Enhancing distributed traffic monitoring via traffic digest splitting.

Lam, Chi Ho.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 113-117).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.viChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Organization --- p.4Chapter 2 --- Related Works and Background --- p.7Chapter 2.1 --- Related Works --- p.7Chapter 2.2 --- Background --- p.9Chapter 2.2.1 --- Datalite --- p.9Chapter 2.2.2 --- Proportional Union Method --- p.14Chapter 2.2.3 --- Quasi-Likelihood Approach --- p.18Chapter 3 --- Estimation Error of Existing TD-based TMA schemes --- p.24Chapter 3.1 --- Error Accumulation and Amplification of Existing Schemes --- p.25Chapter 3.1.1 --- Pu --- p.25Chapter 3.1.2 --- Qmle --- p.26Chapter 3.1.3 --- Datalite --- p.26Chapter 3.2 --- Estimation Error of 3-sets intersection cases --- p.28Chapter 3.2.1 --- Pu --- p.28Chapter 3.2.2 --- Datalite --- p.30Chapter 4 --- Error Reduction Via Traffic Digest Splitting --- p.36Chapter 4.1 --- Motivation --- p.36Chapter 4.2 --- Objective Functions for Optimal TD-splitting --- p.39Chapter 4.3 --- Problem Formulation of Threshold-based Splitting --- p.41Chapter 4.3.1 --- Minimizing Maximum Estimation Error --- p.42Chapter 4.3.2 --- Minimizing R.M.S. Estimation Error --- p.46Chapter 4.4 --- Analysis of Estimation Error Reduction Via Single-Level TD-splitting --- p.48Chapter 4.4.1 --- Noise-to-signal Ratio Reduction --- p.49Chapter 4.4.2 --- Estimation Error Reduction --- p.52Chapter 4.5 --- Recursive Splitting --- p.56Chapter 4.5.1 --- Minimizing Maximum Estimation Error --- p.57Chapter 4.5.2 --- Minimizing R.M.S. Estimation Error --- p.59Chapter 5 --- Realization of TD-splitting for Network Traffic Measurement --- p.61Chapter 5.1 --- Tracking Sub-TD Membership --- p.64Chapter 5.1.1 --- Controlling the Noise due to Non-Existent Flows on a Target Link --- p.64Chapter 5.1.2 --- Sub-TD Membership Tracking for Single-level TD-splitting --- p.65Chapter 5.1.3 --- Sub-TD Membership Tracking under Recursive Splitting --- p.66Chapter 5.2 --- Overall Operations to support TD-splitting for Network-wide Traffic Measurements --- p.67Chapter 5.2.1 --- Computation Time for TD-splitting --- p.69Chapter 6 --- Performance Evaluation --- p.72Chapter 6.1 --- Applying TD-splitting on Generic Network Topology --- p.72Chapter 6.1.1 --- Simulation Settings --- p.73Chapter 6.1.2 --- Validity of the Proposed Surrogate Objective Functions --- p.75Chapter 6.1.3 --- Performance of Single-level TD-splitting --- p.77Chapter 6.1.4 --- Performance of Recursive TD-splitting --- p.88Chapter 6.1.5 --- Heterogeneous NSR Loading --- p.95Chapter 6.2 --- Internet Trace Evaluation --- p.99Chapter 6.2.1 --- Simulation Results --- p.100Chapter 7 --- Conclusion --- p.105Chapter A --- Extension of QMLE for Cardinality Estimation of 3-sets Intersection --- p.107Bibliography --- p.11

A Survey on Data Plane Programming with P4: Fundamentals, Advances, and Applied Research

With traditional networking, users can configure control plane protocols to match the specific network configuration, but without the ability to fundamentally change the underlying algorithms. With SDN, the users may provide their own control plane, that can control network devices through their data plane APIs. Programmable data planes allow users to define their own data plane algorithms for network devices including appropriate data plane APIs which may be leveraged by user-defined SDN control. Thus, programmable data planes and SDN offer great flexibility for network customization, be it for specialized, commercial appliances, e.g., in 5G or data center networks, or for rapid prototyping in industrial and academic research. Programming protocol-independent packet processors (P4) has emerged as the currently most widespread abstraction, programming language, and concept for data plane programming. It is developed and standardized by an open community and it is supported by various software and hardware platforms. In this paper, we survey the literature from 2015 to 2020 on data plane programming with P4. Our survey covers 497 references of which 367 are scientific publications. We organize our work into two parts. In the first part, we give an overview of data plane programming models, the programming language, architectures, compilers, targets, and data plane APIs. We also consider research efforts to advance P4 technology. In the second part, we analyze a large body of literature considering P4-based applied research. We categorize 241 research papers into different application domains, summarize their contributions, and extract prototypes, target platforms, and source code availability.Comment: Submitted to IEEE Communications Surveys and Tutorials (COMS) on 2021-01-2

