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检索条件"任意字段=International Conference on Security in Computer Networks and Distributed Systems, SNDS 2012"

共 5350 条记录，以下是411-420 订阅

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Securing Mobile Adhoc Network with Quantum Key Distribution: An Experimental Analysis

Securing Mobile Adhoc Network with Quantum Key Distribution:...

引用

Sustainable Expert systems (ICSES), international conference on

作者： Sneha Thapa Anjan Bandyopadhyay Mahendra Kumar Gourisaria Satyendr Singh Sudhansu Shekhar Patra School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar Odisha Computer Science & Engineering Department BML Munjal University Gurugram

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Mobile adhoc networks (MANETs) are characterized by their dynamic and decentralized nature, making them inherently vulnerable to security threats. This paper proposes a novel security framework that integrates Quantum Key Distribution (QKD) with error correction, privacy amplification, and distributed key management to enhance the security of MANETs. The experimental analysis was conducted using simulations to evaluate the effectiveness of the proposed framework across various node densities and mobility patterns. Our results demonstrate improved security in key exchanges but also highlight trade-offs in network performance metrics such as throughput and latency.

关键词： Measurement Privacy Throughput Ad hoc networks Quantum key distribution Delays security Reliability Expert systems Mobile computing

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Enforcing Web security Constraints for LLM-driven Robot Agents for Online Transactions

Enforcing Web Security Constraints for LLM-driven Robot Agen...

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

作者： Shraddha Pradipbhai Shah Aditya Vilas Deshpande School of Computer and Information Sciences University of the Cumberlands Kentucky USA

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The integration of Large Language Models (LLMs) into autonomous robotic agents for conducting online transactions poses significant cybersecurity challenges. This study aims to enforce robust cybersecurity constraints to mitigate the risks associated with data breaches, transaction fraud, and system manipulation. The background focuses on the rise of LLM-driven robotic systems in e-commerce, finance, and service industries, alongside the vulnerabilities they introduce. A novel security architecture combining blockchain technology with multi-factor authentication (MFA) and real-time anomaly detection was implemented to safeguard transactions. Key performance metrics such as transaction integrity, response time, and breach detection accuracy were evaluated, showing improved security and system performance. The results highlight that the proposed architecture reduced fraudulent transactions by 90%, improved breach detection accuracy to 98%, and ensured secure transaction validation within a latency of 0.05 seconds. These findings emphasize the importance of cybersecurity in the deployment of LLM-driven robotic systems and suggest a framework adaptable to various online platforms.

关键词： Industries Multi-factor authentication Accuracy Service robots System performance Large language models Real-time systems Blockchains Fraud Anomaly detection

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UAV Aided Post Disaster Relief networks using Blockchain Technology: Challenges and Solutions

UAV Aided Post Disaster Relief Networks using Blockchain Tec...

引用

international conference on Parallel distributed and Grid Computing (PDGC)

作者： Balaram Pal Soumyajit Paul Ashok Kumar Turuk Munshi Mustafijur Rahaman Sanjeev Patel Dept. of Computer Science Engineering Bengal Institute of Technology Kolkata India Dept. of Computer Science Engineering GCECT Kolkata India Dept. of Computer Science Engineering National Institute of Technology Rourkela India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

This paper investigates integrating blockchain technology into UAV-aided disaster relief networks (UDRNs). It addresses challenges such as inefficient resource coordination, security vulnerabilities in smart contracts, and transaction malleability. Solutions include coordinated smart contracts for efficient resource allocation, dynamic vulnerability auditing for enhanced security, and augmented forensic systems to ensure transaction integrity. The proposed approach enhances trust, transparency, and effectiveness in disaster relief operations by leveraging UAVs and blockchain.

关键词： Disasters Scalability Smart contracts Supply chains Standards organizations Autonomous aerial vehicles Blockchains security Resource management Vehicle dynamics

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Harris Corner Detection Algorithm based Long Short-Term Memory for Detecting distributed Denial of Service Anomalies in Software Defined networks

Harris Corner Detection Algorithm based Long Short-Term Memo...

