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Re-Encryption Model for Multi-Block Data Updates in Network security 1

Re-Encryption Model for Multi-Block Data Updates in Network ...

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1st international conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Devaki, K. Leena Jenifer, L. Rajalakshmi Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665497107

Nowadays, online cloud storage networks can be accessed by third parties. Businesses that host large data centers buy or rent storage space from individuals who need to store their data. According to customer needs, data hub operators visualise the data and expose the cloud storage for storing data. Tangibly, the resources may wander around numerous servers. Data resilience is a prior need for all storage methods. For routines in a distributed data center, distributed removable code is appropriate. A safe cloud cache solution, AES-UCODR, is proposed to decrease I/O overheads for multi-block updates in proxy re-encryption systems. Its competence is evaluated using the real-world finance sector. © 2022 IEEE.

关键词： Cloud storage

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6G-SECUREIDS: Blockchain-Enhanced Secure Knowledge Transfer for distributed Intrusion Detection systems in Advanced networks

6G-SECUREIDS: Blockchain-Enhanced Secure Knowledge Transfer ...

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international conference on Networking and Advanced systems (ICNAS)

作者： Sabrina Sakraoui Makhlouf Derdour Ahmed Ahmim Department of Computer Science Networks and Systems Laboratory Badji Mokhtar Annaba University Annaba Algeria Department of Computer Science Networks and Systems Laboratory Oum El Bouaghi University Oum El Bouaghi Algeria Dpt. of Mathematics and Computer Science Networks and Systems Laboratory Mohamed-Cherif Messaadia University Souk Ahras Algeria

As the development of wireless networks advances towards the deployment of 6G technology, ensuring robust security measures becomes crucial. In this paper, we propose 6G-SECUREIDS, a novel intrusion detection system designed specifically for 6G wireless networks. The system leverages machine learning techniques and blockchain technology to detect and mitigate security threats, safeguard data privacy, and ensure efficient and optimized training. Our experimental results demonstrate the effectiveness and reliability of the system, with the clients achieving high accuracy scores across different model architectures. The promising results highlight the potential of 6G-SECUREIDS as a valuable security solution for future 6G networks, contributing to the overall protection and integrity of the wireless communication ecosystem.

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Performance Analysis of DDoS Mitigation in Heterogeneous Environments 2

Performance Analysis of DDoS Mitigation in Heterogeneous Env...

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2nd international conference on Interdisciplinary Cyber Physical systems, ICPS 2022

作者： Verma, Amandeep Saha, Rahul Lovely Professional University School of Computer Science and Engineering India

ISBN: (数字)9781665470223

ISBN: (纸本)9781665470223

computer and Vehicular networks, both are prone to multiple information security breaches because of many reasons like lack of standard protocols for secure communication and authentication. distributed Denial of Service (DDoS) is a threat that disrupts the communication in networks. Detection and prevention of DDoS attacks with accuracy is a necessity to make networks *** this paper, we have experimented two machine learning-based techniques one each for attack detection and attack prevention. These detection & prevention techniques are implemented in different environments including vehicular network environments and computer network environments. Three different datasets connected to heterogeneous environments are adopted for experimentation. The first dataset is the NSL-KDD dataset based on the traffic of the computer network. The second dataset is based on a simulation-based vehicular environment, and the third CIC-DDoS 2019 dataset is a computer network-based dataset. These datasets contain different number of attributes and instances of network traffic. For the purpose of attack detection AdaBoostM1 classification algorithm is used in WEKA and for attack prevention Logit Model is used in STATA. Results show that an accuracy of more than 99.9% is obtained from the simulation-based vehicular dataset. This is the highest accuracy rate among the three datasets and it is obtained within a very short period of time i.e., 0.5 seconds. In the same way, we use a Logit regression-based model to classify packets. This model shows an accuracy of 100%. © 2022 IEEE.

关键词： Machine learning

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Opcode based malware detection using Stacked Recurrent Neural networks and Convolutional Neural networks

Opcode based malware detection using Stacked Recurrent Neura...

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Blockchain and distributed systems security (ICBDS), IEEE international conference on

作者： Arya Gokhale Yash Lad Akshit Mahaur Rashmi Rane School of Computer Engineering and Technology MIT World Peace University Pune India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Malwares is designed to infiltrate devices and in most cases the underlying operating system and are packaged in multiple file formats. Considering that Windows operating system is one of the most opted operating systems worldwide in this paper we are focusing on developing a PE file malware detection system using Stacked CNN and RNN. In this paper we are training a LSTM and GRU model along a CNN model to detect malware PE files. We are using two techniques for comparing the results, we are using the DikeDataset which is an open source active dataset for collecting malware and benign files. Further we are employing static analysis using the opcode frequency based malware detection technique. Further we are exploring the Blue Hexagon Open Dataset for Malware AnalysiS (BODMAS) dataset which is a comprehensive dataset maintained for 57,293 malware samples and 77,142 benign samples and 581 malware families and the EMBER dataset.

