Topology Discovery Research Articles

Stop-Probability-Based Network Topology Discovery Method

Stop-Probability-Based Network Topology Discovery Method

Vulnerabilities in SDN Topology Discovery Mechanism: Novel Attacks and Countermeasures

Vulnerabilities in SDN Topology Discovery Mechanism: Novel Attacks and Countermeasures

ServiceNet: resource-efficient architecture for topology discovery in large-scale multi-tenant clouds

Modern computing infrastructures are evolving due to virtualisation, especially with the advent of 5G and future technologies. While this transition offers numerous benefits, it also presents challenges. Consequently, understanding these complex systems, including networks, services, and their interconnections, is crucial. This paper introduces ServiceNet, a groundbreaking architecture that accurately performs the important task of providing understanding of a multi-tenant architecture by discovering the complete topology, crucial in the realm of high-performance distributed computing. Experimental results have been carried out in different scenarios in order to validate our approach, demonstrating the effectiveness of our approach in comprehensive multi-tenant topology discovery. The experiments, involving up to forty tenant, highlight the adaptability of ServiceNet as a valuable tool for real-time monitoring in topology discovery purposes, even in challenging scenarios.

Adaptive HELLO protocol for vehicular networks

In vehicular networks, the update of car Firmware Over The Air (FOTA) is becoming a challenging issue and it mainly relies on topology discovery of neighbouring nodes. Topology discovery in mobile wireless networks is usually done by using HELLO messages. Due to mobility, topology changes occur frequently and must be quickly discovered to avoid routing failures. Since the optimal HELLO frequency depends on parameters that are subject to changes (e.g., speed of nodes, density of nodes), it must be dynamically adjusted to obtain the best trade-off between the network load and the freshness of routing tables. Existing solutions assume random mobility, constant node density and average speed, which do not hold in vehicular networks because vehicles follow specific trajectory patterns (the roads) and density and speed evolve as a function of time (rush hour vs non-rush hour) and area (urban, rural, highway). In this paper, we first draw the specific features of a vehicular network at different times and spaces by analysing real datasets and then propose a dynamic neighbour discovery protocol, Vehicular Adaptive Neighbour discovery Protocol (VANP). VANP is a fully-distributed protocol that sends beacons at an optimal frequency without knowing it a priori. The objective is to reduce the frequency at which HELLO messages are sent to save bandwidth and energy while still preserving the quality of the neighbour discovery. Through extensive simulations run on real datasets, we show that the optimal HELLO frequency can be reached by maintaining a constant optimal turnover, independent from the speed of the nodes and by aiming at this turnover, nodes automatically use the optimal HELLO frequency. Results show that VANP allows the discovery of relevant neighbours by missing at most two neighbours over all scenarios and reducing the number of HELLO messages up to twice, hence saving bandwidth and energy.

Multipoint Relay Path for Efficient Topology Maintenance Algorithm in Optimized Link State Routing-Based for VANET

The Optimal Link State Routing (OLSR) protocol employs multipoint relay (MPR) nodes to disseminate topology control (TC) messages, enabling network topology discovery and maintenance. However, this approach increases control overhead and leads to wasted network bandwidth in stable topology scenarios due to fixed flooding periods. To address these challenges, this paper presents an Efficient Topology Maintenance Algorithm (ETM-OLSR) for Enhanced Link-State Routing Protocols. By reducing the number of MPR nodes, TC message generation and forwarding frequency are minimized. Furthermore, the algorithm selects a smaller subset of TC messages based on the changes in the MPR selection set from the previous cycle, adapting to stable and fluctuating network conditions. Additionally, the sending cycle of TC messages is dynamically adjusted in response to network topology changes. Simulation results demonstrate that the ETM-OLSR algorithm effectively reduces network control overhead, minimizes end-to-end delay, and improves network throughput compared to traditional OLSR and HTR-OLSR algorithms.

