Efficient ECC-based authentication scheme for fog-based IoT environment
- URL: http://arxiv.org/abs/2408.02826v1
- Date: Mon, 5 Aug 2024 20:47:49 GMT
- Title: Efficient ECC-based authentication scheme for fog-based IoT environment
- Authors: Mohamed Ali Shaaban, Almohammady S. Alsharkawy, Mohammad T. AbouKreisha, Mohammed Abdel Razek,
- Abstract summary: A signature scheme based on the elliptic curve digital signature algorithm (ECDSA) is proposed to improve the security of the private key and the time taken for key-pair generation.
Results indicate that, in comparison to the two-party ECDSA and RSA, the proposed ECDSA decreases computation time by 65% and 87%, respectively.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rapid growth of cloud computing and Internet of Things (IoT) applications faces several threats, such as latency, security, network failure, and performance. These issues are solved with the development of fog computing, which brings storage and computation closer to IoT-devices. However, there are several challenges faced by security designers, engineers, and researchers to secure this environment. To ensure the confidentiality of data that passes between the connected devices, digital signature protocols have been applied to the authentication of identities and messages. However, in the traditional method, a user's private key is directly stored on IoTs, so the private key may be disclosed under various malicious attacks. Furthermore, these methods require a lot of energy, which drains the resources of IoT-devices. A signature scheme based on the elliptic curve digital signature algorithm (ECDSA) is proposed in this paper to improve the security of the private key and the time taken for key-pair generation. ECDSA security is based on the intractability of the Elliptic Curve Discrete Logarithm Problem (ECDLP), which allows one to use much smaller groups. Smaller group sizes directly translate into shorter signatures, which is a crucial feature in settings where communication bandwidth is limited, or data transfer consumes a large amount of energy. The efficiency and effectiveness of ECDSA in the IoT environment are validated by experimental evaluation and comparison analysis. The results indicate that, in comparison to the two-party ECDSA and RSA, the proposed ECDSA decreases computation time by 65% and 87%, respectively. Additionally, as compared to two-party ECDSA and RSA, respectively, it reduces energy consumption by 77% and 82%.
Related papers
- CANDoSA: A Hardware Performance Counter-Based Intrusion Detection System for DoS Attacks on Automotive CAN bus [45.24207460381396]
This paper presents a novel Intrusion Detection System (IDS) designed for the Controller Area Network (CAN) environment.<n>A RISC-V-based CAN receiver is simulated using the gem5 simulator, processing CAN frame payloads with AES-128 encryption as FreeRTOS tasks.<n>Results indicate that this approach could significantly improve CAN security and address emerging challenges in automotive cybersecurity.
arXiv Detail & Related papers (2025-07-19T20:09:52Z) - Backscattering-Based Security in Wireless Power Transfer Applied to Battery-Free BLE Sensors [44.99833362998488]
Integration of security and energy efficiency in Internet of Things systems remains a critical challenge.<n>This paper explores the scalability and protocol-agnostic nature of a backscattering-based security mechanism by integrating it into Bluetooth Low Energy battery-free Wireless Sensor Network.
arXiv Detail & Related papers (2025-07-17T12:15:09Z) - DCentNet: Decentralized Multistage Biomedical Signal Classification using Early Exits [4.44410626000765]
DCentNet partitions a single CNN model into multiple sub-networks using EEPs.
EEPs compress large feature maps before transmission, significantly reducing wireless data transfer and power usage.
A genetic algorithm is used to optimize EEP placement, balancing performance and complexity.
arXiv Detail & Related papers (2025-01-31T04:24:39Z) - Efficient and Secure Cross-Domain Data-Sharing for Resource-Constrained Internet of Things [2.5284780091135994]
We propose an efficient, secure blockchain-based data-sharing scheme for the Internet of Things.
First, our scheme adopts a distributed key generation method, which avoids single point of failure.
Also, the scheme provides a complete data-sharing process, covering data uploading, storage, and sharing, while ensuring data traceability, integrity, and privacy.
arXiv Detail & Related papers (2024-11-14T06:53:03Z) - FL-DABE-BC: A Privacy-Enhanced, Decentralized Authentication, and Secure Communication for Federated Learning Framework with Decentralized Attribute-Based Encryption and Blockchain for IoT Scenarios [0.0]
This study proposes an advanced Learning (FL) framework designed to enhance data privacy and security in IoT environments.
We integrate Decentralized Attribute-Based Encryption (DABE), Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC) and technology.
Unlike traditional FL, our framework enables secure, decentralized authentication and encryption directly on IoT devices.
arXiv Detail & Related papers (2024-10-26T19:30:53Z) - Homomorphic Encryption-Enabled Federated Learning for Privacy-Preserving Intrusion Detection in Resource-Constrained IoV Networks [20.864048794953664]
This paper proposes a novel framework to address the data privacy issue for Federated Learning (FL)-based Intrusion Detection Systems (IDSs) in Internet-of-Vehicles (IoVs) with limited computational resources.
