Data Mining Practical Machine Learning Tools and Techniques

Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution—the part of the distribution representing large but rare events—and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov (KS) statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data, while in others the power law is ruled out.

/pdf/power-law-distributions-in-empirical-data-4dybihkltl.pdf

Power-Law Distributions in Empirical Data

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Named Data Networking (NDN) is one of five projects funded by the U.S. National Science Foundation under its Future Internet Architecture Program. NDN has its roots in an earlier project, Content-Centric Networking (CCN), which Van Jacobson first publicly presented in 2006. The NDN project investigates Jacobson's proposed evolution from today's host-centric network architecture (IP) to a data-centric network architecture (NDN). This conceptually simple shift has far-reaching implications for how we design, develop, deploy, and use networks and applications. We describe the motivation and vision of this new architecture, and its basic components and operations. We also provide a snapshot of its current design, development status, and research challenges. More information about the project, including prototype implementations, publications, and annual reports, is available on named-data.net.

/pdf/named-data-networking-4kzv69blab.pdf

Named data networking

We design a novel, communication-efficient, failure-robust protocol for secure aggregation of high-dimensional data. Our protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner (i.e. without learning each user's individual contribution), and can be used, for example, in a federated learning setting, to aggregate user-provided model updates for a deep neural network. We prove the security of our protocol in the honest-but-curious and active adversary settings, and show that security is maintained even if an arbitrarily chosen subset of users drop out at any time. We evaluate the efficiency of our protocol and show, by complexity analysis and a concrete implementation, that its runtime and communication overhead remain low even on large data sets and client pools. For 16-bit input values, our protocol offers $1.73 x communication expansion for 210 users and 220-dimensional vectors, and 1.98 x expansion for 214 users and 224-dimensional vectors over sending data in the clear.

https://dl.acm.org/doi/pdf/10.1145/3133956.3133982

Practical Secure Aggregation for Privacy-Preserving Machine Learning

Modern Internet applications, from HD video-conferencing to health monitoring and remote control of power-plants, pose stringent demands on network latency, bandwidth and availability. An approach to support such applications and provide inter-domain guarantees, enabling new avenues for innovation, is using centralized inter-domain routing brokers. These entities centralize routing control for mission-critical traffic across domains, working in parallel to BGP. In this work, we propose using IXPs as natural points for stitching inter-domain paths under the control of inter-domain routing brokers. To evaluate the potential of this approach, we first map the global substrate of inter-IXP pathlets that IXP members could offer, based on measurements for 229 IXPs worldwide. We show that using IXPs as stitching points has two useful properties. Up to 91 % of the total IPv4 address space can be served by such inter-domain routing brokers when working in concert with just a handful of large IXPs and their associated ISP members. Second, path diversity on the inter-IXP graph increases by up to 29 times, as compared to current BGP valley-free routing. To exploit the rich path diversity, we introduce algorithms that inter-domain routing brokers can use to embed paths, subject to bandwidth and latency constraints. We show that our algorithms scale to the sizes of the measured graphs and can serve diverse simulated path request mixes. Our work highlights a novel direction for SDN innovation across domains, based on logically centralized control and programmable IXP fabrics.

Stitching Inter-Domain Paths over IXPs

In the context of the SysSec Network of Excellence call for consolidating the European (and international) systems security research community, this position paper aims at summarizing the current research activities in the Communication Systems Group (CSG) of ETH Zurich relating to network security. Our research is aligned along three projects: 1) identifying, validating, and characterizing computer infections from intrusion alerts, 2) building privacy-preserving collaborative network security mechanisms based on efficient secure multi-party computation (MPC) primitives, and 3) dissecting Internet background radiation towards live networks with one-way flow classification. In addition, we highlight important directions for future research.

Research Roadmap on Security Measurements

Unmanned Aerial Vehicles (UAVs) communications attracted significant research interest in recent years as a promising tool for future wireless communication systems (beyond fifth-generation (B5G)/sixth-generation (6G)) due to their numerous capabilities and broad applicability. In this paper we consider a UAV-enabled wireless communication system consisting of two ground base stations. Owing to distance, direct communication is not feasible for the ground stations. For this reason a UAV is used to travel periodically between the ground stations in order to enable communication. This communication model is widely used in single UAV architectures enabling throughput maximization and increased network security (especially in military applications). Since the performance of this communication system depends on several factors, in this paper we examine the Age of Information (AoI) performance of the system under different parameters, such as the number of packets exchanged between the ground base stations, the distance between the ground stations, the frequency that each ground station receives data packets and the SNR level since transmissions take place via an error-prone channel. Furthermore, we derive an analytical expression for the AoI considering the aforementioned parameters, and finally we provide simulation results that illustrate the impact of the network operating parameters on the age performance, considering the trade-off between the amount of data transferred through the network and the freshness of data exchanged between the terrestrial base stations.

Modeling the Age of Information in UAV-aided Wireless Networks

The latest advances in traffic measurement, analysis, and modeling play an important role in automatically building and maintaining a distributed intelligent monitoring layer that we describe as the awareness plane. The purpose of this article is to describe the components of this awareness plane in the areas of flexible network measurement, application and relationship discovery, and traffic classification, as well as data aggregation and semantically enriched infrastructure models. We present management services and scenarios, and list the research challenges on the path to integrating the components and making them interoperate for future autonomic service and network management approaches.

Topics in network and service management

Ultra-low delay sensitive applications can afford delay only at the level of msec An example of this application class are the Networked Music Performance (NMP) systems that describe a live music performance by geographically separate musicians over the Internet The present work proposes a novel architecture for NMP systems, where the key-innovation is the close collaboration between the network and the application Using SDN principles, the applications are enabled to adapt their internal audio signal processing, in order to cope with network delay increase Thus, affordable end-to-end delay is provided to NMP users, even under considerable network congestion

Xenofontas Dimitropoulos

Papers

Stitching Inter-Domain Paths over IXPs

Research Roadmap on Security Measurements

Modeling the Age of Information in UAV-aided Wireless Networks

Topics in network and service management

Enabling Ultra-Low Delay Teleorchestras using Software Defined Networking.