Secure Link State Routing for Mobile Ad Hoc Networks

• URL: http://arxiv.org/abs/2403.19859v1
• Date: Thu, 28 Mar 2024 22:10:40 GMT
• Title: Secure Link State Routing for Mobile Ad Hoc Networks
• Authors: Panagiotis Papadimitratos, Zygmunt J. Haas,
• Abstract summary: The proposed here Secure Link State Routing Protocol (SLSP) provides secure proactive topology discovery. SLSP can be employed as a stand-alone protocol, or fit naturally into a hybrid routing framework.
• Score: 2.965855310793378
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The secure operation of the routing protocol is one of the major challenges to be met for the proliferation of the Mobile Ad hoc Networking (MANET) paradigm. Nevertheless, security enhancements have been proposed mostly for reactive MANET protocols. The proposed here Secure Link State Routing Protocol (SLSP) provides secure proactive topology discovery, which can be multiply beneficial to the network operation. SLSP can be employed as a stand-alone protocol, or fit naturally into a hybrid routing framework, when combined with a reactive protocol. SLSP is robust against individual attackers, it is capable of adjusting its scope between local and network-wide topology discovery, and it is capable of operating in networks of frequently changing topology and membership.

Related papers

• Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks [82.3753728955968]
  We introduce a novel Mixture-of-Experts (MoE)-based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system.
  arXiv  Detail & Related papers  (2024-09-24T03:17:51Z)
• Secure Routing for Mobile Ad hoc Networks [2.965855310793378]
  We present a route discovery protocol that mitigates the effects of malicious behavior in MANET networks. Our protocol guarantees that fabricated, compromised, or replayed route replies would either be rejected or never reach back the querying node. The scheme is robust in the presence of a number of non-colluding nodes.
  arXiv  Detail & Related papers  (2024-03-01T09:50:00Z)
• A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
  The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure. This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus. We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
  arXiv  Detail & Related papers  (2024-01-19T14:52:04Z)
• Ejafa_protocol: A custom INC secure protocol [0.0]
  The protocol incorporates modern cryptographic primitives, including X25519 for key exchange and ChaCha20 for encryption. A key feature of the protocol is its adaptability to resource-constrained environments without compromising on security.
  arXiv  Detail & Related papers  (2024-01-05T12:51:19Z)
• SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems [51.97204522852634]
  We present a unified framework that aims to secure an online semantic communications system with three semantic protection modules. Specifically, these protection modules are able to encrypt semantics to be transmitted by an encryption method, mitigate privacy risks from wireless channels by a perturbation mechanism, and calibrate distorted semantics at the destination. Our framework enables an existing online SC system to dynamically assemble the above three pluggable modules to meet customized semantic protection requirements.
  arXiv  Detail & Related papers  (2023-09-04T06:34:43Z)
• Reinforcement Learning for Protocol Synthesis in Resource-Constrained Wireless Sensor and IoT Networks [1.462434043267217]
  The paper introduces the use of RL and Multi Armed Bandit (MAB), a specific type of RL, for Medium Access Control (MAC) It then introduces a novel learning based protocol synthesis framework that addresses specific difficulties and limitations in medium access for both random access and time slotted networks. The ability of independent protocol learning by the nodes makes the system robust and adaptive to the changes in network and traffic conditions.
  arXiv  Detail & Related papers  (2023-01-14T03:28:26Z)
• A scheme for multipartite entanglement distribution via separable carriers [68.8204255655161]
  We develop a strategy for entanglement distribution via separable carriers that can be applied to any number of network nodes. We show that our protocol results in multipartite entanglement, while the carrier mediating the process is always in a separable state with respect to the network.
  arXiv  Detail & Related papers  (2022-06-20T10:50:45Z)
• Learning Emergent Random Access Protocol for LEO Satellite Networks [51.575090080749554]
  We propose a novel grant-free random access solution for LEO SAT networks, dubbed emergent random access channel protocol (eRACH) eRACH is a model-free approach that emerges through interaction with the non-stationary network environment. Compared to RACH, we show from various simulations that our proposed eRACH yields 54.6% higher average network throughput.
  arXiv  Detail & Related papers  (2021-12-03T07:44:45Z)
• Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
  We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications. We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology. We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
  arXiv  Detail & Related papers  (2021-06-03T16:45:40Z)
• Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis [2.6397379133308214]
  This paper proposes a multi-agent reinforcement learning based medium access framework for wireless networks. The access problem is formulated as a Markov Decision Process (MDP), and solved using reinforcement learning with every network node acting as a distributed learning agent.
  arXiv  Detail & Related papers  (2021-02-02T17:13:37Z)
• Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer [12.018165291620164]
  We propose a novel reinforcement learning approach for simultaneous routing and spectrum access in wireless ad-hoc networks. A single agent makes all routing and spectrum access decisions as it moves along the frontier nodes of each flow. The proposed deep reinforcement learning strategy is capable of accounting for the mutual interference between the links.
  arXiv  Detail & Related papers  (2020-12-22T01:47:20Z)

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.