关键词： Training Adaptation models Recurrent neural networks Operating systems Static analysis Malware Topology Convolutional neural networks Long short term memory Optimization

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Enhanced Hybrid Deep Learning Model for distributed Denial of Service Attack Detection in IoT Environments

Enhanced Hybrid Deep Learning Model for Distributed Denial o...

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

作者： Sharmila Kumari N H S Vimala Pruthvi C N Shreyas J Dept. of CSE UVCE Bengaluru India Dept. of CSE Dayananda Sagar College of Engineering Bengaluru India Dept. of Information Technology Manipal Academy of Higher Education Manipal Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

security concerns have grown significantly with the proliferation of Internet of Things (IoT) devices, particularly in light of distributed Denial of Service (DDoS) attacks. Network resources may be overloaded by these assaults, leading to significant disruptions, data leaks, and outages. Studies have demonstrated that a combined deep learning strategy utilizing Convolutional Neural networks (CNN) and Long Short-Term Memory (LSTM) networks may successfully identify these DDoS assaults in Internet of Things environments. This study aims to make the hybrid model better by adding new techniques like feature selection, ensemble learning, and an improved attention mechanism. We thoroughly test the upgraded model against the original hybrid model and other traditional machine learning methods using different datasets. The results show that the improved model boosts detection accuracy, precision, recall, and F1-score, making it a stronger tool for spotting DDoS attacks in IoT networks. By using CIC-DDoS2019 dataset, our model yields results with an accuracy of 99%, precision, recall and F1 score of 98%.

关键词： Deep learning Accuracy Computational modeling Denial-of-service attack Feature extraction Internet of Things Convolutional neural networks Ensemble learning computer crime Long short term memory

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Function Scheduling with Data security in Serverless Computing systems

Function Scheduling with Data Security in Serverless Computi...

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international Communication systems and networks and Workshops, COMSNETS

作者： Srestha Saha Arvind Pandey Sourav Kanti Addya Shubha Brata Nath Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these challenges, we have proposed a system to secure the data of an end-user. We also aim to meet the quality of service (QoS) for the end-user requests. This work presents a Simulated Annealing-based optimization algorithm for function placement. Also, we have Hyperledger Fabric, a blockchain framework in the system architecture for securing the data of an end-user. We have conducted experiments in Amazon Elastic Compute Cloud (EC2) taking virtual machine instances. The experiments in Amazon EC2 indicate that the proposed system secures the data and enhances the end-user’s QoS.

关键词： Processor scheduling distributed ledger Data security Serverless computing systems architecture Quality of service Simulated annealing Virtual machines Fabrics Time factors

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Integration of Heterogeneous IDS with SIEM for DDoS Attack Detection in computer Networked Multi-Organizational Environments

Integration of Heterogeneous IDS with SIEM for DDoS Attack D...

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Communications, Information, Electronic and Energy systems (CIEES), international conference on

作者： M. Akmal Maliki Parman Sukarno Aulia Arif Wardana School of Computing Telkom University Bandung Indonesia Faculty of ICT Wrocław University of Science and Technology Wrocław Poland

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

The increasing reliance on computer networks for business operations has led to a rise in distributed Denial of Service (DDoS) attacks. These attacks pose significant threats to network security, economic stability, and organizational operations. Organizations connected through the same network are particularly vulnerable. In response to these growing threats, detection systems have been developed to integrate heterogeneous Intrusion Detection systems (IDS) with security Information and Event Management (SIEM) systems. This integration assists in the detection of DDoS attacks within multi-organizational environments. The system employs the Opensearch dashboard, providing a centralized and efficient interface for the security Operations Center. System testing was conducted through coordinated DDoS attack simulations by three attackers over a duration of 7-8 minutes. The Snort IDS demonstrated an average detection rate of 95.4%, with an alert correlation mechanism efficiency of 84.6%. Among the IDS systems tested, Zeek IDS consumed the most resources, with an average CPU usage of 24.6% and memory usage of 86.5%. In contrast, the Wazuh Dashboard exhibited lower resource consumption, with an average CPU usage of 0.6% and memory usage of 5.2%.

关键词： System testing Correlation Accuracy Scalability Denial-of-service attack computer networks Threat assessment Stability analysis Robustness computer crime

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23rd international conference on Embedded computer systems: Architectures, Modeling, and Simulation, SAMOS 2023

23rd International Conference on Embedded Computer Systems: ...

