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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.

In Named Data Networking, cache pollution, cache poisoning and interest flooding are three popular types of attacks that can drastically degrade the network performance. However, previous methods for mitigating these attacks are not sufficiently effective or efficient. Also, they cannot simultaneously handle the three attacks, or the case that core routers or edge routers are compromised. To handle these problems, we propose an effective and lightweight countermeasure scheme. It consists of token-based router monitoring policy (TRM), hierarchical consensus-based trust management (HCT), and popularity-based probabilistic caching and caching replacement policy (PPC). In TRM, each edge router monitors and evaluates each data requester's probability of launching the cache pollution attack and each data provider's probability of launching the cache poisoning attack, and accordingly assigns, rewards and penalizes tokens to them to control their data request and data provision activities. Thus, the interest flooding attack can also be mitigated by limiting the consumption of tokens. In HCT, each core router manages its directly connected edge routers using TRM, and the core routers trust each other through adopting the concept of consensus in Blockchain. Thus, the edge and core routers executing monitoring and evaluation are trustable. PPC uses probabilistic caching and caching replacement based on the popularity of received content to further mitigate the attacks and reduce caching and data verification overhead. Results from simulation experiments demonstrate that our proposed scheme has better performance, in terms of interest satisfaction ratio and average end-to-end delay than current mechanisms.

Cybersecurity incidents in ISP (Internet Service Provider) infrastructure have grown significantly and it is necessary to identify vulnerabilities that need immediate protection. In this environment, the firewall can filter data packets, analyzing the headers and deciding about the routing of the packet based on the established rules. This device is very essential in an ISP network because it mitigates the vulnerabilities coming from the network, maintaining a higher degree of computer security for your internal network, thus guaranteeing the availability, integrity, and confidentiality of the information. In the case study, a Mikrotik brand infrastructure was selected with a proprietary operating system called RouterOS, which will function as a core router, where the security rules will be applied to your firewall for each type of attack that will be generated against the router. , whether they are internal or external attacks on the network, avoiding causing serious security failures such as being the victim of a DoS (Denial of Service) attack, brute force attacks, etc. As a result, a 50% decrease in CPU consumption was obtained in each attack generated, thus achieving the proper functioning of the network infrastructure and guaranteeing stability and availability of the communications network.

The health status of core router systems needs to be analyzed efficiently in order to ensure high reliability and timely error recovery. Although a large amount operational data is collected from core routers, due to high computational complexity and expensive labor cost, only a small part of this data is labeled by experts. The lack of labels is an impediment toward the adoption of supervised learning. We present an iterative self-learning procedure for assessing the health status of a core router. This procedure first computes a representative feature matrix to capture different characteristics of time-series data. Not only statistical-modeling-based features are computed from three general categories but also a recurrent neural network-based autoencoder is utilized to capture a wider range of hidden patterns. Moreover, both minimum-redundancy–maximum-relevance (mRMR) method and fully connected feedforward autoencoder are applied to further reduce dimensionality of extracted feature matrix. Hierarchical clustering is then utilized to infer labels for the unlabeled dataset. Finally, a classifier is built and iteratively updated using both labeled and unlabeled dataset. Field data collected from a set of commercial core routers are used to experimentally validate the proposed health-status analyzer. The experimental results show that the proposed feature-based self-learning health analyzer achieves higher precision and recall than the traditional supervised health analyzer as well the currently deployed rule-based health analyzer. Moreover, it achieves better performance than the three anomaly detection baseline methods under the transformed binary classification scenario.

To ensure high reliability and rapid error recovery in commercial core router systems, a health-status analyzer is essential to monitor the different features of core routers. However, traditional health analyzers need to store a large amount of historical data in order to identify health status. The storage requirement becomes prohibitively high when we attempt to carry out long-term health-status analysis for a large number of core routers. We describe the design of a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence to do health analysis. The symbolic aggregation approximation (SAX), 1d-SAX, moving-average-based trend approximation, and nonparametric symbolic approximation representation methods are implemented to encode complex time series in a hierarchical way. Hierarchical agglomerative clustering and sequitur rule discovery are implemented to learn important global and local patterns. Three classification methods including a vector-space-model-based approach are then utilized to identify the health status of core routers. Data collected from a set of commercial core router systems are used to validate the proposed health-status analyzer. The experimental results show that our symbol-based health status analyzer requires much lower storage than traditional methods, but can still maintain comparable diagnosis accuracy.