Security for networked smart healthcare systems: A systematic review

Background and Objectives Smart healthcare systems use technologies such as wearable devices, Internet of Medical Things and mobile internet technologies to dynamically access health information, connect patients to health professionals and health institutions, and to actively manage and respond intelligently to the medical ecosystem's needs. However, smart healthcare systems are affected by many challenges in their implementation and maintenance. Key among these are ensuring the security and privacy of patient health information. To address this challenge, several mitigation measures have been proposed and some have been implemented. Techniques that have been used include data encryption and biometric access. In addition, blockchain is an emerging security technology that is expected to address the security issues due to its distributed and decentralized architecture which is similar to that of smart healthcare systems. This study reviewed articles that identified security requirements and risks, proposed potential solutions, and explained the effectiveness of these solutions in addressing security problems in smart healthcare systems. Methods This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines and was framed using the Problem, Intervention, Comparator, and Outcome (PICO) approach to investigate and analyse the concepts of interest. However, the comparator is not applicable because this review focuses on the security measures available and in this case no comparable solutions were considered since the concept of smart healthcare systems is an emerging one and there are therefore, no existing security solutions that have been used before. The search strategy involved the identification of studies from several databases including the Cumulative Index of Nursing and Allied Health Literature (CINAL), Scopus, PubMed, Web of Science, Medline, Excerpta Medical database (EMBASE), Ebscohost and the Cochrane Library for articles that focused on the security for smart healthcare systems. The selection process involved removing duplicate studies, and excluding studies after reading the titles, abstracts, and full texts. Studies whose records could not be retrieved using a predefined selection criterion for inclusion and exclusion were excluded. The remaining articles were then screened for eligibility. A data extraction form was used to capture details of the screened studies after reading the full text. Of the searched databases, only three yielded results when the search strategy was applied, i.e., Scopus, Web of science and Medline, giving a total of 1742 articles. 436 duplicate studies were removed. Of the remaining articles, 801 were excluded after reading the title, after which 342 after were excluded after reading the abstract, leaving 163, of which 4 studies could not be retrieved. 159 articles were therefore screened for eligibility after reading the full text. Of these, 14 studies were included for detailed review using the formulated research questions and the PICO framework. Each of the 14 included articles presented a description of a smart healthcare system and identified the security requirements, risks and solutions to mitigate the risks. Each article also summarized the effectiveness of the proposed security solution. Results The key security requirements reported were data confidentiality, integrity and availability of data within the system, with authorisation and authentication used to support these key security requirements. The identified security risks include loss of data confidentiality due to eavesdropping in wireless communication mediums, authentication vulnerabilities in user devices and storage servers, data fabrication and message modification attacks during transmission as well as while the data is at rest in databases and other storage devices. The proposed mitigation measures included the use of biometric accessing devices; data encryption for protecting the confidentiality and integrity of data; blockchain technology to address confidentiality, integrity, and availability of data; network slicing techniques to provide isolation of patient health data in 5G mobile systems; and multi-factor authentication when accessing IoT devices, servers, and other components of the smart healthcare systems. The effectiveness of the proposed solutions was demonstrated through their ability to provide a high level of data security in smart healthcare systems. For example, proposed encryption algorithms demonstrated better energy efficiency, and improved operational speed; reduced computational overhead, better scalability, efficiency in data processing, and better ease of deployment. Conclusion This systematic review has shown that the use of blockchain technology, biometrics (fingerprints), data encryption techniques, multifactor authentication and network slicing in the case of 5G smart healthcare systems has the potential to alleviate possible security risks in smart healthcare systems. The benefits of these solutions include a high level of security and privacy for Electronic Health Records (EHRs) systems; improved speed of data transaction without the need for a decentralized third party, enabled by the use of blockchain. However, the proposed solutions do not address data protection in cases where an intruder has already accessed the system. This may be potential avenues for further research and inquiry