Security of Topology Discovery Service in SDN: Vulnerabilities and Countermeasures

Security of Topology Discovery Service in SDN: Vulnerabilities and Countermeasures

Digital Twin Enriched Green Topology Discovery for Next Generation Core Networks

Topology discovery is the key function of core network management since it utilizes the perception of data and mapping network devices. Nevertheless, it holds operational and resource efficiency complexities. For example, traditional discovery cannot perform predictive analysis to learn the network behavior. Moreover, traditional discovery periodically visits IP ports without considering the utilization levels, which leads to high resource usage and energy consumption. Hence, it is necessary to integrate intelligent methods into traditional discovery to deeply understand the behavioral pattern of a core network and recommend action to avoid these intrinsic complexities. Therefore, we propose a Digital Twin (DT) enriched Green Discovery Policy (DT-GDP) to serve a green discovery by using the increased intelligence and seamless assistance of DT. DT-GDP jointly uses the outputs of two modules to calculate the total energy consumption in Watts. In the energy module, we consider the service power, idle state power, and the cooling power of an IP port and derive a novel energy formula. In the visit decision module, we use Multilayer Perceptron (MLP) to classify the IP ports and recommend visit action. According to experimental results, we achieve a significant reduction in the visited ports by 53% and energy consumption by 66%.

Design of an In-Band Control Plane for Automatic Bootstrapping and Fast Failure Recovery in P4 Networks

In this paper, we present the design of an in-band control plane system for P4 networks. The proposed in-band control plane performs automatic switch bootstrapping, network status monitoring, topology discovery and fast failover. The authentication and registration of newly connected switches are managed by the controller without manual intervention. The system can recover from any single link or single node failure in a short period of time. The recovery control paths for each single link and single node failure are prepared in advance. We propose an algorithm that takes recovery time into account for preplanning the recovery paths. In addition to handling any single failure, the proposed control plane is survivable even if there are multiple failures in the network. We implement an emulated network to evaluate the proposed control plane system. The experimental results show that the system successfully provides all the target functions.

6Search: A reinforcement learning-based traceroute approach for efficient IPv6 topology discovery

Topology discovery can infer the interconnection relationship between network entities. A complete network topology is of great significance for network security analysis, application research, etc. However, due to the huge address space and uneven distribution of active addresses in the IPv6 Internet, it is infeasible to use brute-force traceroute to discover the entire topology. To address this problem, we propose 6Search, a target generation method based on reinforcement learning algorithm for IPv6 topology discovery. 6Search first obtains the routeable BGP prefixes and then carries out traceroute in each (/32) prefix. The number of probes allocated is dynamically adjusted based on the results of previous scans. Using the reinforcement learning algorithm, 6Search allocates more probes to prefixes with more address discovery in each scan iteration. Real-world experiments demonstrate that 6Search has better performance in terms of discovery efficiency, which is 24.1%–139.8% improvement over the existing methods.

Characterizing the Bitcoin network topology with Node‐Probe

AbstractIn blockchain networks, topology discovery is a prerequisite when investigating the network characteristics (e.g., performance and robustness), which can provide a deeper comprehension of the behavior of the nodes and topology dynamicity. In this paper, we conduct a longitudinal study on the Bitcoin topology by collecting network snapshots from 2018 to 2022 with Node‐Probe, our topology discovery technique that uses recursive scanning to find all reachable nodes in the Bitcoin network. Using Node‐Probe, we have collected 5‐week‐long snapshots (36‐day‐long snapshots) of the Bitcoin main network and analyzed the network properties, community structure, and topology dynamicity. We confirm that our approach achieves a precision of 99% with a recall of 98% in inferring the topology. Analytical results on community structure show that the Bitcoin network has more communities than what should be expected from a random network. Meanwhile, analytical results on dynamicity indicate that the topology stands firmly on heavy and long‐running nodes. Improving the propagation mechanism using master nodes could improve the propagation delay by proximity compared with the Bitcoin default protocol. Considering a K‐anonymity attack, any transaction from one of the autonomous systems containing only a single Bitcoin node can easily be linked to real users' IP information.

Combination Attacks and Defenses on SDN Topology Discovery

The topology discovery service in Software-Defined Networking (SDN) provides the controller with a global view of the substrate network topology, allowing for central management of the entire network. Unfortunately, emerging topology attacks can poison the network topology and result in unforeseeable disasters. Although researchers have made great efforts to mitigate this problem, security hazards still exist. In this paper, we propose Invisible Assailant Attack (IAA), the first combination topology attack capable of injecting and maintaining fake links even when 12 existing defense strategies are deployed simultaneously. IAA consists of 14 attack phases that apply multiple attack strategies. Attackers skillfully disguise the attack traffic in each phase so that it looks like normal network traffic, and perform these phases in a well-planned sequence, thereby bypassing existing defenses step by step. To mitigate this attack, we propose a Route Path Verification (RPV) mechanism that orchestrates multiple defense strategies to identify fake links. According to the experiments, RPV can successfully detect IAA with low overhead: its detection completes within 1 ms while its per-flow storage consumption is only a few KB.

