller and a low-power 8-bit RISC processor for its robust features and versatile capabilities. It includes 32 KB of self-programming Flash program memory, 2.5 KB of SRAM, and 1 KB of EEPROM. Our experimental analysis reveals that Arduino based Pro Micro Versa USB successfully automates a sequence of interactions with the Windows OS, manipulating settings and navigating through menus. It emulates human user actions, showcasing the capabilities of microcontroller-based automation for GUI interactions. This proposed approach offers efficiency and consistency, making it valuable for streamlining repetitive tasks and saving time in various applications.

关键词： Integrated circuits Technological innovation Reduced instruction set computing Automation Microcontrollers Navigation EPROM Random access memory Universal Serial Bus Graphical user interfaces

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Detection of DDoS Attacks in SDN Environment with Big Data Distributed Frameworks - A Review

Detection of DDoS Attacks in SDN Environment with Big Data D...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Amandeep Kaur C. Rama Krishna Nilesh Vishwasrao Patil Aditya Bhardwaj Department of Computer Science & Engineering National Institute of Technical Teacher Training & Research Chandigarh India Department of Computer Engineering Govt. Polytechnic Chhatrapati Sambhajinagar Aurangabad India School of Computer Science Engineering & Technology (SCSET) Bennett University Greater Noida India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Software-Defined Networking (SDN) represents a paradigm shift in network management, offering unprecedented control and flexibility. However, due to its architectural flaws, it remains vulnerable to various cyberattacks, such as Distributed Denial of Service (DDoS). This review critically analyzes existing literature on DDoS detection approaches within SDN environments, primarily focusing on the effectiveness of various big data distributed frameworks. We compare traditional detection methods with those deployed in Distributed big-data frameworks. It emphasizes the advantages of stream-processing frameworks such as Apache Flink, Apache Kafka Streams, Apache Spark Streaming, Apache Structured Streaming, Apache Storm and Eclipse StreamPipes. Our analysis reveals that distributed big data frameworks handle large-scale, high-traffic DDoS attacks more efficiently than traditional frameworks. By systematically contrasting the characteristics of these frameworks, we identify the most suitable use cases for each, providing insights into their applicability and effectiveness in detecting DDoS threats in the SDN environment.

关键词： Storms Reviews Big Data Denial-of-service attack Sparks Security computer crime Artificial intelligence Software defined networking Streams

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A Comparative Study of Aggregation Techniques in Group Food Recommendations

A Comparative Study of Aggregation Techniques in Group Food ...

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IEEE India Conference (INDICON)

作者： Reetu Singh Pragya Dwivedi SCSET Bennett University Greater Noida India Computer Science and Engineering Department Motilal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Recommender systems (RSs) have become essential tools for personalizing content and assisting users in discovering relevant items across various domains. Group RSs extend this concept by generating recommendations that satisfy the preferences of multiple users within a group, which is particularly challenging due to the need to aggregate diverse individual preferences. In the context of food recommendations, these systems play a crucial role in helping users explore and select recipes that suited to the tastes of all group members. In this paper, we investigate the impact of different aggregation techniques on the effectiveness of group RSs using two food datasets: *** and ***. We implement and evaluate several aggregation methods within both user-based and item-based collaborative filtering models. The performance of these techniques is assessed using key evaluation metrics: RMSE, MAE, Precision, Recall, and F1-score. Our comparative analysis provides insights into the strengths and limitations of each aggregation technique in the context of group recommendations, offering guidance on selecting the most appropriate method for designing effective food RSs.

关键词： Measurement Accuracy Collaborative filtering Aggregates Reliability Recommender systems

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Enhancing Security and Privacy of Healthcare Data Using Hybrid Cryptographic Scheme for Cloud-Based Environment 3

Enhancing Security and Privacy of Healthcare Data Using Hybr...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Kumar, Sanjay Kumar, Ajay Abhishek, Kumar Arya, Pradeep Kumar National Institute of Technology Department of Computer Science and Engineering Bihar Patna India Bennett University Dept of Computer Science Engineering and Technology UP Greater Noida India Bennett University Department of Scset UP Greater Noida India

ISBN: (纸本)9798331519582

关键词： Electrotherapeutics

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Big Data-Driven Long Term Accurate CO2 Emission Forecasting Using Advanced Recurrent Neural Networks

Big Data-Driven Long Term Accurate CO2 Emission Forecasting ...

