9 research outputs found
ネットワークのトラフィックおよび構成のマネジメントにおける教師なし構造パターン分析
学位の種別: 課程博士審査委員会委員 : (主査)東京大学教授 浅見 徹, 国立情報学研究所教授 安達 淳, 東京大学教授 瀬崎 薫, 東京大学教授 江崎 浩, 東京大学講師 落合 秀也University of Tokyo(東京大学
Unsupervised host behavior classification from connection patterns
International audienceA novel host behavior classification approach is proposed as a preliminary step toward traffic classification and anomaly detection in network communication. Though many attempts described in the literature were devoted to flow or application classifications, these approaches are not always adaptable to operational constraints of traffic monitoring (expected to work even without packet payload, without bidirectionality, on highspeed networks or from flow reports only...). Instead, the classification proposed here relies on the leading idea that traffic is relevantly analyzed in terms of host typical behaviors: typical connection patterns of both legitimate applications (data sharing, downloading,...) and anomalous (eventually aggressive) behaviors are obtained by profiling traffic at the host level using unsupervised statistical classification. Classification at the host level is not reducible to flow or application classification, and neither is the contrary: they are different operations which might have complementary roles in network management. The proposed host classification is based on a nine-dimensional feature space evaluating host Internet connectivity, dispersion and exchanged traffic content. A Minimum Spanning Tree (MST) clustering technique is developed that does not require any supervised learning step to produce a set of statistically established typical host behaviors. Not relying on a priori defined classes of known behaviors enables the procedure to discover new host behaviors, that potentially were never observed before. This procedure is applied to traffic collected over the entire year 2008 on a transpacific (Japan/USA) link. A cross-validation of this unsupervised classification against a classical port-based inspection and a state-of-the-art method provides assessment of the meaningfulness and the relevance of the obtained classes for host behaviors
Operations Smart Contract to Realize Decentralized System Operations Workflow for Consortium Blockchain
Enterprises have paid attention to consortium blockchains like Hyperledger
Fabric, which is one of the most promising platforms, for efficient
decentralized transactions without depending on any particular organization. A
consortium blockchain-based system will be typically built across multiple
organizations. In such blockchain-based systems, system operations across
multiple organizations in a decentralized manner are essential to maintain the
value of introducing consortium blockchains. Decentralized system operations
have recently been becoming realistic with the evolution of consortium
blockchains. For instance, the release of Hyperledger Fabric v2.x, in which
individual operational tasks for a blockchain network, such as command
execution of configuration change of channels (Fabric's sub-networks) and
upgrade of chaincodes (Fabric's smart contracts), can be partially executed in
a decentralized manner. However, the operations workflows also include the
preceding procedure of pre-sharing, coordinating, and pre-agreeing the
operational information (e.g., configuration parameters) among organizations,
after which operation executions can be conducted, and this preceding procedure
relies on costly manual tasks. To realize efficient decentralized operations
workflows for consortium blockchain-based systems in general, we propose a
decentralized inter-organizational operations method that we call Operations
Smart Contract (OpsSC), which defines an operations workflow as a smart
contract. Furthermore, we design and implement OpsSC for blockchain network
operations with Hyperledger Fabric v2.x. This paper presents OpsSC for
operating channels and chaincodes, which are essential for managing the
blockchain networks, through clarifying detailed workflows of those operations.
The implementation of OpsSC has been open-sourced and registered as one of
Hyperledger Labs projects