IPv6 Networks Research Articles

A Localized Forwarding Scheme for the Shared-Prefix Model-Based Vehicular Mobility Management Over IEEE WAVE IPv6 Networks

A Localized Forwarding Scheme for the Shared-Prefix Model-Based Vehicular Mobility Management Over IEEE WAVE IPv6 Networks

Design of a Hybrid Bio-inspired Model for Improving Addressing Capabilities of IPv6 Networks

Addressing in IPv6 networks is a complex task, that involves multiple decision criterion that include base addressing, subnet addressing, location & energy aware addressing, and temporal performance aware addressing constraints. To incorporate these constraints a wide variety of models are proposed by researchers, and most them utilize location-aware addressing and do not consider multimodal parameters. Schemes that consider these parameters are either highly complex, or cannot be scaled for heterogeneous network scenarios. To overcome these limitations, this text proposes design of a novel hybrid bioinspired model that assists in improving addressing capabilities of IPv6 networks. The proposed model bee colony optimization (BCO), genetic algorithm (GA), and particle swarm optimization (PSO) in order to improve addressing quality for different network types. Initially, GA is used to stochastically assign location-specific addresses to a simulated network, which is incrementally tuned by PSO via a cognitive & social learning process. The fine-tuned addresses are further optimized via integration of BCO model, which assists in integrating energy awareness. Final addresses are initially simulated with exhaustive communication test, and then deployed to real-time networks for optimized operations. Due to which, the assigned addresses are observed to be delay & energy efficient, thereby assisting in deploying them for real-time use cases. The proposed addressing model was tested under different scaled networks, and an energy efficiency of 8.3%, with delay reduction of 6.5% was achieved when compared with various state-of-the-art methods, which assists in deploying the model for multiple scaled network scenarios.

Analysis of Group Intelligence Machine Learning Optimization Algorithms to enhance IPv6 Addressing

The current variety of the Internet Protocol is IPv6 for addressing the networking devices as per the mechanisms proposed by past researchers to minimize the delay and improve the efficiency. It is additionally recognized as a classless addressing scheme that locates computing machines throughout the web so they can be located. Among the mechanisms, the Group Intelligence (GI) algorithms that consists of Evolutionary and Group based Optimization techniques have gained attention of the researchers to contribute an effective optimized solution towards solving an optimization problem. Thus, the aim of this paper is to study the implementation, features and effectiveness of different GI based metaheuristic machine learning optimization algorithms, so as to contribute in future towards designing and upgrade to a new IPv6 addressing scheme by blending the benefits of the metaheuristic algorithms to find good or near –optimal solutions at a reasonable computation cost in IPv6 network to enhance the execution result of addressing scheme on real time data.

A Comparative Performance Evaluation of IPv4/IPv6 Using Network Simulation and Virtualization Tools

Internet users are expected to grow from 4.5 billion in 2020 to 5.3 billion by 2023. In terms of the population, this represents 66% of the global population. However, the growth of connected devices and internet users is going very fast. Not yet as the number of the internet of things (IoT) applications such as video surveillance, smart meters, and tracking and the machine to machine increased as well. The address space of the IPv4 cannot meet these requirements and this growth in the connected devices. According to that another IP address scheme designed to meet the requirements and the needs for the IoT & M2M and the growth of the number of internet users. This is the IPv6 which consist of 128 bit and can provide a huge number of addresses. The limitation of the addressing space was solved by the IPv6 but the problem arises in the migration from IPv4 to IPv6 as the migration cannot happen overnight so transition mechanisms developed to allow the communication between both IPv4 and IPv6 network. This paper aims to study the performance parameters such as the delay, the throughput, and the TCP establishment time which could be affected by the transition mechanism. The methodology that used in this project is using a network simulation tools such as GNS3 and virtual box to simulate the network scenarios and network performance check such as Wireshark and iperf3 tools to analyse the traffic. The results in this project show that if the delay is the main concern, then the tunneling machine will be the best choice, behind that when the TCP flow playing an important role for the network engineer then the tunneling machine will be the best option. However, the dual-stack method is considered the best in terms of throughput.