Editor’s note: Rapid error discovery is crucial for timely correction mechanisms and reliable router systems. Aiming to achieve a high degree of reliability, this article presents a machine-learning framework for analyzing router time series to evaluate the health status and detect anomalies while accounting for the important temporal characteristics of complex communication systems. —Paul Bogan, University of Southern California

Prognostic diagnosis is desirable for commercial core router systems to ensure early failure prediction and fast error recovery. The effectiveness of prognostic diagnosis depends on whether anomalies can be accurately detected before a failure occurs. However, traditional anomaly detection techniques fail to detect “outliers” when the statistical properties of the monitored data change significantly as time proceeds. We describe the design of a changepoint (CP)-based anomaly detector that first detects CPs from collected time-series data, and then utilizes these CPs to detect anomalies. Different CP detection approaches are implemented to detect various types of CPs. A clustering method is then developed to identify normal/abnormal patterns from CP windows. Data collected from a set of commercial core router systems are used to validate the proposed anomaly detector. Experimental results show that our CP-based anomaly detector achieves better performance than traditional methods in terms of two metrics, namely success ratio and nonfalse-alarm ratio.

This document updates RFC 6775 and RFC 8505 in order to enable proxy services for IPv6 Neighbor Discovery by Routing Registrars called Backbone Routers. Backbone Routers are placed along the wireless edge of a Backbone, and federate multiple wireless links to form a single Multi-Link Subnet.

In Named Data Networks, cache pollution, cache poisoning and interest flooding are three popular types of attacks that can drastically degrade the network performance. However, previous methods for mitigating these attacks are not sufficiently effective or efficient. Also, they cannot simultaneously handle the three attacks. To handle these problems, we propose an effective and lightweight countermeasure scheme. It consists of token-based router monitoring policy (TRM), hierarchical consensus-based trust management (HCT), and popularity-based probabilistic caching policy (PPC). In TRM, each edge router monitors and evaluates each data requester’s probability of launching the cache pollution attack and each data provider’s probability of launching the cache poisoning attack, and accordingly assigns, rewards and penalizes tokens to them to control their data request and data provision activities. In HCT, each core router manages its directly connected edge routers using TRM, and the core routers trust each other through adopting the concept of consensus in Blockchain. PPC uses probabilistic caching based on the popularity of received content to further mitigate the attacks and reduce caching and data verification overhead. Results from simulation experiments demonstrate that our proposed scheme has better performance, in terms of interest satisfaction ratio and average end-to-end delay than current mechanisms.

We conduct a local stability and Hopf bifurcation analysis for Compound TCP, with small Drop-tail buffers, in three topologies. The first topology consists of two sets of TCP flows having different round trip times, and feeding into a core router. The second topology corresponds to two queues in tandem, and consists of two distinct sets of TCP flows, regulated by a single edge router and feeding into a core router. The third topology comprises of two distinct sets of TCP flows, regulated by two separate edge routers, and feeding into a common core router. For each of these cases, we conduct a detailed local stability analysis and obtain conditions on the network and protocol parameters to ensure stability. If these conditions get marginally violated, our analysis shows that the underlying systems would lose local stability via a Hopf bifurcation. After exhibiting a Hopf, a key concern is to determine the asymptotic orbital stability of the bifurcating limit cycles. We present a detailed analytical framework to address the stability of the limit cycles, and the type of the Hopf bifurcation by invoking Poincare normal forms and the center manifold theory. We conduct packet-level simulations to highlight the existence and stability of the limit cycles in the queue size dynamics.