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IEEE International Conference on Big Data

作者： Anil Verma Lalit Chandra Routhu Rishikant Chigrupaatii Anurag Choubey Rajiv Misra T.N. Singh Department of Computer Science and Engineering Indian Institute of Technology Patna Dayalpur India SCSET Bennett University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Global climate change driven by carbon dioxide (CO2) emissions necessitates accurate forecasting for effective mitigation strategies. Traditional forecasting models, such as ARIMA and statistical methods, often struggle to handle the non-linear and long-term temporal dependencies in CO2 emission data. This paper evaluates the performance of advanced Recurrent Neural Network (RNN) architectures, specifically Long Short-Term Memory (LSTM), Stacked LSTM, Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) with attention mechanisms, in forecasting long-term CO2 emissions. These models are compared against traditional machine learning algorithms like Random Forest and Gradient Boosted Trees (GBT). The Random Forest and Gradient Boosted Trees, implemented using PySpark framework for distributed computing, are leveraged for efficient big data handling, enabling scalable solutions for CO2 emission forecasting. Our experimental results, using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), show that the Stacked LSTM model achieved the lowest MAE of 25.795, RMSE of 80.678, and MAPE of 1.072, outperforming Random Forest (MAE: 72.4, RMSE: 310.5, MAPE: 1.32) and GBT (MAE: 75.6, RMSE: 356.8, MAPE: 1.57). The findings demonstrate that advanced RNN models, particularly the Stacked LSTM and Bi-LSTM with attention, provide superior performance over traditional models, effectively capturing complex temporal patterns in the data. This study offers valuable insights into CO2 emission forecasting approaches, contributing to the development of accurate tools for climate policy and strategy formulation.

关键词： Accuracy Statistical analysis Computational modeling Prevention and mitigation Carbon dioxide Big Data Data models Forecasting Carbon emissions Random forests

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Unveiling Deception: Machine Learning Strategies for Fake News Detection and Classification

Unveiling Deception: Machine Learning Strategies for Fake Ne...

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Aayush Kunwar Mayank Aggarwal Neha Sharma Daksh Rawat Satya Prakash Yadav Manisha Aeri Computer Science and Engineering Graphic Era Hill University Dehradun India Computer Science and Engineering IIMT College of Engineering Greater Noida Uttar Pradesh India School of Computer Science Engineering and Technology (SCSET) Bennett University Greater Noida U.P. India R/S Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

In this study, a system is developed to detect and classify fake news whose proliferation significantly threatens information integrity, public trust, and societal stability. This study presents a comprehensive list of ML strategies for fake news detection and classification. It investigates the efficiency of different ML algorithms such as Logistic regression, Decision tree, and Random Forest, in distinguishing between factual and deceptive content. The findings reveal that some algorithms demonstrate high accuracy and reliability in identifying fake news articles. Moreover, a novel approach is proposed for the manual testing of news articles thus enabling real-time verification of information authenticity.

关键词： Logistic regression Accuracy Computational modeling Predictive models Data models Classification algorithms Decision trees Fake news Random forests Testing

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Machine Learning Empowered Breast Cancer Diagnosis: Insights from Coimbra Dataset Analysis

Recent Advances in Computer Science and Communications

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Recent Advances in computer science and Communications 2025年第3期18卷

作者： Tiwari, Manish Singh, Nagendra Mewada, Arvind Ansari, Mohd. Aquib Department of Computer Science Engineering Career Point University Rajasthan Kota India Department of Electrical & Electronics Engineering Trinity College of Engineering and Technology Hyderabad Telangana Karimnagar India SCSET Bennett University U.P. Greater Noida India

Aim: The aim of this work is to succinctly communicate the key aspects of a research study on breast cancer. This includes highlighting the global impact and prevalence of breast cancer, emphasizing the challenges of early diagnosis, discussing the potential of technological advancements, and showcasing the role of machine learning algorithms in the context of liver cancer diagnosis. Background: Cancer, notably breast cancer, represents a global health challenge, claiming a significant toll with 12.5% of new cancer cases annually. The prevalence of breast cancer among women worldwide is alarming, resulting in 2.26 million incidents and the unfortunate loss of 685,000 lives. Objective: This article focuses on the critical aspect of early breast cancer diagnosis, acknowledging its heightened difficulty in developing nations compared to developed counterparts. The potential for advancements in technology to serve as a beacon of hope lies in early identification and timely treatment, offering salvation to numerous women and significantly elevating survival chances. Methods: In this intricate landscape, machine learning algorithms, particularly in diagnosing liver cancer at its nascent stages, emerge as instrumental tools. The study employs the latest Coimbra dataset, encompassing nine key attributes and a binary classification attribute, with values 1 and 2 signifying benign and malignant cases, respectively. Results: Supervised machine learning algorithms, including Bayes net, multilayer perceptron, IBK, random committee, and random tree, are meticulously applied. Certain models exhibit superior accuracy, precision, recall, and performance, positioning them as promising cornerstones for breast cancer analysis. Conclusion: This structured abstract highlights the urgent need for effective screening and prevention strategies, emphasizing the potential of advanced technology and machine learning algorithms to play a pivotal role in the early detection and analysis of breas

关键词： Adversarial machine learning

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