Measuring Infringement of Intellectual Property Rights

© Crown Copyright 2014. You may re-use this information (excluding logos) free of charge in any format or medium, under the terms of the Open Government Licence. To view this licence, visit http://www.nationalarchives.gov. uk/doc/open-government-licence/ Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concernedThe review is wide-ranging in scope and overall our findings evidence a lack of appreciation among those producing research for the high-level principles of measurement and assessment of scale. To date, the approaches adopted by industry seem more designed for internal consumption and are usually contingent on particular technologies and/or sector perspectives. Typically, there is a lack of transparency in the methodologies and data used to form the basis of claims, making much of this an unreliable basis for policy formulation. The research approaches we found are characterised by a number of features that can be summarised as a preference for reactive approaches that look to establish snapshots of an important issue at the time of investigation. Most studies are ad hoc in nature and on the whole we found a lack of sustained longitudinal approaches that would develop the appreciation of change. Typically the studies are designed to address specific hypotheses that might serve to support the position of the particular commissioning body. To help bring some structure to this area, we propose a framework for the assessment of the volume of infringement in each different area. The underlying aim is to draw out a common approach wherever possible in each area, rather than being drawn initially to the differences in each field. We advocate on-going survey tracking of the attitudes, perceptions and, where practical, behaviours of both perpetrators and claimants in IP infringement. Clearly, the nature of perpetrators, claimants and enforcement differs within each IPR but in our view the assessment for each IPR should include all of these elements. It is important to clarify that the key element of the survey structure is the adoption of a survey sampling methodology and smaller volumes of representative participation. Once selection is given the appropriate priority, a traditional offline survey will have a part to play, but as the opportunity arises, new technological methodologies, particularly for the voluntary monitoring of online behaviour, can add additional detail to the overall assessment of the scale of activity. This framework can be applied within each of the IP right sectors: copyright, trademarks,patents, and design rights. It may well be that the costs involved with this common approach could be mitigated by a syndicated approach to the survey elements. Indeed, a syndicated approach has a number of advantages in addition to cost. It could be designed to reduce any tendency either to hide inappropriate/illegal activity or alternatively exaggerate its volume to fit with the theme of the survey. It also has the scope to allow for monthly assessments of attitudes rather than being vulnerable to unmeasured seasonal impacts

A Review on Features’ Robustness in High Diversity Mobile Traffic Classifications

Mobile traffics are becoming more dominant due to growing usage of mobile devices and proliferation of IoT. The influx of mobile traffics introduce some new challenges in traffic classifications; namely the diversity complexity and behavioral dynamism complexity. Existing traffic classifications methods are designed for classifying standard protocols and user applications with more deterministic behaviors in small diversity. Currently, flow statistics, payload signature and heuristic traffic attributes are some of the most effective features used to discriminate traffic classes. In this paper, we investigate the correlations of these features to the less-deterministic user application traffic classes based on corresponding classification accuracy. Then, we evaluate the impact of large-scale classification on feature's robustness based on sign of diminishing accuracy. Our experimental results consolidate the needs for unsupervised feature learning to address the dynamism of mobile application behavioral traits for accurate classification on rapidly growing mobile traffics

Hierarchical Learning for Fine Grained Internet Traffic Classification

Traffic classification is still today a challenging prob- lem given the ever evolving nature of the Internet in which new protocols and applications arise at a constant pace. In the past, so called behavioral approaches have been successfully proposed as valid alternatives to traditional DPI based tools to properly classify traffic into few and coarse classes. In this paper we push forward the adoption of behavioral classifiers by engineering a Hierarchical classifier that allows proper classification of traffic into more than twenty fine grained classes. Thorough engineering has been followed which considers both proper feature selection and testing seven different classification algorithms. Results obtained over actual and large data sets show that the proposed Hierarchical classifier outperforms off-the-shelf non hierarchical classification algorithms by exhibiting average accuracy higher than 90%, with precision and recall that are higher than 95% for most popular classes of traffi

Performance Evaluation of a Field Programmable Gate Array-Based System for Detecting and Tracking Peer-to-Peer Protocols on a Gigabit Ethernet Network

