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Internet backbone refers to the principal data routes between large, strategically interconnected networks and core routers on the Internet. Internet backbone router is essentially the core router of Internet backbone and its performance is mainly relevant to the reliability of its mainboard. The mainboard is an embedded system consisting of hardware and software. Its reliability testing involves executing a number of test cases, which are designed to expose potential defects, under harsh environmental conditions. The testing process is largely prolonged due to the dramatic increase of the number of test cases, mainly due to the continuous increase and upgrade of its functional modules. Thus, there is a big demand from industry to improve the reliability testing efficiency and effectiveness. In this work, we exploit the principles of regression testing in software maintenance: test case selection and prioritization, and construct two testing planning models to largely reduce the testing time as well as to improve the effectiveness of failure detections. The former is a two-step model we introduced in previous work that optimizes test case selection and test case sequencing sequentially. The latter, an integrated model is newly developed, optimizing the test case selection and sequencing simultaneously with the precedence constraints among the test cases. Moreover, we propose exact algorithms based on branch-and-price for solving these two models. Finally, we present a case study demonstrating that the integrated model outperforms the two-step method and the advantage is more significant if the sequencing objective has greater weight in the integrated objective function.

This paper proposes a general-purpose anomaly detection mechanism for Internet backbone traffic named GAMPAL (General-purpose Anomaly detection Mechanism using Prefix Aggregate without Labeled data). GAMPAL does not require labeled data to achieve general-purpose anomaly detection. For scalability to the number of entries in the BGP RIB (Border Gateway Protocol Routing Information Base), GAMPAL introduces prefix aggregate. The BGP RIB entries are classified into prefix aggregates, each of which is identified with the first three AS (Autonomous System) numbers in the AS_PATH attribute. GAMPAL establishes a prediction model for traffic sizes based on past traffic sizes. It adopts a LSTM-RNN (Long Short-Term Memory Recurrent Neural Network) model that focuses on the periodicity of the Internet traffic patterns at a weekly scale. The validity of GAMPAL is evaluated using real traffic information, BGP RIBs exported from the WIDE backbone network (AS2500), a nationwide backbone network for research and educational organizations in Japan, and the dataset of an ISP (Internet Service Provider) in Spain. As a result, GAMPAL successfully detects anomalies such as increased traffic due to an event, DDoS (Distributed Denial of Service) attacks targeted at a stub organization, a connection failure, an SSH (Secure Shell) scan attack, and anomaly spam.

Long-haul backbone communication networks provide internet services across a region or a country. The access to internet at smaller areas and the functioning of other critical infrastructures rely on the long-haul backbone high speed services and resilience. Hence, such networks are key for the decision-making of internet service managers and providers, as well as for the management and control of other critical infrastructures. This paper proposes a critical link analysis of the physical infrastructure of the UK internet backbone network from a dynamic, complex network approach. To this end, perturbation network analyses provide a natural framework to measure the network tolerance facing structural or topological modifcations. Furthermore, there have been taken into account variations on data-traffic for the internet backbone that usually happen in a typical day. The novelty of the proposal is, then, twofold: proposing a weighted (traffic informed) Laplacian matrix to compute a perturbation centrality measure, and enhancing it by a time-dependent perturbation analysis to detect changes in link criticality within the network, coming from data traffic variation in a day. The results show which are the most critical links at every time of the day, being of main importance for protection, maintenance and mitigation plans for the UK internet backbone.

GEANT and AFRICACONNECT are the advanced networks for Europe and Africa respectively, offering advanced Internet backbone infrastructure to 72 countries, interconnecting the national research and education networks in, 43 countries in Europe and 29 countries in Africa. Europe and Africa are closely related, having three communication links among them that, are evolving in time, developing a better infrastructure with increasing bandwidth and backbone equipment capabilities. In this work, management emulation was developed for a network resulting from joining of the GEANT and AFRICACONNECT backbones topologies for 2020 under IPv6 communications protocols. The results show the capabilities of the GNS3 emulator when running these kinds of topologies in a limited computer resources environment and are useful for analysis by ISP companies.

The queueing delay faced by a packet is arguably the largest source of uncertainty during its journey. It therefore seems crucial that we understand how big the buffers should be in Internet routers. Our 2004 Sigcomm paper revisited the existing rule of thumb that a buffer should hold one bandwidth-delay product of packets. We claimed that for long-lived TCP flows, it could be reduced by √ N , where N is the number of active flows, potentially reducing the required buffers by well over 90% in Internet backbone routers. One might reasonably expect that such a result, which supports cheaper routers with smaller buffers, would be embraced by the ISP community. In this paper we revisit the result 15 years later, and explain where it has succeeded and failed to affect how buffers are sized.

