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Next Generation Firewall and Self Authentication for Network security

Next Generation Firewall and Self Authentication for Network...

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international conference on Image Information Processing

作者： Bhagwant Singh Sikander Singh Cheema Department of Computer Science and Engineering Punjabi University Patiala Punjab India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

In the era of the technologies, given the importance of the prevalence of the Internet, network safety is now essential to maintaining the accessibility and confidentiality of networks and data. Regular firewalls cannot deal with new dangers including targeted and data-focused attacks. The many types of current and next-generation firewalls are covered thoroughly in this study, emphasizing their possible capabilities. The Next Generation Firewall (NGFW)’s various network security mechanisms are discussed. Additionally, the benefits of modern firewalls versus conventional firewalls were contrasted. The introduction of smartphones and widespread technology that users connect to these systems, as well as the constantly shifting model connected to global networks like the Internet, are not supported by today’s authorization systems. Several of the shortcomings of a conventional authentication method will be addressed by the idea of a self-authorizing framework for authenticating in an environment that is distributed. The article discusses the main objectives of network security, current security threats, and prospective network protection measures and self-authentication.

关键词： Firewalls (computing) Surveillance Authentication Open systems Information processing Network security Internet

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Assessment of Data Augmentation Paradigms in Pathology Identification

Assessment of Data Augmentation Paradigms in Pathology Ident...

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Information Technology (OCIT), OITS international conference on

作者： Sai Karthik Nallamothu Pradeep Sai Teja Sanka Srikar Vaka Raja Pavan Vignesh Kajjayam Yeswanthvenkatakumar Vidavaluru Tapas Kumar Mishra Department of Computer Science and Engineering SRM University-AP Andhra Pradesh India Department of Computer Science and Engineering SRM University Kattankulathur India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

Data augmentation has an important role in improving the performance of models in medical realm. This research shows the impact of various data augmentation techniques by evaluating its performance using two medical datasets. By employing augmentation strategies such as under sampling and oversampling techniques to balance imbalanced datasets which includes Repeated Edited Nearest Neighbors (RENN), Random under sampling (RUS), Near Miss and SVM, Borderline, Simple Smote techniques. This study explores the connection between data preprocessing, model development and the effectiveness of various data augmentation techniques. Three different deep learning models-Artificial Neural networks (ANN), Long Short-Term Memory networks (LSTM), and its varient Gated Recurrent Units (LSTM) were used on each method, and their performance was observed and analyzed. The comparison of these models on different augmentation techniques provides crucial insights into how each method affects the models performance. These insights can be useful in the real time where the data is not distributed equally such as in medical, security industry. In this research two medical datasets related to heart and cancer were taken and experimented with sampling methods which have imbalance labels.

关键词： Deep learning Support vector machines Pathology Neural networks Data augmentation Sampling methods Data models Real-time systems security Long short term memory

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Intrusion Detection for Advanced Metering Infrastructure using Machine Learning

Intrusion Detection for Advanced Metering Infrastructure usi...

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international conference on Adaptive Science & Technology (ICAST)

作者： Kenneth Broni Godfrey Mills Department of Computer Engineering University of Ghana Accra Ghana

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

Advanced Metering Infrastructure (AMI) system, is made up of smart meters, data management software, and communication networks for energy usage analysis and data transmission. AMI technology has seen a surge in popularity all over the world due to its benefits over analogue systems. However, the system’s reliance on the internet, a public network, exposes them to vulnerabilities and cybersecurity threats. The inherent resource constraints of smart meter devices, such as limited memory and computing power, complicate the implementation of traditional security solutions, heightening concerns about cybersecurity risks. To address this challenge, we utilize machine learning techniques to develop a lightweight Intrusion Detection System (IDS) to protect AMI systems. In this study, we propose an Artificial Neural Network (ANN) model for the development of the Intrusion Detection System for AMI systems. Through rigorous testing on an AMI setup, our proposed IDS achieved remarkable success in detecting intrusion attacks of Denial of Service (DoS) and distributed Denial of Service (DDoS), recording a 99.89% detection rate. The machine learning IDS proved efficient and lightweight, consuming less computing power and memory in its operations.

关键词： Computational modeling Intrusion detection Surge protection Machine learning Artificial neural networks Denial-of-service attack Smart meters computer crime Surges Testing

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Octopus: Experiences with a Hybrid Event-Driven Architecture for distributed Scientific Computing

Octopus: Experiences with a Hybrid Event-Driven Architecture...