引用

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

作者： Rashmi Cigiri Gotte Ranjith Kumar Haider Alabdeli Y.M. Mahaboob John S. Kaliappan Malla Reddy Engineering College for Women (UGC-Autonomous) Hyderabad India Department of Computer Science & Artificial Intelligence SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of ECE Mahendra College of Engineering Salem India Department of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Software-Defined Networking (SDN) is the innovative development in network technology with a number of attractive features, including management and flexibility. Despite these advantages, SDN was compromised by distributed Denial of Service (DDoS) attacks, which causes substantial challenge due to the harm networks. Hence, innovative model is required to identify DDoS attacks despite a range of security methods. In order to detect DDoS attacks in SDN in real time, the Harris Corner Detection Algorithm-Long Short-Term Memory (HCDA-LSTM) is presented in this research. The Canadian Institute of Cybersecurity Intrusion Detection system (CICIDS) 2017 dataset is initially used to collect the data. Consequently, the proposed HCDA-LSTM model significantly reduced the challenges of network security, particularly in relation to SDN. The proposed method enhances SDN security by mitigating DoS/DDoS attacks and improve the performance effectively in results. When compared to the existing models such as Deep Learning Approach for Detecting Controller (DLADSC), Extreme Gradient Boosting (XGBoost) and XRDI the suggested HCDA-LSTM method achieved better performances in terms Accuracy in CICIDS 2017 dataset of 99.92%, Precision of 99.64%, F-measure of 98.89% and recall of 98.65% which is comparatively higher when compared to existing models.

关键词： Accuracy Computational modeling Software algorithms Network security Denial-of-service attack Real-time systems Software defined networking computer crime Corner detection Long short term memory

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Evaluation of Deep Neural Network Quality by CleanAI Coverage Metrics Library 17

Evaluation of Deep Neural Network Quality by CleanAI Coverag...

引用

17th international conference on INnovations in Intelligent systems and Applications, INISTA 2023

作者： Ayubi, Abdul Hannan Taskin, Furkan Caglar, Osman Asik, Sergen Baglum, Cem Yayan, Ugur Eskisehir Osmangazi University Computer Engineering Department Eskisehir Turkey Eskisehir Osmangazi University Software Engineering Department Eskisehir Turkey

ISBN: (纸本)9798350338904

The increasing use of AI-based systems in critical domains necessitates the assessment of their quality and reliability. However, there is a lack of readily available tools for analyzing metrics related to the structure, security, safety, and quality control of Deep Neural Network (DNN) models. To address this gap, CleanAI is introduced as a white-box testing library that utilizes coverage metrics to evaluate the structural analysis, quality, and reliability parameters of DNN models. The library incorporates eleven coverage test methods and enables developers to perform essential model analyses and generate output reports. By using CleanAI, developers can effectively evaluate the quality of DNNs and make informed decisions. The library's effectiveness was demonstrated through testing popular models like ResNet50, providing insights into neuron coverage, threshold value coverage, and other coverage metrics. The adoption of CleanAI can significantly evaluate the quality of AI-based systems and result in time and labor cost savings for companies and developers in the industry. © 2023 IEEE.

关键词： Deep neural networks

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Intrusion Detection System Using Ensemble Machine Learning in Cloud Environment 2nd

Intrusion Detection System Using Ensemble Machine Learning i...

引用

2nd international conference on Emerging Technologies and Intelligent systems, ICETIS 2022

作者： Manzoor, Saba Ahmad, Musheer Alhadawi, Hussam S. Department of Computer Engineering Jamia Millia Islamia New Delhi110025 India Department of Computer Techniques Engineering Dijlah University College Baghdad10011 Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq

ISBN: (纸本)9783031252730

Due to the expeditious evolution of advanced computing innovations, new organizational and operational strategies call for adoption. In the last few years, cloud computing has emerged and evolved as one such example to provide complex IT infrastructures, remote storage, a platform for developers and software according to user agreement. In order to make cloud computing possible which provides a distributed domain to its users, the internet has played a significant role. Irrespective of cloud’s versatility and efficiency, there are vulnerabilities present in the cloud environment which can arise both from within and outside the cloud. To protect clouds from any security risk, the traditional security methods fail to completely guard the networks and devices. As a consequence, building an effective intrusion detection system which can detect intrusions with higher accuracy in cloud environment needs to be prioritized. In this paper, we have suggested performing feature selection for reducing dimensions before applying ensemble method to build a model for detection of intrusions. A tree-based technique has been used to select the most prominent features. The intrusion detection system devised is anomaly-based that employs machine learning algorithms: Bagging algorithm and Random Forest algorithm on the UNSW-NB15 dataset which is more recent and comprehensive. An accuracy of 99.5% was achieved for both the bagging algorithm and Random Forest algorithm by reducing the dimension up to only 11 features. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloud computing

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A Comparison of security Analysis Methods for Smart Contracts Built on the Ethereum Blockchain 3rd

A Comparison of Security Analysis Methods for Smart Contra...