Data Crawler Data Collection and QoS Optimization Path of Smart Course Online + Offline Evaluation Platform

Based on the development background of "Internet +", this paper introduces the advantages of the application of the smart teaching mode, analyzes the problems existing in the smart teaching of vocational education courses, and explores the countermeasures for the application of the smart teaching mode in vocational education. The influence of internal factors such as educational emotion and information teaching technology and external factors such as schools and social environment, web page analysis technology, database technology, Web technology and GIS technology for extraction, storage, and spatial analysis of real estate data, "smart learning platform", etc. The core link, the routing strategy is mainly implemented by the QoS routing control framework, and the physical topology discovery module in the framework, in order to analyze the internal elements of the teaching paradigm change from the perspective of technical support.

Discovering and Mapping Subnet Level Topology

<p>The research of Internet measurement has promoted the development of router-level topology discovery, while the subnet of the network layer can provide a more detailed intermediate complementary view. To improve the current level of router-level topology discovery, some researchers have proposed subnet discovery methods. However, the existing methods do not perform well in terms of efficiency and accuracy, which leads to the failure of the final resulting topology map. In this paper, we propose a new approach to discover subnets, which consists of a network pre-processing stage and a subnet inference stage. Given a set of target IP prefixes, the network pre-processing stage introduces a minimal probing overhead to improve probing efficiency. Based on the IP address allocation principle, the subnet inference stage utilizes a set of complementary inference rules to infer subnets. The experimental results show that our method can achieve a higher accuracy in discovering subnets while keeping lower probing overhead compared with existing methods. Finally, we utilize the proposed approach to discover the subnets of six geographically dispersed public Autonomous System (AS) networks and analyze their various subnet characteristics, including degree distribution.</p> <p> </p>

Software-Defined Small Cell-Linked Vehicular Networks: Architecture and Evaluation

Vehicle-to-everything services are in the implementation phase, and automakers agree that V2X would improve the safety-critical applications already deployed. 3GPP Release 12 introduces LTE-V for V2V and V2I services. The LTE-V is extended to C-V2X to support V2N. Because of the challenge of high mobility in the V2X system, cutting-edge technologies, such as SDN and small cell in 5G networks, pave the way to the next generation of vehicular networks. SDN is a network technology concept that divides the data and control planes. The OpenFlow protocol is used for communication between the control layer and the network layer in SDN. Different from wireless traditional cellular base stations, small cells are lower-power cell sites that are deployed every few blocks. Small cells can transmit data using mid- and high-band spectrums. Small cell-linked road side unit (RSU) is considered a key enabling technology because it has the capability to create a logical cluster platform residing at the edge of the network, which provides high computation performance. Accordingly, we consider a novel distributed software-defined small cell-linked road side unit vehicular network architecture (diSRsVN). Based on diSRsVN, logical software-defined on-board wireless vehicle, and topology discovery over diSRsVN are presented. The proposed architecture is evaluated under an omnet++ network simulator. The simulation results show the effectiveness of the proposed architecture, which improves the packet delivery ratio and minimizes end-to-end delay.

Securing Topology Control in SDWSNs Using Identity‐Based Cryptography

In software‐defined wireless sensor networks (SDWSNs), topology control is a fundamental procedure to maintain the global network topology. However, the open wireless channels of SDWSNs make it possible for an attacker to eavesdrop, replay, or modify the topology messages, thus posing a great threat to the network operations. The security of SDWSN topology control has not received enough attention yet. Identity‐based cryptography (IBC) may be fitter for SDWSNs due to its capability of generating the public key from the node identity directly, compared with traditional cryptography. In particular, identity‐based combined encryption and signature cryptography (IBCES) could encrypt and sign the messages using the same identity. As such, to secure the confidentiality, integrity, and authentication of topology information, we put forward a secure topology control mechanism based on IBCES. First, we use an identity‐based encryption authenticated key agreement scheme to implement the authentication of neighbor nodes and hop‐to‐hop verification via secure neighbor discovery and topology discovery processes. Then through the node admission and key establishment process, the end‐to‐end secure channels are established between the nodes, sinks, and Controller. Finally, secure topology collection and management processes supporting flat and hierarchical network structures are designed to guarantee the security of topology information. Theoretical analysis shows that our methods could satisfy the security needs of SDWSN topology control and resist several security attacks. The experimental results indicate that our mechanisms are suitable for SDWSNs.

