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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 5348 条记录，以下是1-10 订阅

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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...

引用

2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Shah, Shraddha Pradipbhai Deshpande, Aditya Vilas University of the Cumberlands School of Computer and Information Sciences KY United States

ISBN: (纸本)9798350375442

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. © 2024 IEEE.

关键词： Chatbots

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Fault Tolerance Performance of distributed Network security Protection System Optimized by Ant Colony Algorithm

Fault Tolerance Performance of Distributed Network Security ...

引用

2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Li, Teng Technician College of LiaoCheng City Department of Economic Management Liaocheng China

ISBN: (纸本)9798350375442

Ant colony algorithm simulates the foraging behavior of ants in nature, using their unique pheromones to label paths and find the shortest path. This method can effectively solve the optimal problem, especially suitable for complex and changing network environments. The research was conducted on the construction of network environments, simulation of attack scenarios, and integration with ant algorithms. The article provided a detailed explanation of the implementation process of the algorithm, including algorithm initialization, execution process, and adjustment mechanism. This article also applied evaluation indicators such as attack detection rate, recovery speed, and packet loss rate to evaluate the performance of the system. Through the above research, it was verified that ant colony algorithm had practical significance and application prospects for improving the fault tolerance of network protection systems. When there was no node failure, the false alarm rate of the system was only 0.002%. Through the research in this article, it is expected to promote the development of information security, especially to enhance the ability and resilience of distributed systems to resist high-intensity network attacks. © 2024 IEEE.

关键词： Ant colony optimization

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CAN Bus Information security Technology for Connected Vehicles based on Artificial Intelligence

CAN Bus Information Security Technology for Connected Vehicl...

引用

2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Zhou, Yixuan Guo, Zhen Ning, Yuqiao Yu, Mingming Ren, Shixuan China Automobile Data of Tianjin Co. Ltd Tianjin China Catarc Intelligent and Connected Technology Co. Ltd Tianjin China

ISBN: (纸本)9798350375442

With the rapid development of Intelligent Connected Vehicle (ICV) technology, data exchange between vehicles has become increasingly frequent, which greatly improves the demand for information security technology in vehicle systems. In order to achieve this goal, we adopt a variety of AI algorithms, including machine learning and deep learning technology, and develop an intelligent system that can detect and respond to security threats in real time. The specific method includes training AI model to identify potential threats with simulated data. Under the condition of low load, the success rate increased from the initial 60% to 97%, which shows the excellent performance of the AI system in an ideal network environment, indicating that the system can maintain good performance under various network pressures. AI technology has the potential to become the core technology of CV information security, which will help to promote the development of safety standards in the entire automobile industry. © 2024 IEEE.

关键词： artificial intelligence CAN bus information security technology connected vehicle load conditions

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Investigating Cyber Threats against Proof-of-work Blockchain networks 3

Investigating Cyber Threats against Proof-of-work Blockchain...

引用

3rd IEEE international conference on Blockchain and distributed systems security, ICBDS 2024

作者： Fattahdizaji, Ali Pishdar, Mohammad Shukur, Zarina University Kebangsaan Malasya Information Science and Technology Department Kualalumpur Malaysia Bu-Ali Sina University Computer Science Department Hamedan Iran

ISBN: (纸本)9798350354348

In recent years, blockchain networks have become a singular power in the financial sector. Proof of work (POW) networks are among the most significant and widespread kinds of blockchain networks. One of the main issues POW blockchains have been dealing with recently is cyber threats. Targets and a market segment have been impacted by cyberattacks against multiple well-known initiatives in this field. In this sense, several attacks have been examined and researched in these kinds of networks with the goal of finding important information and roots. Furthermore, the Ethereum (POW) network is analyzed using 140 actual attacks as a sample. The study's findings demonstrate the numerous similarities among various attacks. Additionally, variations are apparent. © 2024 IEEE.

关键词： Cyber attacks

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Sharding:A Scalability Solutions for Blockchain networks 3

Sharding:A Scalability Solutions for Blockchain Networks

引用

3rd IEEE international conference on Blockchain and distributed systems security, ICBDS 2024

作者： Kale, Dattatray Raghunath Jadhav, Amolkumar N. Kruishna Prasad, K. Salunkhe, Somanath Janardan Hirve, Sumit Goswami, Chandrashekhar Mit Art Design and Technology University Mit School of Computing Department of Computer Science & Engg Pune India Annasaheb Dange College of Engineering and Technology Department of Computer Science & Engg Ashta India Shrinivas University Department of Computer Science & Engg Mangalore India D Y Patil Agriculture and Technical University Department of Computer Science & Engg Talsande India Sir Padampat Singhania University Faculty of Computing and Informatics Rajasthan Udaipur India

ISBN: (纸本)9798350354348

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks. © 2024 IEEE.

关键词： distributed ledger

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Optimization of Data Encryption Algorithms in Information Construction

Optimization of Data Encryption Algorithms in Information Co...

