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WO2018136088A1 - Système d'inspection de reseau otxit utilisant une detection d'anomalie basée sur une analyse de groupe - Google Patents

Système d'inspection de reseau otxit utilisant une detection d'anomalie basée sur une analyse de groupe Download PDF

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Publication number
WO2018136088A1
WO2018136088A1 PCT/US2017/014440 US2017014440W WO2018136088A1 WO 2018136088 A1 WO2018136088 A1 WO 2018136088A1 US 2017014440 W US2017014440 W US 2017014440W WO 2018136088 A1 WO2018136088 A1 WO 2018136088A1
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WO
WIPO (PCT)
Prior art keywords
information
data
grids
cluster
command
Prior art date
Application number
PCT/US2017/014440
Other languages
English (en)
Inventor
Takashi Isobe
Original Assignee
Hitachi, Ltd.
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 Hitachi, Ltd. filed Critical Hitachi, Ltd.
Priority to PCT/US2017/014440 priority Critical patent/WO2018136088A1/fr
Publication of WO2018136088A1 publication Critical patent/WO2018136088A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • 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
    • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1425—Traffic logging, e.g. anomaly detection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/12—Detection or prevention of fraud
    • H04W12/121—Wireless intrusion detection systems [WIDS]; Wireless intrusion prevention systems [WIPS]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network

Definitions

  • the physical platform can be configured to support multi-domain IoT services such as smart grid, predictive maintenance and optimized factory using virtual machine of service platform and virtual wide area network of IT network.
  • multi-domain IoT services such as smart grid, predictive maintenance and optimized factory using virtual machine of service platform and virtual wide area network of IT network.
  • the requirement for quality and security is different between services.
  • example implementations can logically detect anomaly using actual energy consumption data from microgrid using NIS implementations.
  • Example implementations can confirm logical anomalies as actual anomalies using sensor data in combination with network attributes and OT protocol inspection from NIS implementations.
  • FIG. 3 illustrates an example of security implementations for IT systems and OT systems.
  • FIG. 7A illustrates an example OT Command / Format Dictionary Table, in accordance with an example implementation.
  • FIG. 7B illustrates an example flow diagram for the OT Protocol Inspector, in accordance with an example implementation.
  • FIG. 8A illustrates an example FFT range table, in accordance with an example implementation.
  • FIG. 9 illustrates a configuration of function blocks, in accordance with an example implementation.
  • FIG. 17 illustrates example anomaly detection over each connection shown over a map, in accordance with an example implementation.
  • OT protocol information 104-4 can include communication information (com), reply information (reply), sequence information (seq), and acknowledgement information (ack).
  • NIS 103 received packets at TCP/IP header inspector from Mirror/Tap 102.
  • FIG. 13 illustrates an example flow for Clustering 1022, in accordance with an example implementation.
  • Clustering 1022 determines the grid with largest density at 2301. In the example of FIG. 12, the largest density grid is identified at 2204.
  • the grid is set as an independent cluster. The cluster number of 1 is assigned to the grid.
  • Clustering 1022 finds the grid with next largest density at 2303. If the grid with next largest density exists at 2304, Clustering 1022 judges if the neighbor with the larger density exists at 2305. If yes, the grid is merged with neighbor as shown at 2307 and as illustrated at 2205 in FIG. 12. If no, the grid is set as an independent cluster as shown in FIG. 12 at 2206.
  • FIG. 16 illustrates example anomaly detection over each connection from using cluster analysis, in accordance with an example implementation.
  • a dashboard 1601 can be provided to compare OT parameters with a desired OT or IT parameter, as described with respect to FIGS. 15(a) to 15(d).
  • a dashboard 1602 can also be provided to indicate when clusters are generated or when new clusters are detected for selected OT or IT parameters.
  • Another dashboard 1603 can also be provided to indicate connections from both OT and IT devices, which can include information such as wide area network (WAN) internet protocol (IP) address, local area network (LAN) IP address, WAN port, LAN port, WAN round trip delay (RRT), OT sequence number, OT sensor data, OT frequency, and loss in WAN connection.
  • WAN wide area network
  • IP internet protocol
  • LAN local area network
  • RRT WAN round trip delay