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23rd international conference on Embedded computer systems: Architectures, Modeling, and Simulation, SAMOS 2023

ISBN: (纸本)9783031460760

The proceedings contain 36 papers. The special focus in this conference is on Embedded computer systems: Architectures, Modeling, and Simulation. The topics include: Micro-architecture and Control Electronics Simulation of Modular Color Center-Based Quantum computers;From Algorithm to Implementation: Enabling High-Throughput CNN-Based Equalization on FPGA for Optical Communications;parti-gem5: gem5’s Timing Mode Parallelised;reliable Basic Block Energy Accounting;rattlesnakeJake: A Fast and Accurate Pre-alignment Filter Suitable for Computation-in-Memory;PATARA: Extension of a Verification Framework for RISC-V Instruction Set Implementations;Fast Shared-Memory Barrier Synchronization for a 1024-Cores RISC-V Many-Core Cluster;High Performance Instruction Fetch Structure within a RISC-V Processor for Use in Harsh Environments;Unlocking the Potential of RISC-V Heterogeneous MPSoC: A PANACA-Based Approach to Simulation and Modeling;DD-MPU: Dynamic and distributed Memory Protection Unit for Embedded System-on-Chips;Fault Detection Mechanisms for COTS FPGA systems Used in Low Earth Orbit;trust-Based Adaptive Routing for NoCs;run-Time Detection of Malicious Behavior Based on Exploit Decomposition Using Deep Learning: A Feasibility Study on SysJoker;a Survey of Software Implementations for the Number Theoretic Transform;METASAT: Modular Model-Based Design and Testing for Applications in Satellites;RISC-V Processor Technologies for Aerospace Applications in the ISOLDE Project;Towards Privacy-First security Enablers for 6G networks: The PRIVATEER Approach;RISC-V-Based Platforms for HPC: Analyzing Non-functional Properties for Future HPC and Big-Data Clusters;enabling an Isolated and Energy-Aware Deployment of Computationally Intensive Kernels on Multi-tenant Environments;quantum Computing Research Lines in the Italian Center for Supercomputing;Devices and Architectures for Efficient Computing In-Memory (CIM) Design.

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Use of blockchain in health sensor networks to secure information integrity and accountability 13th

Use of blockchain in health sensor networks to secure inform...

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13th international conference on Emerging Ubiquitous systems and Pervasive networks, EUSPN / The 12th international conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2022 / Affiliated Workshops

作者： Godawatte, Kithmini Branch, Philip But, Jason Swinburne University of Technology John Street Hawthorn Australia

Accountability and integrity are likely to play a key role in emerging health sensor network environments. Since health sensor devices are low capability devices, they are particularly susceptible to modern cyber threats. Blockchain technology is an emerging security technology that was explored by researchers in the past few years. Blockchain technology has been designed to protect information integrity and accountability. In a distributed manner making forgery and destruction of data can be very difficult. In this survey paper, we are specifically focused on how blockchain platforms adopt to the healthcare industry and achieve data integrity and accountability. Also, we will discuss the benefits and challenges of blockchain technology, and future blockchain research directions. Blockchain technology can be used to prevent unauthorized information access and enhance the security of sensor data. The use of blockchain technology in health sensor networks may address many of the integrity and accountability concerns of health sensor networks. © 2022 Elsevier B.V.. All rights reserved.

关键词： Blockchain

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Detection of Anomalous Cryptocurrency Transactions Using Neural networks with Decision-making Explanation

Detection of Anomalous Cryptocurrency Transactions Using Neu...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Dmitry Levshun Ksenia Zhernova Andrey Chechulin St. Petersburg Federal Research Center of the Russian Academy of Sciences Saint Petersburg Russia

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The objective of this paper is to explore an effective approach for detecting anomalies in cryptocurrency transactions using artificial intelligence techniques. The proposed method is compared with existing approaches for identifying illegal transactions within cryptocurrency networks. This research systematically analyzes various data processing methods applicable to the problem of detecting illicit transactions, as well as reviewing relevant studies in cryptocurrency transaction analysis, data visualization, and computer vision-based anomaly detection. Experimental evaluations on a cryptocurrency transaction dataset show significant insights, with results benchmarked against those obtained in prior studies on the same dataset (BABD). The findings suggest that the proposed method, utilizing statistical graph characteristics, deep and ensemble learning technologies, enhances the accuracy of identifying fraudulent transactions. This approach could be instrumental in developing software solutions for monitoring cryptocurrency transactions, potentially aiding in digital forensics and cybersecurity applications. The findings may benefit researchers in computer security and developers focused on information security systems.

关键词： Adaptation models Accuracy Sensitivity Neural networks Digital forensics Information security Cryptocurrency Fraud Ensemble learning Anomaly detection

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