We first propose a highly-effective framework using homomorphic encryption to secure data that requires offloading to a centralized server for processing.
We develop an effective training algorithm tailored to handle the challenges of FL-based systems with encrypted data.
arXiv Detail & Related papers (2024-07-26T04:19:37Z) - Leakage-Resilient and Carbon-Neutral Aggregation Featuring the Federated AI-enabled Critical Infrastructure [42.688679691088204]
We propose a leakage-resilient, communication-efficient, and carbon-neutral approach for ACI networks.
We show that CDPA can reduce communication cost by half while preserving model utility.
We highlight existing benchmarks that generate 2.6x to over 100x more carbon emissions than CDPA.
arXiv Detail & Related papers (2024-05-24T06:35:09Z) - Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors [0.9831489366502302]
Homomorphic encryption schemes are promising solutions as they enable the processing and execution of operations on IoT data while still encrypted.
We propose a novel privacy-preserving anomaly detection solution designed for homomorphically encrypted data generated by IoT devices.
arXiv Detail & Related papers (2024-03-14T12:11:25Z) - TernaryVote: Differentially Private, Communication Efficient, and
Byzantine Resilient Distributed Optimization on Heterogeneous Data [50.797729676285876]
We propose TernaryVote, which combines a ternary compressor and the majority vote mechanism to realize differential privacy, gradient compression, and Byzantine resilience simultaneously.
We theoretically quantify the privacy guarantee through the lens of the emerging f-differential privacy (DP) and the Byzantine resilience of the proposed algorithm.
arXiv Detail & Related papers (2024-02-16T16:41:14Z) - Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS)
FLEKD enables a more flexible aggregation method than conventional model fusion techniques.
Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
arXiv Detail & Related papers (2024-01-22T14:16:37Z) - A high throughput Intrusion Detection System (IDS) to enhance the security of data transmission among research centers [39.65647745132031]
This paper presents a packet sniffer that was designed using a commercial FPGA development board.
The system can support a data throughput of 10 Gbit/s with preliminary results showing that the speed of data transmission can be reliably extended to 100 Gbit/s.
It is particularly suited for the security of universities and research centers, where point-to-point network connections are dominant.
arXiv Detail & Related papers (2023-11-10T14:30:00Z) - SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers [50.608828039206365]
We propose SOCI+ which significantly improves the performance of SOCI.
SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive.
Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.4 times more efficient in computation and 40% less in communication overhead.
arXiv Detail & Related papers (2023-09-27T05:19:32Z) - Multiagent Reinforcement Learning with an Attention Mechanism for
Improving Energy Efficiency in LoRa Networks [52.96907334080273]
As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions.
We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa)
Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
arXiv Detail & Related papers (2023-09-16T11:37:23Z) - Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual
Antenna Arrays [55.736718475856726]
Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT)
Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points.
We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
arXiv Detail & Related papers (2023-08-03T02:49:50Z) - A Safe Genetic Algorithm Approach for Energy Efficient Federated
Learning in Wireless Communication Networks [53.561797148529664]
Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner.
Despite the existing efforts made in FL, its environmental impact is still under investigation, since several critical challenges regarding its applicability to wireless networks have been identified.
The current work proposes a Genetic Algorithm (GA) approach, targeting the minimization of both the overall energy consumption of an FL process and any unnecessary resource utilization.
arXiv Detail & Related papers (2023-06-25T13:10:38Z) - Task-Oriented Integrated Sensing, Computation and Communication for
Wireless Edge AI [46.61358701676358]
Edge artificial intelligence (AI) has been proposed to provide high-performance computation of a conventional cloud down to the network edge.
Recently, convergence of wireless sensing, computation and communication (SC$2$) for specific edge AI tasks, has aroused paradigm shift.
It is paramount importance to advance fully integrated sensing, computation and communication (I SCC) to achieve ultra-reliable and low-latency edge intelligence acquisition.
arXiv Detail & Related papers (2023-06-11T06:40:51Z) - Securing IoT Communication using Physical Sensor Data -- Graph Layer
Security with Federated Multi-Agent Deep Reinforcement Learning [12.941755390387295]
Internet-of-Things (IoT) devices are often used to transmit physical sensor data over digital wireless channels.
Traditional Physical Layer Security (PLS)-based cryptography approaches rely on accurate channel estimation and information exchange for key generation.
We present a new concept called Graph Layer Security (GLS), where digital keys are derived from physical sensor readings.
arXiv Detail & Related papers (2023-02-24T12:10:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.