Simulating the BGP routing system of Internet is crucial to the analysis of Internet backbone network routing behavior, locating network failure and, evaluating network performance for future Internet. However, the existing BGP routing model lacks in the coarse modeling granularity and the priori knowledge based model. The analysis of BGP routing data that reflects the routing behaviors, directly impacts the BGP routing decision and forward strategy. The efficiency of such analysis dictates the time it takes to come up with such a time-critical decision and strategy. Under the existing model, BGP routing data analysis does not scale up. In this paper, we analyze the inter-domain routing decision making process, then present a prefix level route decision prediction model. More specifically, we apply deep learning methods to build a high-precision BGP route decision process model. Our model handles as much available routing data as possible to promote the prediction accuracy. It analyzes the routing behaviors without any prior knowledge. Beyond discussing the characteristics of the model, we also evaluate the proposed model using experiments explained in detailed cases. For the research community, our method could help in detecting routing dynamics and route anomalies for routing behavior analysis.

The deployment of drone small cells has emerged as a promising solution to agile provisioning of Internet backbone access for Internet of Things devices, and many other types of users/devices. In this paper, we consider the problem of deploying a set of drone cells operating on multiple channels in a target area to provide access to the backbone/core network, which is formulated as a combinatorial network utility maximization problem. Since an offline and centralized solution to such a problem is not feasible, a low-complexity and distributed online algorithm is highly desired. Therefore, we propose a measurement-aided dynamic planning (MAD-P) algorithm, where the dispatched drones perform position and channel configurations autonomously on the fly based on the real-time measurement of network throughput to solve the problem in a distributed fashion during flight with minimal centralized control. We prove that the proposed MAD-P algorithm is asymptotically optimal, and investigate how long it takes for the convergence to stationarity under the MAD-P algorithm by giving a mixing time analysis. We also derive an upper bound of the performance gap in presence of measurement errors. Simulation results are provided to validate our analytic results and demonstrate the effectiveness of our algorithm.

Robustness is a desired feature for any backbone network. As a minimum requirement to be considered robust, a network must remain connected after any single node or link failure, so that each node is able to communicate with all other nodes. It turns out that some national optical backbones do not satisfy this condition, the Chilean Internet backbone being an example. To solve this problem, it is needed the addition of link(s) to the network, which can usually be done in different ways, leaving room to do that while minimizing other metrics of interest. This letter discusses metrics to evaluate the robustness of such networks (specifically, edge betweenness centrality, the number of link cutsets, and node Wiener impact) and proposes a variable neighborhood search heuristic to improve it by adding a few well-placed links. As a case of study, results are presented for the Chilean Internet backbone, considering three and four extra links.

This paper proposes to use multifractal analysis to detect backbone network traffic anomalies that indicate network failures or attacks. Multifractal spectrum characteristics are used as security metrics. The effectiveness of the proposed approach is confirmed by experimental results on detecting Denial of Service attacks.

The global demand for high-speed communication has increased dramatically over the past few years when data beginning to dominating of the traffic according to the Cisco Visual Networking Index (VNI). Data traffic is triple between 2014 and 2020, mainly, due to developing applications that consume bandwidth such as cloud services, HD video, high quality of real-time video transmission, virtual- augmented reality (VR-AR), online- games (video games), exchange of multimedia via smartphones and the more like. In fact, in 2020, more than a million minutes of multimedia (video) content is transiting the IP network every second according to the VNI; and the demands will exceed the capability of the current (core) internet backbone systems, in which optical communications are the main infrastructure. In this paper, the focus was on reviewing the mechanisms used for the most important and most effective techniques used to increase the capacity of optical transmission systems, namely Nonlinear Compensation (NLC) which work to reduce the nonlinear impairments that represent the main intrinsic challenges and the main capacity limitations facing the optical systems. The traditional NLC techniques were determined based on the approximate solution of the Nonlinear Schrodinger Equation (NLSE) through Digital Back Propagation (DBP), or Split- step Fourier Method (SSFM). however, their implementation demands excessive signal processing resources, and high-level accurate knowledge. A completely new approach that uses artificial intelligence (AI) algorithms to identify and solve these impairments has been studied in this paper. Traditional NLC techniques are reviewed in the first part to mitigation the nonlinearities and estimate the quality of transmission (QoT). Whereas in the second part, we review the uses of AI techniques that have been studied in applications related to monitoring performance, reduce nonlinearity, and quantify QoT. Finally, this paper presents a summary with a conclusion and outlook for development and challenges in optical fiber communication systems where AI is predictable to represent a hot major role in the near future.