Intrusion Detection System of IPv6 Network

Abstract: There are variations within the network of the differential computing system's underlying IPv6 network. There are different blind zones for security detection. It is thoroughly investigated how the automatic data processing system differs from the underlying IPv6 network. An enhanced underlying IPv6 network security detection module design approach is suggested for the multicomputer system based on this concept. One gets the comprehensive module design plan. The new technologies like receiving thread domain, object forwarding domain, and file buffer are taken into account by the network security detection system. It can alleviate the challenges with security detection brought on by IPv6 network variations in the underlying computing system. Thus, IPv6 networks have a significantly lower number of blind zones.

HONEYPOT BASED INTRUSION DETECTION SYSTEM

Honeypot is a computer security mechanism set to detect, deflect or counteract attempts at unauthorized use of information. It has enormous potential for the security community. This can be modeled after any digital asset, including software applications, servers or the network itself. It is intentionally and purposefully designed to look like a legitimate target, resembling the model in terms of structure, components and content and is meant to convince the adversary that they have accessed the actual system and encourage them to spend time within this controlled environment. It acts better than firewalls or intrusion detection system by detecting encrypted attacks in IPv6 networks to capture different types of frauds.

Novel Machine Learning (ML) Algorithms to Classify IPv6 Network Traffic in Resource-Limited Systems

Providing machine learning (ML) based security in heterogeneous IoT networks including resource-constrained devices is a challenge because of the fact that conventional ML algorithms require heavy computations. Therefore, in this paper, lightweight ProtoNN, CMSIS-NN, and Bonsai tree ML algorithms were evaluated by using performance metrics such as testing accuracy, precision, F1 score and recall to test their classification ability on the IPv6 network dataset generated on resource-scarce embedded devices. The Bonsai tree algorithm provided the best performance results in all metrics (98.8 in accuracy, 98.9% in F1 score, 99.2% in precision, and 98.8% in recall) compared to the ProtoNN, and CMSIS-NN algorithms.

A Programmable SRv6 Processor for SFC

A reconfigurable match table (RMT) is a programmable pipeline architecture for packet processing. In order to enable the programmable data plane to support segment routing IPv6 (SRv6) and other network protocols, this paper extends the deparser based on RMT. The paper uses the extended deparser and two RMT pipelines to build a protocol-independent network slice programmable data plane model named as the programmable SRv6 processor. We design it primarily for segment identifier (SID) processing of SRv6. We have proved that it can support SRv6, multiple semantics for SIDs, micro segment ID, multi-protocol label switching, and a service function chain (SFC). This architecture has broad application prospects. Experimental results on an FPGA showed that the extended deparser could achieve 100 Gbps throughput for 512B packets with few resources.

An efficient and reliable service customized routing mechanism based on deep learning in IPv6 network

Best-effort service model of traditional routing is gradually hard to meet the personalized demands under the rapid development of network technologies (e.g. 5G and IPv6). Therefore, service customization should be considered. In this work, a service customized routing mechanism based on deep learning in IPv6 network is proposed, which includes deep learning-based service customization module, reliability evaluation module, and routing calculation module. The first module uses neural network to learn the complex service customization function, which can quickly output win-win customized service strategies based on user demands. The second module can quantify the reliability of service routing paths, where not only the link status of IPv6 Neighbor Unreachable Detection (NUD) is considered, but also propose link performance weights to ensure the reliability of differentiated service performance. The third module uses the gray wolf optimization algorithm to calculate an optimal routing path to forward services with the customized strategies as the constraints and the maximum reliability and minimum cost as the goal. Finally, the mechanism is tested on the IPv6 Source Address Validation Improvement (SAVI) platform, which can reduce the execution time by 12.25% and improve the average routing reliability, user and ISP satisfaction by 9.0%, 40.45% and 7.4%, respectively.

Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks

Internet Protocol version six (IPv6) is more secure than its forerunner, Internet Protocol version four (IPv4). IPv6 introduces several new protocols, such as the Internet Control Message Protocol version six (ICMPv6), an essential protocol to the IPv6 networks. However, it exposes IPv6 networks to some security threats since ICMPv6 messages are not verified or authenticated, and they are mandatory messages that cannot be blocked or disabled. One of the threats currently facing IPv6 networks is the exploitation of ICMPv6 messages by malicious actors to execute distributed denial of service (DDoS) attacks. Therefore, this paper proposes a deep-learning-based approach to detect ICMPv6 flooding DDoS attacks on IPv6 networks by introducing an ensemble feature selection technique that utilizes chi-square and information gain ratio methods to select significant features for attack detection with high accuracy. In addition, a long short-term memory (LSTM) is employed to train the detection model on the selected features. The proposed approach was evaluated using a synthetic dataset for false-positive rate (FPR), detection accuracy, F-measure, recall, and precision, achieving 0.55%, 98.41%, 98.39%, 97.3%, and 99.4%, respectively. Additionally, the results reveal that the proposed approach outperforms the existing approaches.

