Related papers: Prisec II -- A Comprehensive Model for IoT Security: Cryptographic Algorithms and Cloud Integration

Related papers

Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities [0.0]
Traditional centralized security methods often struggle to balance privacy preservation and real-time threat detection in IoT networks. This study proposes a Federated Learning-Driven Cybersecurity Framework designed specifically for IoT environments. Secure aggregation of locally trained models is achieved using homomorphic encryption, allowing collaborative learning without exposing sensitive information.
arXiv Detail & Related papers (2025-02-14T23:11:51Z)
Secure Resource Allocation via Constrained Deep Reinforcement Learning [49.15061461220109]
We present SARMTO, a framework that balances resource allocation, task offloading, security, and performance. SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs. These enhancements highlight SARMTO's potential to revolutionize resource management in intricate distributed computing environments.
arXiv Detail & Related papers (2025-01-20T15:52:43Z)
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)
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)
Secure Outsourced Decryption for FHE-based Privacy-preserving Cloud Computing [3.125865379632205]
Homomorphic encryption (HE) is one solution for safeguarding data privacy, enabling encrypted data to be processed securely in the cloud. We propose an outsourced decryption protocol for the prevailing RLWE-based fully homomorphic encryption schemes. Our experiments demonstrate that the proposed protocol achieves up to a $67%$ acceleration in the client's local decryption, accompanied by a $50%$ reduction in space usage.
arXiv Detail & Related papers (2024-06-28T14:51:36Z)
Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems [1.749521391198341]
The integration of Internet of Things (IoT) applications in our daily lives has led to a surge in data traffic, posing significant security challenges. This paper focuses on improving the effectiveness of ML-based IDS at the edge level by introducing a novel method to find a balanced trade-off between cost and accuracy.
arXiv Detail & Related papers (2024-04-29T21:26:18Z)
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 Blockchain Solution for Collaborative Machine Learning over IoT [0.31410859223862103]
Federated learning (FL) and blockchain technologies have emerged as promising approaches to address these challenges. We present a novel IoT solution that combines the incremental learning vector quantization algorithm (XuILVQ) with blockchain technology. Our proposed architecture addresses the shortcomings of existing blockchain-based FL solutions by reducing computational and communication overheads while maintaining data privacy and security.
arXiv Detail & Related papers (2023-11-23T18:06:05Z)
Towards Scalable Wireless Federated Learning: Challenges and Solutions [40.68297639420033]
federated learning (FL) emerges as an effective distributed machine learning framework. We discuss the challenges and solutions of achieving scalable wireless FL from the perspectives of both network design and resource orchestration.
arXiv Detail & Related papers (2023-10-08T08:55:03Z)
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)
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)
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)
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT [82.33080550378068]
The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue. This paper studies the FL technology applications to manage IIoT equipment data in wireless network environments.
arXiv Detail & Related papers (2022-02-03T07:12:36Z)
RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning [75.677197535939]
We propose a simultaneous wireless power transfer and information transmission scheme for IoT devices with support from unmanned aerial vehicle (UAV) communications. In a first phase, IoT devices harvest energy from the UAV through wireless power transfer; and then in a second phase, the UAV collects data from the IoT devices through information transmission. We formulate a Markov decision process and propose two deep reinforcement learning algorithms to solve the optimization problem of maximizing the total network sum-rate.
arXiv Detail & Related papers (2021-08-05T23:55:44Z)
Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA [78.60275748518589]
Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks. A novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
arXiv Detail & Related papers (2021-01-02T15:21:08Z)
True-data Testbed for 5G/B5G Intelligent Network [46.09035008165811]
We build the world's first true-data testbed for 5G/B5G intelligent network (TTIN) TTIN comprises 5G/B5G on-site experimental networks, data acquisition & data warehouse, and AI engine & network optimization. This paper elaborates on the system architecture and module design of TTIN.
arXiv Detail & Related papers (2020-11-26T06:42:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.