CN114019942A - Industrial robot system security threat evaluation method based on time-sharing frequency - Google Patents

Industrial robot system security threat evaluation method based on time-sharing frequency Download PDF

Info

Publication number
CN114019942A
CN114019942A CN202111301994.7A CN202111301994A CN114019942A CN 114019942 A CN114019942 A CN 114019942A CN 202111301994 A CN202111301994 A CN 202111301994A CN 114019942 A CN114019942 A CN 114019942A
Authority
CN
China
Prior art keywords
threat
frequency
industrial robot
robot system
time
Prior art date
Legal status (The legal status 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 status listed.)
Granted
Application number
CN202111301994.7A
Other languages
Chinese (zh)
Other versions
CN114019942B (en
Inventor
刘晓胜
姜文淇
李中伟
金显吉
苏长河
于港旭
佟为明
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Harbin Institute of Technology
Original Assignee
Harbin Institute of Technology
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 Harbin Institute of Technology filed Critical Harbin Institute of Technology
Priority to CN202111301994.7A priority Critical patent/CN114019942B/en
Publication of CN114019942A publication Critical patent/CN114019942A/en
Application granted granted Critical
Publication of CN114019942B publication Critical patent/CN114019942B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • 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
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • 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
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/24—Pc safety
    • G05B2219/24065—Real time diagnostics
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Abstract

The invention discloses an industrial robot system security threat evaluation method based on time-sharing frequency, which comprises the following steps: s1: identifying potential threats of an industrial robot system according to information such as system logs, historical behaviors and the like, classifying the threats according to threat expression forms, and determining a threat subject and a threat approach; s2: counting the occurrence frequency of the threat behaviors in a historical period aiming at one threat behavior selected by the industrial robot system to form a threat frequency sequence diagram of the industrial robot system; s3: carrying out sectional processing on the overall statistical time of the occurrence frequency of the threat behaviors, selecting a plurality of experts to evaluate the threat frequency, and sequentially evaluating the threat frequency in different time periods by each expert; s4: and calculating the trust weight of each expert threat frequency evaluation result to form a threat frequency vector and realize the system threat assignment of the industrial robot. The method can realize the security threat evaluation of the industrial robot system with longer historical security threat behavior records.