Measurement for encrypted open resolvers: Applications and security

Encrypted DNS has been proposed to mitigate the vulnerability of traditional DNS to surveillance and tampering. Some encrypted DNS protocols, like DNS over HTTPS (DoH) and DNS over TLS (DoT), have been promoted by the community and supported by the industry. However, although encrypted DNS are proposed to protect users’ privacy and security, the security of their application in practice is still unknown.In this study, we focus on DoH and DoT to study the application and security of encrypted DNS from the perspective of open resolvers. We first propose a novel encrypted open resolvers discovery method. It enables us to implement a comprehensive discovery of encrypted open resolvers across the IPv4 network. Furthermore, we conduct security measurements on encrypted open resolvers for the recursive and iterative resolution they perform. In our measurements, we conduct the most comprehensive discovery to date and detect 5.7k open DoH resolvers and 9.6k open DoT resolvers in the IPv4 network. Moreover, we have observed several security issues of the encrypted open resolvers. For example, we find 10.2% of the open DoH resolvers and 60.7% of the open DoT resolvers use invalid certificates; 19.2% of the DNSSEC-supporting open DoH resolvers do not actually implement DNSSEC validation in resolution, including those from famous providers Facebook and Alidns. Our research reveals pervasive misconfigurations of the encrypted open resolvers in the wild. We recommend that resolver administrators need carefully check and maintain the DNS security configurations on their encrypted resolvers.

Cross-Layer Design Based N-ary Huffman Coding for Performance Analysis of DSDV Routing Protocol in MANETs

The implementation of the optimal Huffman coding technique for achieving proficient data compression, lower information redundancy and minimal utilization of the scarce bandwidth is essential to handle the effective processing of massive network data. In this paper, we employ cross-layer design technique among the data link, network and presentation layers of the traditional protocol stack to develop the improved DSDV routing protocol based on n-ary Huffman coding procedure. The entropy-based adaptive prefix codewords with variable length are assigned to the probability of packet successful delivery across the ad-hoc wireless network. Several coding and network efficiency parameters are deployed to assess the performance of the proposed routing scheme under three distinctive IPv4 network scenarios. These investigated network protocols include the default IPv4, multi IPv4 and dynamic IPv4 routing methodologies. Diversified simulation settings are employed with deviating network size to measure the multitude of essential wireless network characteristics incorporating the average delay, packet error rate, packet delivery fraction, data rate, ping loss rate, entropy rate, and reception cache hit. In addition, the set of key data compression/coding indicators are examined through comprehensive numerical analysis such as codeword length, source information rate, mean redundancy, and coding efficiency. Beyond, we significantly compare the performance of our developed cross-layer coded mobile routing model with several previous algorithms to validate its enhanced feasibility and superiority in terms of crucial network operation metrics such as throughput, packet drop rate, mean delay and packet delivery ratio.

Weighted NSFIB Aggregation With Generalized Next Hop of Strict Partial Order

The size of the global routing table has been growing at an alarming rate. With the exhaustion of IPv4 addresses and the gradual deployment of IPv6 networks, the growth rate will continue to accelerate in the future. Although modern high performance routers provide enough line-card memory, Internet Service Providers (ISPs) cannot afford to upgrade their routers as fast as the growth of global routing tables. In this paper, we propose an algorithm to calculate the generalized next hops with strict partial order (GSPO next hops) of a network prefix and use them for the aggregation of the Nexthop-Selectable Forwarding Information Base (NSFIB). Since the existing NSFIB aggregation algorithm may introduce path stretch, we also propose a weighted NSFIB aggregation algorithm to effectively control path stretch under a given upper limit of the FIB size. Experiment results show that our algorithm can shrink the FIB size by at most 97% under IPv4 networks, and at most 95% under IPv6 networks. Under a given upper limit of the FIB size, our algorithm can reduce the path stretch by at least 22%.