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computer Security & Cryptography (AREA)
  • Computer Hardware Design (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

Des modes de réalisation illustratifs de l'invention concernent des systèmes d'inspection de réseau (NIS) configurés pour fournir une solution de sécurité couvrant des réseaux de technologie opérationnelle (OT). Des exemples de mises en oeuvre peuvent impliquer un bloc de transformée de Fourier rapide (FFT) et un générateur de données historiques pour calculer des valeurs de bande passante et de capteur cycliquement à travers chaque connexion. Des modes de réalisation donnés à titre d'exemple impliquent également un inspecteur de protocole OT et un dictionnaire de commande/format OT pour extraire des données de couche OT sur chaque connexion. Des modes de réalisation donnés à titre d'exemple impliquent en outre une analyse de grappe en utilisant un capteur ou d'autres données en plus des attributs de réseau pour chaque connexion, et fournissent une interface pour indiquer des anomalies associées à de telles données à travers chaque connexion.
PCT/US2017/014440 2017-01-20 2017-01-20 Système d'inspection de reseau otxit utilisant une detection d'anomalie basée sur une analyse de groupe WO2018136088A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/US2017/014440 WO2018136088A1 (fr) 2017-01-20 2017-01-20 Système d'inspection de reseau otxit utilisant une detection d'anomalie basée sur une analyse de groupe

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2017/014440 WO2018136088A1 (fr) 2017-01-20 2017-01-20 Système d'inspection de reseau otxit utilisant une detection d'anomalie basée sur une analyse de groupe

Publications (1)

Publication Number Publication Date
WO2018136088A1 true WO2018136088A1 (fr) 2018-07-26

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WO (1) WO2018136088A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113361869A (zh) * 2021-05-19 2021-09-07 上海天麦能源科技有限公司 一种用于燃气管网的人工智能异常检测方法及系统
CN114270281A (zh) * 2019-08-29 2022-04-01 西门子股份公司 用于对ot系统进行安全监控的方法和系统

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070289013A1 (en) * 2006-06-08 2007-12-13 Keng Leng Albert Lim Method and system for anomaly detection using a collective set of unsupervised machine-learning algorithms
US20090234899A1 (en) * 2008-03-11 2009-09-17 Paragon Science, Inc. Systems and Methods for Dynamic Anomaly Detection
US20110145262A1 (en) * 2009-12-15 2011-06-16 International Business Machines Corporation Measuring node proximity on graphs with side information
US20130245793A1 (en) * 2011-03-28 2013-09-19 International Business Machines Corporation Anomaly detection system, anomaly detection method, and program for the same
US20140074796A1 (en) * 2011-12-12 2014-03-13 International Business Machines Corporation Dynamic anomaly, association and clustering detection
US20160301709A1 (en) * 2015-04-09 2016-10-13 Accenture Global Services Limited Event correlation across heterogeneous operations

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070289013A1 (en) * 2006-06-08 2007-12-13 Keng Leng Albert Lim Method and system for anomaly detection using a collective set of unsupervised machine-learning algorithms
US20090234899A1 (en) * 2008-03-11 2009-09-17 Paragon Science, Inc. Systems and Methods for Dynamic Anomaly Detection
US20110145262A1 (en) * 2009-12-15 2011-06-16 International Business Machines Corporation Measuring node proximity on graphs with side information
US20130245793A1 (en) * 2011-03-28 2013-09-19 International Business Machines Corporation Anomaly detection system, anomaly detection method, and program for the same
US20140074796A1 (en) * 2011-12-12 2014-03-13 International Business Machines Corporation Dynamic anomaly, association and clustering detection
US20160301709A1 (en) * 2015-04-09 2016-10-13 Accenture Global Services Limited Event correlation across heterogeneous operations

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114270281A (zh) * 2019-08-29 2022-04-01 西门子股份公司 用于对ot系统进行安全监控的方法和系统
CN113361869A (zh) * 2021-05-19 2021-09-07 上海天麦能源科技有限公司 一种用于燃气管网的人工智能异常检测方法及系统
CN113361869B (zh) * 2021-05-19 2023-11-24 上海天麦能源科技有限公司 一种用于燃气管网的人工智能异常检测方法及系统

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