The Internet is today a key constituent of daily life and the driving force in the field of communication, research, education, e-commerce, and the like. The ISP, an internet backbone, has undergone sea changes in technology, outpacing existing fault localization and fault management methodologies. Active diagnosis risk modeling is vital for ISP backbone requirements (GonzaAndlez&Helvik 2011). An automated fault localization based on risk modelling can be used to identify faults at IP (higher) layers and map them to the (lower) optical layer so as to establish the exact root cause of the failure. The construction of a complete and accurate risk model is extremely difficult taking into consideration all types of faults, so the risk model is constructed for three main scenarios - link, path, and router faults in Tier 1 ISP-backbone networks. In this approach of fault localization, shared failure components are identified by implementing a Shared Risk Greedy Algorithm (SRGA). (RamanaRaoKompellaet al. 2010).

Today’s Internet backbone network offers enhanced performance to overcome loss, delay, and unavailability. In the occurence of failure of a component, the network becomes susceptible to problems like link faults, path faults, and router failure. Link failure causes looping, packet dropping, loss of data and unreliable network communication. So an efficient fault management implementation is vital to handle multiple link failures. Fault management is vital to get the network back to normal.To properly operate and maintain a complex network, the system as a whole, and each of its essential individual components, must work in tandem. In this proposed approach On-demand Link State (OLS) routing, with shared bandwidth technology is implemented for fault management, for handling multiple link failures. It guarantees the forwarding of loop free data to destinations in case of any number of network failures. The core idea of OLS routing is a blacklist, which carries information on downgraded links identified along the path that are to be avoided. This ensures uninterrupted data packet forwarding without packets getting dropped. Also, the shortest path to the destination is located, based on the state and cost of the link (Robertson and Nelakuditi 2012).

China Education and Research Network (CERNET) is the first nationwide education and research computer network in China. It has become an important public infrastructure greatly supports China’s high education, science and technology development. CNGI-CERNET2/6IX is the largest and only academic one of the network backbones of CNGI. It is the largest pure IPv6 Internet backbone around the world. CNGI-CERNET2 has become an important infrastructure for promoting the Chinese next-generation Internet strategy. This chapter introduces the development progress of CERNET and CNGI-CERNET2 since 2015.

Large low earth orbit (LEO) satellite networks such as SpaceX's Starlink constellation promise to deliver low-latency, high-bandwidth Internet access with global coverage. As an alternative to terrestrial fiber as a global Internet backbone, they could potentially serve billions of Internet-connected devices. Currently, operators of CDNs exploit the hierarchical topology of the Internet to place points-of-presence near users, yet this approach is no longer possible when the topology changes to a single, wide-area, converged access and backhaul network. In this paper, we explore the opportunities of points-of-presence for CDNs within the satellite network itself, as it could provide better access latency for users while reducing operational costs for the satellite Internet service providers. We propose four strategies for selecting points-of-presence in satellite constellations that we evaluate through extensive simulation. In one case, we find that replicating web content within satellites can reduce bandwidth use in the constellation by 93% over an approach without replication in the network, while storing just 0.01% of all content in individual satellites., Accepted for the 21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid '21)

Machine learning has been applied to network traffic analysis for a variety of purposes, including botnet detection. To improve the computational efficiency, several architectures have been proposed to consolidate processes common across multiple applications that use the same traffic data. However, when introducing conventional architectures to real-world traffic analysis at Internet scale, the amount of input traffic data and the variety of output features to represent global access patterns become new challenges. To address the challenges, we have developed Piper, a machine learning pipeline, that consolidates diversified machine learning applications in a highly efficient manner. On top of the consolidated architecture, Piper employs two novel techniques: (1) selective sampling to reduce traffic data efficiently while maintaining prediction performance, and (2) a set of enriched features to extract temporal and spatial characteristics in global traffic. For the evaluation, we have been deploying Piper to detect botnets from internet backbone traffic over nine months. The evaluation has confirmed the effectiveness of Piper in terms of computational performance, prediction performance, and lead time to detect botnets.