Retracted: Immersive VR Network Management Analysis considering Automatic Topology Discovery Algorithms.

[This retracts the article DOI: 10.1155/2022/3625502.].

Immersive VR Network Management Analysis considering Automatic Topology Discovery Algorithms.

In order to make up for the deficiency of a two-dimensional user interface, enhance the visualization of network information, and comprehensively and effectively improve the efficiency of network management, this paper attempts to use virtual reality technology to design the network management system and uses the research results of related fields for reference to carry out the specific practice, providing a new idea for the design and development of the network management system. The automatic topology discovery algorithm is introduced into the network management analysis, and the immersive network management is modularized by using the method of dividing subnets. Finally, the test results show that through the functional design of the system and the hierarchical description of the structural model, the practical method is given. Finally, the operation process of the simulation system is analyzed in detail. The proposed method can effectively reduce the network load and reduce the network bandwidth and greatly improve the slow convergence of the penetration VR network topology.

High-Speed Path Probing Method for Large-Scale Network.

In large-scale network topology discovery, due to the complex network structure and dynamic change characteristics, it is always the focus of network topology measurement to obtain as many network paths as possible in a short time. In this paper, we propose a large-scale network path probing approach in order to solve the problems of low probing efficiency and high probing redundancy commonly found in current research. By improving the packet delivery order and the update strategy of time-to-live field values, we redesigned and implemented an efficient large-scale network path probing tool. The experimental results show that the method-derived tool can complete path probing for a sample of 12 million/24 network address segments worldwide within 1 hour, which greatly improves the efficiency of network path probing. Meanwhile, compared to existing methods, the proposed method can reduce the number of packets sent by about 10% with the same number of network addresses found, which effectively reduces probing redundancy and alleviates the network load.

DESIGN OF A MINIMAL OVERHEAD CONTROL TRAFFIC TOPOLOGY DISCOVERY AND DATA FORWARDING PROTOCOL FOR SOFTWARE-DEFINED WIRELESS SENSOR NETWORKS

Software-defined networking is a novel concept that is ported into wireless sensor networks to make them more manageable and customizable. unfortunately, the topology discovery and maintenance processes generate high overhead control packet exchange between the sensor nodes and the central controller leading to a deterioration of the network's performance. In this paper, a novel minimal overhead control traffic topology discovery and data forwarding protocol is proposed and detailed. The proposed protocol requires some changes to the topology discovery protocol implemented in SDN-WISE to improve its performance. The proposed protocol has been implemented within the IT-SDN framework for evaluation. The results show reduced overhead control traffic and increase, of about 20%, data packet delivery rate over the protocol in SDN-WISE.

Internet of Things Network Topology Discovery Algorithm Based on Wireless Sensors

Remote sensor network is a multijump self-coordinating organization framework shaped by countless energy-restricted miniature sensor hubs sent in the observing region. Be that as it may, the correspondence conventions of customary organization configuration cannot be straightforwardly applied to remote sensor organizations. The improvement of committed correspondence conventions and estimation techniques has turned into a pressing exploration subject in the field of remote sensor organizations. The research purpose of this paper is to research the Internet of Things network topology discovery algorithm based on wireless sensors. This paper analyzes and mines an adaptive neighbor discovery scheduling algorithm with lower latency, acquires the information of potential neighbor nodes based on existing neighbor nodes, discovers potential neighbor nodes by proactively waking up, and studies the information recommendation mechanism between neighbor nodes and compares. The intimacy between neighbor nodes (such as common neighbor rate) is used to selectively receive recommended information from neighbor nodes, thereby filtering redundant data information, lessening hub energy utilization, and accomplishing the motivation behind broadening the organization life cycle. A geography disclosure calculation in light of versatile specialists is proposed to tackle the geography revelation issue under the various leveled geography structure in remote sensor organizations. The calculation joins the qualities of portable specialist innovation and various leveled Internet of Things network geography and simultaneously further develops the relocation methodology of versatile specialists for information assortment, intermittent undertaking assignment, and organization status observing. Research shows that in the investigation of the effect of hub obligation cycle on framework execution, the energy utilization of the DDC-Group calculation is marginally expanded contrasted and the gathering calculation, and the increase is not more than 1%, but compared to the CNR-Group increase more.