Description

Industrial robot system security threat evaluation method based on time-sharing frequency
Technical Field
The invention belongs to the field of information security risk assessment, and relates to an industrial robot system security threat evaluation method based on time-sharing frequency.
Background
With the accelerated intelligent construction of factories, industrial robots integrate advanced technologies such as machinery, electronics, sensing, control and the like, and become important automation equipment in modern manufacturing industry, and also become indispensable automation tools of flexible manufacturing systems, automation factories and computer integrated manufacturing systems. However, the informatization and networking of the manufacturing industry also inevitably increase the possibility of information security events while improving the overall work efficiency of the factory, and a threat behavior initiated by a malicious attacker by utilizing the potential vulnerability of the industrial robot system brings huge loss to the manufacturing industry. Therefore, it is necessary to construct a standard and effective industrial robot system security classification evaluation system, find the threats faced by the system in time, and adjust the security protection strategy according to the corresponding countermeasures, and the security threat evaluation is an important part of the security classification evaluation system.
For a set of industrial robot system, a large number of various equipment assets exist in the system, and a large number of hardware ports or software ports are opened on the equipment assets, so that an attack way is provided for the attack behavior of an attacker. In GB/T20984 and 2007 information safety risk assessment Specification, guidance suggestions for evaluating the safety threat are given. However, at present, many security threat assessment methods only perform rating assessment on the overall threat frequency of the system, and the influence of the change situation of the threat frequency of the system in different time periods on the security threat assessment cannot be fully considered.
Disclosure of Invention
The invention provides an industrial robot system security threat evaluation method based on time-sharing frequency, aiming at the problem that the industrial robot system security threat evaluation method cannot fully consider the influence of threat frequency change conditions on the security threat evaluation in different time periods of a system. The method can realize the security threat evaluation of the industrial robot system with longer historical security threat behavior records.
The purpose of the invention is realized by the following technical scheme:
an industrial robot system security threat evaluation method based on time sharing frequency comprises the following steps:
s1: identifying potential threats of an industrial robot system according to information such as system logs, historical behaviors and the like, classifying the threats according to threat expression forms, and determining a threat subject and a threat approach;
s2: counting the occurrence frequency of the threat behaviors in a historical period aiming at one threat behavior selected by the industrial robot system to form a threat frequency sequence diagram of the industrial robot system;
s3: carrying out sectional processing on the overall statistical time of the occurrence frequency of the threat behaviors, selecting a plurality of experts to evaluate the threat frequency, and sequentially evaluating the threat frequency in different time periods by each expert;
s4: and calculating the trust weight of each expert threat frequency evaluation result to form a threat frequency vector and realize the system threat assignment of the industrial robot.
Compared with the prior art, the invention has the following advantages:
the method analyzes the influence of the change situation of the threat frequency of the industrial robot system on the safety threat evaluation in different time periods, compares different expert evaluation results with the change situation of the threat frequency of the industrial robot system to calculate the evaluation trust weight, and retains the historical threat occurrence frequency characteristic and the recent threat occurrence frequency characteristic of the industrial robot system in the safety threat evaluation process.
Drawings
Fig. 1 is an overall flow chart of the time-sharing frequency-based industrial robot system security threat evaluation method.
Fig. 2 is an example of the frequency of occurrence of historical threats for an industrial robot system of the present invention.
Fig. 3 is a specific flowchart of the time-sharing frequency-based industrial robot system security threat assessment method according to the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings, but not limited thereto, and any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention shall be covered by the protection scope of the present invention.
The invention provides an industrial robot system security threat evaluation method based on time sharing frequency, which comprises the following steps of:
s1: identifying potential threats of an industrial robot system according to information such as system logs, historical behaviors and the like, classifying the threats according to threat expression forms, and determining a threat subject and a threat approach;
s2: counting the occurrence frequency of the threat behaviors in a historical period aiming at one threat behavior selected by the industrial robot system to form a threat frequency sequence diagram of the industrial robot system;
s3: carrying out sectional processing on the overall statistical time of the occurrence frequency of the threat behaviors, selecting a plurality of experts to evaluate the threat frequency, and sequentially evaluating the threat frequency in different time periods by each expert;
s4: and calculating the trust weight of each expert threat frequency evaluation result to form a threat frequency vector and realize the system threat assignment of the industrial robot.
As shown in fig. 3, the specific implementation steps are as follows:
s1: identifying potential threats of an industrial robot system according to information such as system logs, historical behaviors and the like, classifying the threats according to threat expression forms, and determining a threat subject and a threat approach;
s2: counting the occurrence frequency of the threat behaviors in time T aiming at one threat behavior selected by the industrial robot system to form a threat frequency sequence diagram of the industrial robot system;
s3: averagely dividing the total statistical time T of the occurrence frequency of the threat behaviors into m time intervals according to the demand of evaluation granularity;
s4: selecting n experts, and grading the threat frequency of the industrial robot system in m periods by each expert in a grading manner;
s5: synthesizing the expert threat evaluation result to form an initial time-sharing threat frequency matrix Fn×m；
S6: initial time-sharing threat frequency matrix Fn×mSumming elements of each row, calculating a total threat value of each time period in time T, and sequentially arranging m data from large to small to form a threat vector f and a standard ordinal number vector X;