In this work we model the Internet as a physical-logical interdependent network composed by the logical Internet network (Autonomous System level network), the physical Internet network (Internet backbone), and their interactions. We have tested the effect of adding physical links over the Internet’s robustness against both physical random attacks, and localized attacks. We add links using strategies that are simple enough to be used when information of the physical network is incomplete or not accurate enough to use more complex strategies. To measure the effect of adding links to the physical network our tests consider the logical network, and the set of interlinks to be constant. We tested four physical link addition strategies: random addition, distance addition, local hubs addition, and degree addition, over three different physical network models: Gabriel Graphs, n -nearest neighbors, and relative neighborhood graphs, and two extreme space shapes based on the geography of real countries: a long and narrow space with a width to length ratio of (1:25), and square space with a (1:1) width to length ratio. Our results show that there are High Damage Localized Attacks (HDLA): localized attacks that cause the failure of more than half of the logical network after removing less than 9% of the physical nodes. Some HDLA can even result in total failure. We found that HDLA are caused by the failure of “bridge nodes” in the logical network. Our results show that adding links to the physical network improves the robustness against localized attacks, and physical random attacks. Adding physical links also decreases the damage caused by HDLA, but does not fully prevent them. We found that degree and random addition strategies improve the Internet’s robustness the most, while distance addition is the most cost efficient link addition strategy in terms of robustness improvement. We also found that the high robustness and low cost efficiency of random strategy is related to the length of the links added, highlighting the importance of simple features such as the length of the links added over the robustness of physical-logical interdependent networks . Our findings suggest that given cost constraints it may be better to add more physical links using distance addition than it is to add fewer physical links using degree or random link addition strategies, and that more cost efficient versions of degree strategy could be obtained by simply limiting the length of the links added by the strategy.

The internet traffic is mainly dominated by videos, limiting video transmission is thus required to manage the traffic when network congestion occurs. Besides, service providers want to guarantee video transmission for customers’ satisfaction. However, traffic encryption is becoming increasingly common, making conventional payload-based methods unsuitable, while traffic analysis (TA) still works. In this paper, we implement and evaluate machine learning (ML)-based TA for video flow identification and propose a method named detecting ON-OFF mode (DOOM), which is a training-free, real-time and lightweight video flow identification method suitable for Gateway or Internet Backbone Provider (IBP) middle-boxes.

The automatic detection and interpretation of network attacks through machine learning is a well-known problem, for which no general solution is available. Super-vised learning and anomaly detection approaches require prior knowledge on the system under analysis, either in the form of normal operation profiles, or on the specific attacks to detect. As a consequence, both approaches have clear limitations when it comes to detecting, and in particular interpreting, previously unseen attacks and anomalies.In this paper we present WhatsThat, a novel approach to unsupervised network anomaly detection, which can both detect and interpret anomalous behaviors in a completely black-box manner, without relying on any ground-truth on the system under analysis. WhatsThat relies on hierarchical–clustering techniques to discover and characterize anomalous patterns present in nested or hierarchically structured multi–dimensional data, which is common in network traffic – e.g., due to multi–layer protocols. The solution uses unsupervised cluster validity metrics to automatically explore the data structure, and builds on automatic identification of relevant features to provide meaningful descriptions for the detected patterns. We showcase WhatsThat in the detection and interpretation of network attacks hidden in real, large-scale network traffic collected at a transit Internet backbone network. While WhatsThat is mainly tailored for unsupervised anomaly detection and interpretation, it can also be applied to the unsupervised analysis of any kind of nested or hierarchically structured multi-dimensional data, showing the potential of hierarchical clustering for general unsupervised data analysis.

Detecting and classifying anomalous behaviors in computer networks remains a formidable challenge. This work outlines a machine learning technique that uses deep neural networks to detect and classify a variety of network attacks. Our approach is based on that hypothesis that different network attacks generate a distinguishable change in entropy of certain network flow features. To generate a training and validation dataset, we inject synthetic attacks of different types and intensities into raw packet capture data collected from an internet backbone link by the MAWI group. Experimental results show that our machine learning classification model can achieve high accuracy for network attacks in which attack intensities are as low as 5% of overall traffic.