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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the international conference for

作者： Haochen Pan Ryan Chard Sicheng Zhou Alok Kamatar Rafael Vescovi Valérie Hayot-Sasson André Bauer Maxime Gonthier Kyle Chard Ian Foster Department of Computer Science University of Chicago Chicago IL USA Data Science and Learning Division Argonne National Laboratory Lemont IL USA Department of Computer Science Southern University of Science and Technology China Department of Computer Science Illinois Institute of Technology Chicago IL USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus’ ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients.

关键词： Automation Scientific computing Instruments Laboratories Process control computer architecture Organizations Dynamic scheduling Fabrics Reliability

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Trust-Based Assured Sensor Fusion in distributed Aerial Autonomy 25

Trust-Based Assured Sensor Fusion in Distributed Aerial Auto...

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Proceedings of the ACM/IEEE 16th international conference on Cyber-Physical systems (with CPS-IoT Week 2025)

作者： R. Spencer Hallyburton Miroslav Pajic Department of Electrical and Computer Engineering Duke University Durham NC USA

ISBN: (纸本)9798400714986

Multi-agent collaboration enhances situational awareness in intelligence, surveillance, and reconnaissance (ISR) missions. Ad hoc networks of unmanned aerial vehicles (UAVs) allow for real-time data sharing, but they face security challenges due to their decentralized nature, making them vulnerable to cyber-physical attacks. This paper introduces a trust-based framework for assured sensor fusion in distributed multi-agent networks, utilizing a hidden Markov model (HMM)-based approach to estimate the trustworthiness of agents and their provided information in a decentralized fashion. Trust-informed data fusion prioritizes fusing data from reliable sources, enhancing resilience and accuracy in contested environments. To evaluate the assured sensor fusion under attacks on system/mission sensing, we present a novel multi-agent aerial dataset built from the Unreal Engine simulator. We demonstrate through case studies improved ISR performance and an ability to detect malicious actors in adversarial settings.

关键词： Autonomous Vehicles

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Sentiment Analysis in Social Media using Bidirectional Long Short Term Memory with Focus Loss function

Sentiment Analysis in Social Media using Bidirectional Long ...

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distributed systems, computer networks and Cybersecurity (ICDSCNC), international conference on

作者： A R. Sivakumaran Ammar H. Shnain Jayapal Lande Anupama Sindgi Patlolla Sruthi Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous) Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science & Artificial Intelligence SR University Warangal India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Sentiment Analysis (SA), is often known as "Opinion Mining" which seeks to analyze sentiment polarities in opinion-based texts. Several Deep Learning (DL) approaches are developed to effectively solves the complex SA challenges. However, existing methods failed to identify the sentiments correctly due to contextual embedding’s and different meaning for same word in a sentence or document. To overcome this, a Bidirectional – Long Short Term Memory with Focal Loss (Bi-LSTM-FL) function for SA in social media. The proposed Bi-LSTM-FL model enhanced the SA process by capturing the context from both past and future words in a sentence which lead to understand complex sentence structures. The twitter dataset is utilized and preprocessed by to enhance the text format for classification. The preprocessed texts are fed to Principal Component Analysis (PCA) for selecting optimal set of features and to reduce dimensionality of features. Finally, with the help of selected features the tweets are categorized as positive, negative or neutral effectively. The experimental results of proposed Bi-LSTM-FL achieved accuracy of 98.2% which is greater when compared to previous method such as Gated Attention Recurrent Network (GARN).

关键词： Deep learning Sentiment analysis Accuracy Social networking (online) Blogs Logic gates Tokenization computer security Principal component analysis Context modeling

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A Review of DDoS Attacks and its Countermeasures in Cloud Computing 5

A Review of DDoS Attacks and its Countermeasures in Cloud Co...

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5th international conference on Information systems and computer networks, ISCON 2021

作者： Varma, S.Asha Reddy, Karri.Ganesh VIT-AP University School of Computer Science and Engineering Amaravathi India

ISBN: (纸本)9781665403412

In the present scenario, cloud computing has become a marketable technology that allows consumers to easily access resources through the internet, based on pay per use model. These resources are provided as services to the customer. Infrastructure as a Service, Platform as a Service, and Software as a Service are the 3 service models provided by the cloud. With the huge advancement in cloud computing technologies, it is widely used by many business applications, industry applications, Enterprise organizations that are committing to multi-cloud architectures, and finally, most IT expenditures are based on the cloud. However, security is the key challenge that needs more concern. According to recent studies, the most critical security concern that is confronted by cloud users is resource availability. This availability issue is mostly affected by the distributed Denial of service attack. The distributed Denial of service attack is an enhanced model of Denial of service. The main intention behind the distributed Denial of Service attack is to bring down a specific service by flooding the servers and network with malformed packets which leads to the unavailability of resources to legitimate users in a cloud. Recently, cloud infrastructures also have been severely impacted by denial of service attacks. The attacker always tries to break the security at the infrastructure level as it provides the basic computing infrastructure for all cloud delivery models. This paper frames the cloud computing services and distributed Denial of Service attack scenarios and their different defense mechanisms. © 2021 IEEE.