Recent years have seen a massive increase in illegal, suspicious, and malicious traffic traversing government and military computer networks. Some examples include illegal file distribution and disclosure of sensitive information using the BitTorrent file sharing protocol, criminals and terrorists using Voice over Internet Protocol (VoIP) technologies to communicate, and foreign entities exfiltrating sensitive data from government, military, and Department of Defense contractor networks. As a result of these growing threats, the TRacking and Analysis for Peer-to-Peer (TRAPP) system was developed in 2008 to detect BitTorrent and VoIP traffic of interest. The TRAPP system, designed on a Xilinx Virtex-II Pro Field Programmable Gate Array (FPGA) proved valuable and effective in detecting traffic of interest on a 100 Mbps network. Using concepts and technology developed for the TRAPP system, the TRAPP-2 system is developed on a Xilinx ML510 FPGA. The goals of this research are to evaluate the performance of the TRAPP-2 system as a solution to detect and track malicious packets traversing a gigabit Ethernet network. The TRAPP-2 system detects a BitTorrent, Session Initiation Protocol (SIP), or Domain Name System (DNS) packet, extracts the payload, compares the data against a hash list, and if the packet is suspicious, logs the entire packet for future analysis. Results show that the TRAPP-2 system captures 95.56% of BitTorrent, 20.78% of SIP INVITE, 37.11% of SIP BYE, and 91.89% of DNS packets of interest while under a 93.7% network utilization (937 Mbps). For another experiment, the contraband hash list size is increased from 1,000 to 131,072,000 unique items. The experiment reveals that each doubling of the hash list size results in a mean increase of approximately 16 central processing unit cycles. These results demonstrate the TRAPP-2 system’s ability to detect traffic of interest under a saturated network utilization while maintaining large contraband hash lists

An FPGA-Based System for Tracking Digital Information Transmitted via Peer-to-Peer Protocols

This thesis addresses the problem of identifying and tracking digital information that is shared using peer-to-peer file transfer and Voice over IP (VoIP) protocols. The goal of the research is to develop a system for detecting and tracking the illicit dissemination of sensitive government information using file sharing applications within a target network, and tracking terrorist cells or criminal organizations that are covertly communicating using VoIP applications. A digital forensic tool is developed using an FPGA-based embedded software application. The tool is designed to process file transfers using the BitTorrent peer-to-peer protocol and VoIP phone calls made using the Session Initiation Protocol (SIP). The tool searches a network for selected peer-to-peer control messages using payload analysis and compares the unique identifier of the file being shared or phone number being used against a list of known contraband files or phone numbers. If the identifier is found on the list, the control packet is added to a log file for later forensic analysis. Results show that the FPGA tool processes peer-to-peer packets of interest 92% faster than a software-only configuration and is 99.0% accurate at capturing and processing BitTorrent Handshake messages under a network traffic load of at least 89.6 Mbps. When SIP is added to the system, the probability of intercept for BitTorrent Handshake messages remains at 99.0% and the probability of intercept for SIP control packets is 97.6% under a network traffic load of at least 89.6 Mbps, demonstrating that the tool can be expanded to process additional peer-to-peer protocols with minimal impact on overall performance

The State of Network Neutrality Regulation

The Network Neutrality (NN) debate refers to the battle over the design of a regulatory framework for preserving the Internet as a public network and open innovation platform. Fueled by concerns that broadband access service providers might abuse network management to discriminate against third party providers (e.g., content or application providers), policymakers have struggled with designing rules that would protect the Internet from unreasonable network management practices. In this article, we provide an overview of the history of the debate in the U.S. and the EU and highlight the challenges that will confront network engineers designing and operating networks as the debate continues to evolve.BMBF, 16DII111, Verbundprojekt: Weizenbaum-Institut für die vernetzte Gesellschaft - Das Deutsche Internet-Institut; Teilvorhaben: Wissenschaftszentrum Berlin für Sozialforschung (WZB)EC/H2020/679158/EU/Resolving the Tussle in the Internet: Mapping, Architecture, and Policy Making/ResolutioNe