In this article we study the Internet’s robustness under physical node failures, given that the physical layer is built over spaces with geometry/shape restrictions. This is of special interest for countries prone to natural catastrophes, and long and narrow geographies such as Chile and Japan. We model the Internet as an interdependent network composed of the Internet’s physical layer (Internet backbone) and he Internet’s logical layer (Autonomous System level network) coupled. Here, the robustness is tested by measuring the amount of functional nodes on the logical network after randomly removing physical nodes. In this work, we tested six different spatially constrained network models to generate the Internet’s physical layer (Yao graphs, geometric preferential attachment, Erdős-Renyi, n-nearest neighbours, Gabriel graphs, and Modified relative neighbourhood model), and three different geometries with width to lengths ratios going from a square geometry to a Chile-like space geometry. Additionally, we study the relation between the amount of physical edges and the Internet’s robustness. Our findings suggest that both: the edge addition strategy (i.e. the physical network model used) and the amount of physical edges play an important role on the Internet’s robustness. Our results also suggest that Internet based interdependent systems whose robustness is affected by the space geometry (geometry-sensitive) can become more robust by randomly adding few edges. Furthermore, these interdependent systems can become geometry-insensitive after the edge addition, meaning that the robustness of the interdependent system is no longer affected by the space geometry.

International audience; This paper proposes a general-purpose anomaly detection mechanism for Internet backbone traffic named GAMPAL (General-purpose Anomaly detection Mechanism using Path Aggregate without Labeled data). GAMPAL does not require labeled data to achieve a general-purpose anomaly detection. For scalability to the number of entries in the BGP RIB (Routing Information Base), GAMPAL introduces path aggregates. The BGP RIB entries are classified into the path aggregates, each of which is identified with the first three AS numbers in the AS_PATH attribute. GAMPAL establishes a prediction model of traffic throughput based on past traffic throughput. It adopts the LSTM-RNN (Long Short-Term Memory Recurrent Neural Network) model focusing on periodicity in weekly scale of the Internet traffic pattern. The validity of GAMPAL is evaluated using the real traffic information and the BGP RIB exported from the WIDE backbone network (AS2500), a nation-wide backbone network for research and educational organizations in Japan. As a result, GAMPAL successfully detects traffic increases due to events and DDoS attacks targeted to a stub organization.

With the rapid increase demand for data usage, Internet has become complex and harder to analyze. Characterizing the Internet traffic might reveal information that are important for Network Operators to formulate policy decisions, develop techniques to detect network anomalies, help better provision network resources (capacity, buffers) and use workload characteristics for simulations (typical packet sizes, flow durations, common protocols). In this paper, using passive monitoring and measurements, we show collected data traffic at Internet backbone routers. First, we reveal main observations on patterns and characteristics of this dataset including packet sizes, traffic volume for inter and intra domain and protocol composition. Second, we further investigate independence structure of packet size arrivals using both visual and computational statistics. Finally, we show the temporal behavior of most active destination IP and Port addresses.

The settlement price of the backbone network affects the competition efficiency of the Internet market.Therefore, how to optimize the interconnection settlement system of Internet backbone network has become the problem that the regulatory agencies most needed to solve, and it is the core issue of the interconnection regulation for the network backbone. On the basis of summarizing the internationally accepted interconnection settlement method of the backbone network and the status quo of interconnection settlement in China’s Internet backbone network.we mainly focuses on the major problems of the interconnection settlement in China’s Internet backbone network.For example, the current interconnection settlement method restricts the improvement of interconnection quality between backbone networks.The leading backbone networks have the motive of implementing discriminatory pricing, which is not conducive to the healthy development of the Internet industry. For the existing problems, we put forward the countermeasures,which is of great significance to improve the quality of interconnection between China’s Internet backbone networks, resolve the problems of interconnection settlement and promote the healthy development of China’s Internet market.

The Internet industry in China is in a period of rapid development. The new businesses of Internet are springing up. As a result, bandwidth users want to improve the quality of the Internet. Interconnection between backbone networks is crucial to network quality. Therefore, the Chinese government is very concerned about the interconnection of backbone networks. This paper mainly analyzes the development status of the interconnection of China’s Internet backbone networks from the interconnection way between the backbone networks, we find out the main problems of the interconnection between the Internet backbone networks, such as the regional monopoly of backbone networks, the resource distribution is not balanced, and the existing interconnection system has raised the market power of the leading backbone network.We put forward corresponding countermeasures and suggestions for the existing problems of the backbone network interconnection. It is great significance to optimize of the interconnection quality of China’s Internet backbone network, improve interconnection efficiency of backbone networks and increase social welfare.