关键词： Denial-of-service attack

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Decentralized File Storage System

Decentralized File Storage System

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international conference on Computing and Networking Technology (ICCNT)

作者： Kshitij Tiwari Namrata Dhanda Rajat Verma Computer Science & Engineering Department Amity University Lucknow Uttar Pradesh India Computer Science & Engineering Department Pranveer Singh Institute of Technology Kanpur Uttar Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The need for safe, scalable, and effective cloud storage solutions has increased in a time when digital information is essential to society. Let us introduce you to Google Drive, a reliable cloud storage solution that is a must-have for both people and businesses. Nonetheless, concerns about data security, privacy, and single points of failure have been raised by Google Drive’s centralization. With the goal of creating a more robust and resilient cloud storage environment, this project explores the decentralisation of Google Drive in an effort to allay these worries. The primary aim is to develop and execute a decentralised system by utilising innovative technologies like distributed file systems, peer-to-peer networks, and blockchain to transform the traditional paradigm of cloud storage. The trip begins with a detailed analysis of cloud storage systems as they exist now, highlighting the benefits and drawbacks of centralised designs. The theoretical underpinnings of decentralisation are then thoroughly examined, including important ideas, difficulties, and possible solutions. Important lessons are learned from current decentralised storage systems, offering a sophisticated perspective on their advantages and disadvantages. The development of a decentralised Google Drive prototype is key to the endeavour. In order to strengthen data integrity, security, and user privacy, this project seamlessly integrates blockchain technology. The implementation of a novel peer-to-peer network design distributes data among several nodes and eliminates the need for a central server. Furthermore, an advanced distributed file system is utilised to maximise the effectiveness of data retrieval and storage.

关键词： Cloud computing Data privacy Technological innovation File systems Data integrity Transforms Data models Peer-to-peer computing Blockchains Teamwork

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Enhancing Cyber security Against DDoS Attacks: A Comprehensive Review and Future Directions

Enhancing Cyber Security Against DDoS Attacks: A Comprehensi...

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AIML-Applications for Engineering & Technology (ICAET), international conference on

作者： Smruti Jagtap Vikrant Kavitkar Mahek Jain Anup Ingle Jayashree P. Tamkhade Ketki Kshirsagar Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

distributed Denial of Service (DDoS) attacks pose significant threats to network infrastructures, particularly in critical sectors such as smart grid systems. This paper presents a comprehensive review of DDoS attack detection and prevention strategies based on recent research findings. It encompasses various methodologies ranging from machine learning-based detection approaches to hybrid models integrating router-based client puzzles and pushback techniques. Key themes explored include the importance of cybersecurity in smart grid applications, communication standard vulnerabilities, and the evolving landscape of cyber threats. Additionally, the paper discusses practical implications such as enhanced security, reduced economic losses, and improved resilience in the face of DDoS attacks. Through a synthesis of diverse perspectives, this review underscores the critical need for unified defense mechanisms and hierarchical structures to effectively combat DDoS threats and ensure the integrity and reliability of network operations.

关键词： Accuracy Machine learning algorithms Reviews Biological system modeling Prevention and mitigation Neural networks Denial-of-service attack Smart grids computer crime Random forests

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An Intense Systematic Review of Mobile Agent Based Harmful Threats And Attack Detection systems in IOT networks

An Intense Systematic Review of Mobile Agent Based Harmful T...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： M. Vigenesh G. Ravivarman Prabhdeep Singh Shaik Vaseem Akram Deepti Sharma Mohit Tiwari Department of CSE Karpagam Academy of Higher Education Coimbatore Department of EEE Karpagam Academy of Higher Education Coimbatore Tamil Nadu Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Uttaranchal Institute of Technology Uttaranchal University Department of Management Uttaranchal University Dehradun Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi

A breach is detected by an intrusion detection system (IDS) by analysing events that are deemed risky from a security standpoint in systems and networks. Due to a surge in occurrences involving network security, organisations must implement IDS for the protection of sensitive data. Both intrusions coming from outside a network and those coming from within a distributed network are challenging to identify. IDS should be in charge of processing a sizable volume of data without overwhelming networks and monitoring systems. Due to the flaws and restrictions in centralised IDS, mobile agents (MA) were developed. By identifying harmful activity, these agents can carry out specified actions. From earlier works of literature, it was deduced that most of the existing IDS based on MA are not significantly effective

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