YSF: A 6TiSCH Scheduling Function Minimizing Latency of Data Gathering in IIoT

Data gathering systems in the Industrial IoT require an end-to-end latency as low as one second with coverage of a few hundred meters. The 6TiSCH standard is well suited for these types of applications. A 6TiSCH network is a multi-hop wireless IPv6 network which uses Time Slotted Channel Hopping (TSCH). TSCH is a medium access mode of IEEE802.15.4 which provides deterministic properties, and increases robustness against external interference and multipath fading. A key component of TSCH is its scheduling function that builds the communication schedule, which greatly impacts network performance. Although there are several proposed TSCH scheduling solutions in the literature, most of them are not directly applicable to 6TiSCH for real-world deployments because they fail to take into consideration the dynamics of a network. Some of them assume a fixed routing topology, which does not match 6TiSCH where the routing topology dynamically changes with the radio environment. In this article, we propose a full-featured 6TiSCH scheduling function called YSF, that autonomously takes into account all aspects of network dynamics, including network formation phase and parent switching. YSF aims at minimizing latency and maximizing reliability for data gathering applications. We evaluate YSF by simulation, and compare it to MSF, the state-of-art scheduling function being standardized by the IETF 6TiSCH working group.

Labelled Dataset on Distributed Denial-of-Service (DDoS) Attacks Based on Internet Control Message Protocol Version 6 (ICMPv6)

The most dangerous attack against IPv6 networks today is a distributed denial-of-service (DDoS) attack using Internet Control Message Protocol version 6 (ICMPv6) messages. Many ICMPv6-DDoS attack detection mechanisms rely on self-created datasets because very few suitable ICMPv6-DDoS attack datasets are publicly available due to privacy and security concerns. When implemented in a real network, however, a detection system that relies on a dataset with incorrect packet or flow representation and contains unqualified features generates a large number of false alerts. The goal of this work is to create a comprehensive ICMPv6-DDoS attack dataset that can be used for tuning, benchmarking, and evaluating any detection systems designed to detect ICMPv6-DDoS attacks. The proposed datasets met the criteria for a good dataset, ensuring their usefulness to other researchers. A GNS3 network simulation tool is used to simulate an IPv6 network and generate ICMPv6 traffic for the dataset. The generated traffic contains both normal and abnormal ICMPv6 traffic, with the abnormal traffic containing ten different ICMPv6-DDoS attacks based on RA and NS message flooding. Five classifiers were chosen, varying in terms of type, classification performance, and the number of features used, and the results were as follows: decision tree 80%, support vector machine 78%, naïve Bayes 80%, k -nearest neighbours 81%, and neural networks 81%. The proposed dataset has been shown to accurately represent attack traffic in tests, with a high detection accuracy and a low false-positive rate.

Analysis of BGP4 Peering Establishment Time on IPv6 Connection over 6PE and 6VPE

Nowadays, because of the exhaustion of IPv4 address space, IPv6 is increasingly being used on enterprise networks. Usually, an enterprise uses an MPLS network from a Service Provider to interconnect their IPv4 network sites. Although MPLS Service Providers mostly built their MPLS backbone based on IPv4, their MPLS backbone have the capability to transport IPv6 traffic of their customers. Two methods can be used by the MPLS Service Provider to connect its customer IPv6 network, which is 6PE (IPv6 Provider Edge Routers) and 6VPE (IPv6 VPN Provider Edge Router). Enterprises generally use a BGP routing protocol to interconnect their networks, and they need to use the best method that suits their requirement from their MPLS Service Provider to transport their IPv6 traffic (including the BGP protocol). The MPLS Service Providers need to consider the advantages and disadvantages of both methods. This paper illustrates the analysis of BGP4 (current BGP version) IPv6 peering establishment time over 6PE and 6VPE methods. The MPLS Service Providers can use the analysis results of this study to determine the suited method to interconnect its customers' IPv6 networks.

