EA201900470A3 - TRAFFIC CLASSIFICATION SYSTEM - Google Patents

TRAFFIC CLASSIFICATION SYSTEM

Info

Publication number
EA201900470A3
EA201900470A3 EA201900470A EA201900470A EA201900470A3 EA 201900470 A3 EA201900470 A3 EA 201900470A3 EA 201900470 A EA201900470 A EA 201900470A EA 201900470 A EA201900470 A EA 201900470A EA 201900470 A3 EA201900470 A3 EA 201900470A3
Authority
EA
Eurasian Patent Office
Prior art keywords
classification
neural
classification system
irregex
informal
Prior art date
Application number
EA201900470A
Other languages
Russian (ru)
Other versions
EA201900470A2 (en
Inventor
Мария Давидовна ГОРЬКОВА
Original Assignee
Общество с ограниченной ответственностью "Алгоритм"
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Общество с ограниченной ответственностью "Алгоритм" filed Critical Общество с ограниченной ответственностью "Алгоритм"
Publication of EA201900470A2 publication Critical patent/EA201900470A2/en
Publication of EA201900470A3 publication Critical patent/EA201900470A3/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/12—Applying verification of the received information
    • H04L63/126—Applying verification of the received information the source of the received data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
    • H04L69/22—Parsing or analysis of headers
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/12—Messaging; Mailboxes; Announcements
    • H04W4/14—Short messaging services, e.g. short message services [SMS] or unstructured supplementary service data [USSD]

Abstract

Предложены способ и реализующая его система классификации трафика, предоставляющие гибкую и универсальную настройку классификации обычными администраторами сетевых узлов за счет применения авторского неформального языка регулярных выражений irregex. Выполнение системы классификации с применением неформального языка регулярных выражений irregex позволяет снять ограничения на обслуживание системы узконаправленными высококлассными специалистами и предоставить возможность ее эксплуатации обычным телекоммуникационным специалистам. Включение в процесс классификации пользовательских вероятностных коэффициентов и выполнение модуля neural net системы классификации с возможностью корректировки результатов работы нейросети (собственной архитектуры или сторонних инструментов) позволяет администратору системы быстро реагировать на изменение схемы и семантики сообщения и оперативно "подправлять" результаты нейронной классификации. Причем способ классификации как в последовательно-параллельном режиме, так и в параллельном обеспечивает существенный прирост точности классификации за счет синергии алгоритмов regex и neural net. Что особенно важно, способ за счет архитектуры системы классификации трафика обеспечивает опосредованное взаимовлияние пользовательских настроек, притом что администратор системы может настраивать их независимо.A method and a traffic classification system that implements it are proposed, providing flexible and universal classification configuration by ordinary network node administrators through the use of the author's informal regular expression language irregex. The implementation of the classification system using the informal regular expression language irregex allows you to remove restrictions on the maintenance of the system by narrowly focused highly qualified specialists and provide the opportunity for its operation to ordinary telecommunications specialists. The inclusion of custom probability coefficients in the classification process and the implementation of the neural net module of the classification system with the ability to adjust the results of the neural network (own architecture or third-party tools) allows the system administrator to quickly respond to changes in the scheme and semantics of the message and quickly "correct" the results of neural classification. Moreover, the classification method, both in serial-parallel mode and in parallel, provides a significant increase in classification accuracy due to the synergy of the regex and neural net algorithms. Most importantly, the method, due to the architecture of the traffic classification system, provides an indirect mutual influence of user settings, while the system administrator can configure them independently.

EA201900470A 2018-10-05 2019-10-07 TRAFFIC CLASSIFICATION SYSTEM EA201900470A3 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
RU2018135235A RU2697648C2 (en) 2018-10-05 2018-10-05 Traffic classification system

Publications (2)

Publication Number Publication Date
EA201900470A2 EA201900470A2 (en) 2020-06-30
EA201900470A3 true EA201900470A3 (en) 2020-10-30

Family

ID=64317094

Family Applications (1)

Application Number Title Priority Date Filing Date
EA201900470A EA201900470A3 (en) 2018-10-05 2019-10-07 TRAFFIC CLASSIFICATION SYSTEM

Country Status (3)

Country Link
EA (1) EA201900470A3 (en)
RU (1) RU2697648C2 (en)
WO (1) WO2020071962A1 (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110489102B (en) * 2019-07-29 2021-06-18 东北大学 Method for automatically generating Python code from natural language
CN110781950B (en) * 2019-10-23 2023-06-30 新华三信息安全技术有限公司 Message processing method and device
CN113872918A (en) * 2020-06-30 2021-12-31 苏州三六零智能安全科技有限公司 Network traffic classification method, equipment, storage medium and device
WO2023033684A1 (en) * 2021-09-04 2023-03-09 Акционерное Общество "Квантум А Рус" Method for the mobile messaging of mobile subscribers

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7096498B2 (en) * 2002-03-08 2006-08-22 Cipher Trust, Inc. Systems and methods for message threat management
US20050060295A1 (en) * 2003-09-12 2005-03-17 Sensory Networks, Inc. Statistical classification of high-speed network data through content inspection
US7694287B2 (en) * 2005-06-29 2010-04-06 Visa U.S.A. Schema-based dynamic parse/build engine for parsing multi-format messages
US8364766B2 (en) * 2008-12-04 2013-01-29 Yahoo! Inc. Spam filtering based on statistics and token frequency modeling
US10848448B2 (en) * 2016-09-21 2020-11-24 King Fahd University Of Petroleum And Minerals Spam filtering in multimodal mobile communication

Also Published As

Publication number Publication date
WO2020071962A1 (en) 2020-04-09
RU2018135235A (en) 2018-11-19
EA201900470A2 (en) 2020-06-30
RU2018135235A3 (en) 2019-05-22
RU2697648C2 (en) 2019-08-15

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