引用

3rd international conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Kushwaha, Satpal Singh Joshi, Sandeep Manipal University Jaipur Jaipur India

ISBN: (纸本)9789819926015

Blockchain technology is one of the superior technologies of recent times. It is a decentralized distributed ledger to record transactions in an immutable way. Smart contracts are digital contracts, i.e., an auto executable small piece of code, written to be deployed on the blockchain. Ethereum is a mostly used blockchain platform for writing and deploying the smart contract. The immutability feature of blockchain poses threats to smart contracts because if a smart contract is uploaded on the blockchain, it becomes immutable. The EVM design and solidity programming language pose some security issues, which should be analyzed properly. So smart contracts must be analyzed for vulnerabilities before deployment. Several smart contracts security analysis approaches are proposed in the literature based on symbolic execution, taint analysis, formal verification, rule-based pattern detection, etc. A comparative study of the above techniques is performed in this paper, which will help the researchers select an efficient method to analyze the Ethereum smart contracts and pave the way for future research directions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Smart contract

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Various Modulation Types Classification on the Air by Convolutional Neural Network 7

Various Modulation Types Classification on the Air by Convol...

引用

7th international conference on Future networks and distributed systems, ICFNDS 2023

作者： Hammadi, Yousif I Alsaeedi, Mokhalad Abdulameer Kadhim Mahmood, Omar Abdulkareem Sameen, Aws Zuhair Muthanna, Mohammed Saleh Ali Aziz, Ahmed Bilad Alrafidain University College Diyala Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Medical Instrumentation Engineering Techniques College Of Medical Techniques Al-Farahidi University Baghdad Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan

ISBN: (纸本)9798400709036

Deep learning (DL), a relatively recent AI technique, has been successfully applied to the problem of automated modulation categorization (AMC), with promising results. An essential part of developing the spectrum-sensing capabilities needed by software-defined radio is carrying out radio signal classification jobs, which we did in this research by introducing a better deep neural architecture. Different detection techniques were utilized in the literature;however, DL neural networks are recently utilized in this field. Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) which is one variation of the RNN network, and other techniques are some of the employed DL architectures in the field of AMC. In this paper, Convolutional Neural Network (CNN) model has been built to automatically classify ten-modulation formats correctly. The suggested CNN network is constructed mainly of six-Convolutional layers. The overall number of trainable parameters in the proposed network is 1,757,962. Thus, the network accuracy, which was achieved during the training reached more than 90%. Python programming packages have been used to implement the suggested approach using *** platform, which provides wide-range of facilities. © 2023 ACM.

关键词： Network architecture

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An Innovative Markovian Environment for Intrusion Detection in IoT networks

An Innovative Markovian Environment for Intrusion Detection ...

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international conference on computer and Applications (ICCA)

作者： Eslam Hesham Abeer Hamdy Khaled Nagaty Computer Networks The British University in Egypt Cairo Egypt Software Engineering The British University in Egypt Cairo Egypt Computer Science The British University in Egypt Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Recently, cyberattacks on IoT (Internet of Things) and IIoT (Industrial Internet of Things) networks have become increasingly complicated and more frequent. These attacks have posed considerable threats such as distributed Denial of Service (DDoS) attacks, malware propagation, and unauthorized data access. These attacks threaten many applications, such as smart grids, manufacturing, and critical infrastructure systems. This paper proposes a novel approach to managing cyber-attacks in IoT and IIoT networks by developing a custom Markov Decision Process (MDP) environment and applying Deep Reinforcement Learning (DRL). The agent will be trained by the applied RL algorithm to create and control security policies autonomously. This study proposes a custom MDP environment suitable for the detection of intrusions in IoT and IIoT networks using DRL. To facilitate the training and evaluation of the DRL agent, the MDP environment is designed to mitigate realistic network conditions and various intrusion scenarios.

关键词： Training Markov decision processes Intrusion detection distributed databases Deep reinforcement learning Malware Smart grids Manufacturing security Industrial Internet of Things

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Lattice Cryptography-Based Geo-Encrypted Contact Tracing for Infection Detection 1

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4th international conference on Advances in distributed Computing and Machine Learning, ICADCML 2023

作者： Dhiman, Mayank Gupta, Nitin Singh Jadon, Kuldeep Gupta, Ujjawal Kumar, Yashwant Department of Computer Science and Engineering National Institute of Technology Hamirpur Himachal Pradesh India

ISBN: (数字)9789819912032

ISBN: (纸本)9789819912025

The world has already witnessed many epidemic diseases in the past years, like H1N1, SARS, Ebola, etc. Now, Covid-19 has also been added to list, which is declared as pandemic by World Health Organization. One of the most commonly used methods to tackle the spread of such diseases is using mobile applications to perform contact tracing of the infected person. However, contact tracing applications involve transmitting sensitive location-based data of the infected person to the government servers. Therefore, recently this has raised a lot of concerns regarding the privacy of the infected persons. This work proposes a lightweight and secure encryption scheme, based on location-based encryption which can be used to transfer the location data to the server without compromising its security. The main aim of the work is to design an algorithm in such a way that the encrypted transferred data can only be decrypted at the server and in-between data leakage can be prevented. This work proposes to use location-based encryption combined with Learning with Errors problems in Lattices, which can provide a solution to privacy concerns in contact tracing, which will even be applicable in the post-quantum period. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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