A New Method to Boost VoIP Performance Over IPv6 Networks

Abstract The demands on virtual communication have increased noticeably during the COVID-19 pandemic lockdown. As an essential part of virtual communication, VoIP should be promoted to achieve the desired level of performance. One critical area investigated in VoIP is the bandwidth utilization (BWU) of the VoIP over IPv6 networks. Enhancing BWU will impact call capacity and quality; it increases call capacity and boosts call quality. Unfortunately, a considerable amount of bandwidth is wasted when running VoIP over IPv6 networks. This is due to the large size of the IPv6 header and the small size of the speech frame. This paper proposes a new method to handle the inefficient BWU when running VoIP over IPv6 networks. The proposed method combines multiplexing multiple VoIP packets in one IPv6 header and using the superfluous fields to carry a portion of the speech frame. Therefore, the proposed is called packet multiplexing and carrier fields (PMCF). Investigation of the PCMF method has been done using four metrics to measure the promotion in BWU, namely calls capacity, header size, bandwidth saving, and speech frame shortening metrics. With the four metrics, the PMCF method has outperformed the comparable methods. For instance, the call capacity has been promoted by up to 269% compared to the typical IPv6 method in the tested scenarios. Therefore, the PMCF method is a feasible solution to facilitate the BWU of VoIP when running VoIP over IPv6 networks.

Saving the Bandwidth of IPv6 Networks Using the Fields of the Packet Header

IPv6 protocol is the future of IP networks due to its large IP address capacity. One of the key consequences of this large capacity is that the IP protocol header has enlarged from 20-byte in IPv4 to 40-byte in IPv6. This will consume a considerable share of the bandwidth (BW) for VoIP applications, which produce small packets in tens of bytes only. To handle this issue, we have introduced an efficient technique to use the superfluous fields in the VoIP packet header, including the IPv6 header, to hold the voice data of the packet. This introduced technique is called the Eliminator technique because it shortens or eliminates the packet payload. The Eliminator technique has been analyzed against the typical technique of transferring the VoIP data. The BW utilization of the two techniques has been compared with different codecs namely G.726, G.728, and G.723.1 codecs. The analysis showed that the BW wasting has alleviated by 16.4%, 19.1%, and 25% with these codecs respectively. This BW utilization improvement will indirectly boost the quality of the VoIP calls, especially in the wireless environments. Accordingly, the Eliminator technique is a viable solution for alleviating the wasted BW when running VoIP in IPv6 networks.

Performance Analysis and Functionality Comparison of First Hop Redundancy Protocol IPV6

First hop redundancy protocols (FHRP) are an essential tool for improving IP networks’ availability. FHRP are protocols used to manage and maintain the network default gateway router by using one or more redundant routers that will take over in case of default router failure. In this paper we present the first hop redundancy concept and the means for its realization in IPv6 network. We evaluate three FHRP protocols, namely, the Hot Standby Router Protocol (HSRPv6), Virtual Router Redundancy Protocol (VRRPv3), and Gateway Load Balancing (GLBPv6) using GNS3 tools. The First Hop Redundancy Protocols have been implemented, tested, optimized, and compared to one another in terms of convergence time, packet loss and convergence time. The comparison indicates which protocol is the best in each scenario and which is the best overall among the three protocols.

A Vehicular Mobility Management Scheme for a Shared-Prefix Model Over IEEE WAVE IPv6 Networks

The IETF IPWAVE Working Group is developing an IPv6-based solution to establish connectivity for V2V and V2I communication. It proposes a vehicular mobility management scheme based on PMIPv6. In this scheme, a shared-prefix model is proposed for its vehicular link model. The shared-prefix model is to share a common prefix among several RSUs. When using it, a vehicle in the same prefix domain does not need to change its IPv6 address even if its serving RSU is changed. So its CNs do not notice the movement of the vehicle. This concept is very similar with PMIPv6, because PMIPv6 also hides the vehicle’s movement to its CNs if PMIPv6 does not use the route optimization. However, current IPWAVE draft of vehicular mobility management has several problems. Firstly, it requires a vehicular neighbor discovery, which is not compatible with standard IPv6 NDP. Secondly, PMIPv6 assumes that it acquires a MN-ID and a profile of a mobile node during the network attachment, but there is no authentication in IEEE WAVE networks. So, such information cannot be acquired in IEEEWAVE networks.We propose a vehicular mobility management scheme for a shared-prefix model over IEEE WAVE IPv6 networks. The proposed scheme introduces VMM-NDP module. It handles the path changes due to the serving RSU changes within a shared prefix domain. It also hides the IPv6 address change to CNs by maintaining the previous IPv6 address when the vehicle’s current IPv6 address is changed due to the movement of the vehicle into new shared prefix domain. In this scheme, a vehicle can know its movement. So, there is no need to acquire a MN-ID. And also, since this scheme uses a shared-prefix model, there is no need to obtain a profile.