Internet of Vehicles (IoV) has gained considerable attention from industry and academia due to the development of communication technology and smart city. However, a proprietary and closed way of operating hardware in network equipment slows down the progress of new service deployment and extension in IoV. Moreover, the tightly coupled control and data planes in traditional networks significantly increase the complexity and cost of network management. By proposing a novel architecture, i.e., software-defined IoV (SDIV), we adopt a software-defined network (SDN) architecture to address these problems by leveraging its separation of the control plane from the data one and a uniform way to configure heterogeneous switches. However, IoV characteristics introduce some great challenges in rule installation due to the limited size of flow tables at OpenFlow-enabled switches that are the main SDN component. It is necessary to build compact flow tables for IoV scalability. Accordingly, we develop a novel rule installation mechanism to reduce the number of rules for real-time query services in SDIV. We separate the wired data plane from the wireless one and use multicast addresses in the latter. We introduce a destination-driven model in the wired data plane to reduce the number of rules at switches. Experiments with a real data trace show that the developed approach significantly reduces the number of rules without degrading the performance of data transmissions for real-time query services in IoV.

A future content-centric Internet would likely consist of autonomous systems (ASes) just like today’s Internet. It would thus be a network of interacting cache networks, each of them optimized for local performance. To understand the influence of interactions between autonomous cache networks, in this paper, we consider ASes that maintain peering agreements with each other for mutual benefit and engage in content-level peering to leverage each others’ cache contents. We propose a model of the interaction between the caches managed by peering ASes. We address whether stable and efficient content-level peering can be implemented without explicit coordination between the neighboring ASes. We show that content-level peering leads to stable cache configurations, both with and without coordination. However, peering Internet Service Providers (ISPs) that coordinate to avoid simultaneous updates converge to a stable configuration more efficiently. Furthermore, if the content popularity estimates are inaccurate, content-level peering is likely to lead to cost efficient cache allocations. We validate our analytical results using simulations on the measured peering topology of more than 600 ASes. QC 20190918

Differentiated Services Code Points are values that can be used on IP packets to mark traffic and allow for different Quality of Service treatment during the traversal through a network. Explicit Congestion Notifications are used to indicate that congestion on a network occurred. Both values are encoded in the previously called Type of Service field in the IP header. In this paper, we look at codepoint values for DSCP and ECN in relation to the ports used on the transport layer, which lets us infer the application that is generating the traffic. We provide new measurement data by analyzing traffic from Internet backbone links collected by CAIDA from the months March, April, and May 2018 in New York City. Our results show that DSCP codepoints are rarely used in IPv4 but even less in IPv6. Moreover, most traffic using DSCP codepoints is only using default values and not values designed for prioritization of packets. ECN-enabled traffic is scarce in IPv4, while in IPv6 it appears to be neglectable. However, we could observe differences for certain application traffic in the usage of DSCP and ECN codepoints and elaborate on their distribution.

The Border Gateway Protocol (BGP) is the primary routing protocol for the Internet backbone, yet it lacks adequate security mechanisms. While simple BGP hijack attacks only involve an adversary hijacking Internet traffic destined to a victim, more complex and challenging interception attacks require that adversary intercept a victim's traffic and forward it on to the victim. If an interception attack is launched incorrectly, the adversary's attack will disrupt its route to the victim making it impossible to forward packets. To overcome these challenges, we introduce SICO attacks (Surgical Interception using COmmunities): a novel method of launching interception attacks that leverages BGP communities to scope an adversary's attack and ensure a route to the victim. We then show how SICO attacks can be targeted to specific source IP addresses for reducing attack costs. Furthermore, we ethically perform SICO attacks on the real Internet backbone to evaluate their feasibility and effectiveness. Results suggest that SICO attacks can achieve interception even when previously proposed attacks would not be feasible and outperforms them by attracting traffic from an additional 16% of Internet hosts (worst case) and 58% of Internet hosts (best case). Finally, we analyze the Internet topology to find that at least 83% of multi-homed ASes are capable of launching these attacks.

The future Tactile Internet is expected to enable ultra-low-latency haptic communication in realtime between peers over arbitrary distances. This vision is currently highly based on the existing Internet Backbone and the upcoming 5G (and beyond) mobile communication networks. However, WiFi networks that are based on the IEEE 802.11 standard have spread widely and will be undiminishedly part of future networks. We derive a probabilistic model for estimation of latency in uncongested 802.11-based Wireless Multi-Hop Networks (WMHNs) with arbitrary topology. The model parameters are: (i) the data rate of the nodes, (ii) the packet error rate, (iii) the sending probability of the nodes, and (iv) the distribution of the queueing delay. In simulations our preliminary model can accurately predict both the minimum and maximum observed delays with multiple haptic flows present.