引用

2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Zhu, Shuai You, Yi Zhou, Bo Chen, Chuanting Yi, Xueqin Hankou University Network Information Center Wuhan China Hankou University School of Computer Science and Technology Wuhan China Wuhan University of Engineering Science Library and Information Center Wuhan China Hankou University School of Music Wuhan China Hankou University Board Office Wuhan China

ISBN: (纸本)9798350375442

Data encryption algorithms play a crucial role in information construction, effectively protecting data security. The problems in traditional research include a lack of targeting, a balance between efficiency and safety, and a lack of comprehensive consideration. Therefore, this article proposed targeted optimization strategies and methods by in-depth analyzing the needs and scenarios of information construction, combined with the characteristics and problems of data encryption algorithms. Meanwhile, this article also comprehensively considered the integration, deployment, management, and maintenance of algorithms to achieve optimal matching and collaborative work between algorithms and information systems. Through research, this article aimed to promote the application and development of data encryption algorithms in information construction, and improve the efficiency, security, and availability of data encryption algorithms in protecting data security. The results showed that the efficiency of the data encryption algorithm based on RSA (Rivest-Shamir-Adleman) reached the highest of 89.6%. This article provides strong support for the application and development of data encryption algorithms, promoting the sustainable development of information construction and the protection of data security. © 2024 IEEE.

关键词： advanced encryption standard data encryption algorithm information technology construction RSA algorithm

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Enhanced Proof of Authentication(E-PoA) Consensus Blockchain-Based Secured Health Care Data Transmission in Internet of Things

Enhanced Proof of Authentication(E-PoA) Consensus Blockchain...

引用

2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Keerthana, T. Krithika, D.R. Vels Institute of Science Technology and Advanced Studies Department of Computer Application Chennai India

ISBN: (纸本)9798350375442

Blockchain and the Internet of Things (IoT) are revolutionizing healthcare, presenting the potential for transformation. The IoT is achieving more and more confidence as the infrastructure for future applications. Therefore, based on the comprehensive integration of Blockchain (BC) technology with IoT, security requirements are provided through security frameworks built into BC. Healthcare IoT applications in BC technology are revolutionizing industries. It also analyzes the potential benefits of BC implementation in IoT and healthcare for securely transferring medical data. Blockchain's decentralized and immutable nature of medical data sharing offers secure ways to store and transmit data. However, it is necessary to protect the data's privacy stored and shared securely. This review paper evaluates the algorithm based on Rivest, Shamir, and Adelman (RSA), Advanced Encryption Standard (AES), Lionized Golden Eagle-based Homomorphic Elapid security (LGE-HES), and BC-Assisted Secure Data Management Framework (BSDMF). This survey paper describes Blockchain technology-based medical data transfer based on IoT medical systems. The performance evaluation regarding Root Mean Square Error (RMAE), MSE, time complexity, and security performance compared to standard technologies can be analyzed. © 2024 IEEE.

关键词： Electronic health record

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Addressing Data Poisoning and Model Manipulation Risks using LLM Models in Web security

Addressing Data Poisoning and Model Manipulation Risks using...

引用

2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Shah, Shraddha Pradipbhai Deshpande, Aditya Vilas University of the Cumberlands School of Computer and Information Sciences KY United States

ISBN: (纸本)9798350375442

Data poisoning and model manipulation represent significant threats to cybersecurity, where adversaries intentionally inject malicious data into training sets, leading to compromised machine learning models. This study explores the efficacy of using Large Language Models (LLMs) to detect and mitigate such risks in cybersecurity. We propose a novel LLM-based framework that analyzes incoming data streams, identifies anomalies indicative of poisoning, and performs real-time model correction to maintain the integrity of security systems. Leveraging the generative and analytical capabilities of LLMs, our approach dynamically adapts to evolving attack vectors by detecting subtle changes in data distributions. We evaluated the framework on a cybersecurity dataset with known data poisoning incidents, achieving an accuracy of 97.5% in identifying poisoned data and reducing model manipulation attempts by 85% compared to conventional machine learning models. Additionally, our method exhibited a 30% improvement in response time to attacks, ensuring faster detection and mitigation. The study demonstrates that integrating LLMs into cybersecurity systems can significantly enhance resilience against sophisticated adversarial attacks. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review on Privacy Enhanced distributed ML Against Poisoning Attacks 1

引用

9th international conference on Cyber security, Privacy in Communication networks, ICCS 2023

作者： Archa, A.T. Kartheeban, K. Kalasalingam Academy of Research and Education Computer Science and Engineering Srivilliputhur India

ISBN: (数字)9789819739738

ISBN: (纸本)9789819739721

There is a higher demand for machine learning applications nowadays. To increase the accuracy of results, more and more training data is essential. To better increase the size of training data, it is necessary to distribute the machine learning workload to different nodes. Such learning systems are referred to as distributed learning systems. With the advancement of machine learning, processing all the data on a single server has become challenging. Therefore, machine learning tasks must be distributed across several servers, by converting centralized machine learning into distributed machine learning. Sometimes, attackers add impurities in the training phase to alter the trained ML model and it affects security of the model. Such attacks are called poisoning attacks. So, mitigating such attacks is still a challenging task. One of the most critical challenges is data and model security. This is a review paper focused on various security mechanisms for enhancing security in distributed ML systems. Privacy-preserving distributed learning mainly focuses on some basic cryptographic techniques. Among these methods, the hybrid method of encryption is used as the most suitable method. Due to complexity issue, it is better to integrate with particle swarm optimization techniques which will enhance the security as well as privacy of the ML systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Federated learning

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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...

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2024 international conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Cigiri, Rashmi Kumar, Gotte Ranjith Alabdeli, Haider John, Y.M. Mahaboob Kaliappan, S. Hyderabad India Sr University Department of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Mahendra College of Engineering Department of Ece Salem India Lovely Professional University Department of Research and Development Phagwara India

ISBN: (纸本)9798350375442

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. © 2024 IEEE.

关键词： Network intrusion

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