s7: f is to ben×mThe elements in each row are arranged from big to small in sequence, and the element in the ith row forms a standard ordinal number vector Xi；
S8: carrying out trust weight calculation on the ith row threat evaluation result, and recording k as 0 and j as 1;
s9: judging whether j is less than or equal to 0.5m (m-1), if yes, entering S10; if not, go to S12;
s10: judging the corresponding elements of X sequence numbers a and b and XiIf the corresponding elements are identical in sequence, if so, making k equal to k +1 and j equal to j +1, and returning to S9; if not, go to S11;
s11: judgment of Xi(a) Whether or not it is equal to Xi(b) If yes, let k be k +0.5, j be j + 1; if not, let j equal j +1, return to S9;
s12: calculating trust weight w of ith expert threat evaluationiCarrying out the same trust weight calculation process on the evaluation results of other experts;
s13: normalizing the n trust weights to obtain w1 (1),w2 (1),…wn (1)；
S14: and calculating the threat evaluation result of each time period considering the trust weight, and selecting the maximum value as the final threat evaluation result.
Example (b):
for ease of understanding, fig. 2 shows an example of the historical threat of the industrial robot system of the present invention. Threat frequency is divided into T in the graph1、T2、T3、T4、T55 sections in total.
As shown in fig. 3, the method for evaluating the system threat of the industrial robot based on the time-sharing frequency according to the embodiment includes the following specific implementation steps:
s1: and identifying potential threats of the industrial robot system according to information such as system logs, historical behaviors and the like, classifying the threats according to threat expression forms, and determining a threat subject and a threat approach.
S2: and (4) counting the occurrence frequency of the threat behaviors in the time T aiming at one threat behavior selected by the industrial robot system to form a threat frequency sequence diagram of the industrial robot system.
In this embodiment, a time chart of threat frequencies of the industrial robot system obtained by performing potential threat discovery and threat occurrence frequency statistics on the industrial robot system is shown in fig. 2, and threat frequencies occurring at different times reflect changes of the threat occurrence frequency.
S3: and averagely dividing the total statistical time T of the occurrence frequency of the threat behaviors into m time intervals according to the evaluation granularity requirement.
In the present embodiment, the total statistical time T is divided into 5 segments, i.e. T in FIG. 21、T2、T3、T4、T5。
S4: and selecting n experts, and grading the threat frequency of the industrial robot system in m periods by each expert in a grading manner.
In the embodiment, 9 experts are selected to evaluate the threat frequency of the industrial robot system, and each expert evaluates T1、T2、T3、T4、T5Grading and scoring are respectively carried out on the threat frequency, and the scoring result is shown in table 1.
TABLE 1 threat value score calculation based on time-of-use threat frequency analysis
Figure BDA0003338770080000061
In this embodiment, the threat frequencies are ranked according to table 2.
TABLE 2 threat frequency quantification assessment basis
Figure BDA0003338770080000062
S5: synthesizing the expert threat evaluation result to form an initial time-sharing threat frequency matrix Fn×m。
In this embodiment, an initial time-sharing threat frequency matrix F is formedn×mComprises the following steps:
Figure BDA0003338770080000071
s6: initial time-sharing threat frequency matrix Fn×mAnd summing the elements of each row, calculating the total threat value of each time period in the time T, and sequentially arranging m data from large to small to form a threat vector f and a standard ordinal number vector X.
In the present embodiment, T1、T2、T3、T4、T5The total threat values are 10.5, 22.5, 29, 29.5, 14, 10.5, respectively, the formed threat vector is f ═ (29.5,29,22.5,10.5,10.5), and the formed standard ordinal vector is X ═ (4,3,2,1, 5).
S7: f is to ben×mThe elements in each row are arranged from big to small in sequence, and the element in the ith row forms a standard ordinal number vector Xi。
In this embodiment, taking the line 1 element as an example, the line 1 element forms a standard ordinal number vector X1＝(3,2,1,4,5)。
S8: and (4) performing trust weight calculation on the threat evaluation result of the ith row, and recording k as 0 and j as 1.
S9: judging whether j is less than or equal to 0.5m (m-1), if yes, entering S10; if not, the process proceeds to S12.
In this example, the 0.5m (m-1) value is 10, taking the element in row 1 as an example, X1It was compared 10 times with X (1).
S10: judging the corresponding elements of X sequence numbers a and b and XiIf the corresponding elements are identical in sequence, if so, making k equal to k +1 and j equal to j +1, and returning to S9; if not, the process proceeds to S11.
In this embodiment, taking row 1 element as an example, if a is 1 and b is 2, X (a) is 4, X (b) is 3, and 4 is given by the number a of X14,3 in the sequence number b of X1Is 1, b > a, b1＜a1So the sequence is different, and S11 is entered.
S11: judgment of Xi(a) Whether or not it is equal to Xi(b) If yes, let k be k +0.5, j be j + 1; if not, let j equal j +1, return to S9.
In this embodiment, taking row 1 element as an example, if a is 1 and b is 5, X (a) is 4, X (b) is 5, and 4 is in X1Number a of1Is 4, 5 at X1Number b of1Is 5, has X1(4)＝X1(5) Therefore, let k be k +0.5 and j be j +1, and return to S9 for subsequent alignment.
S12: calculating trust weight w of ith expert threat evaluationiAnd carrying out the same trust weight calculation process on the evaluation results of other experts.
In this embodiment, taking row 1 element as an example, k is 6.5, and the trust weight w of the 1 st expert threat assessment is obtained through calculationiIs 0.65, wiThe calculation method comprises the following steps:
Figure BDA0003338770080000081
and k is the accumulated trust value of the ith expert on the threat evaluation, and m is the number of time periods of the statistic time of the threat evaluation.
S13: normalizing the n trust weights to obtain w1 (1),w2 (1),…wn (1)。
In this embodimentIn, 1 st expert threatens normalized trust weight w of evaluation'1Is 0.0977, w'iThe calculation method comprises the following steps:
Figure BDA0003338770080000082
s14: and calculating the threat evaluation result of each time period considering the trust weight, and selecting the maximum value as the final threat evaluation result.
In the present embodiment, T1、T2、T3、T4、T5Since the threat assessment results are 1.113, 2.541, 3.026, 3.556 and 1.526, the final threat assessment result is 3.556.

Claims (3)

1. An industrial robot system security threat evaluation method based on time sharing frequency is characterized by comprising the following steps:
s1: identifying potential threats of an industrial robot system according to system logs and historical behavior information, classifying the threats according to threat expression forms, and determining a threat subject and a threat approach;
s2: counting the occurrence frequency of the threat behaviors in a historical period aiming at one threat behavior selected by the industrial robot system to form a threat frequency sequence diagram of the industrial robot system;
s3: carrying out sectional processing on the overall statistical time of the occurrence frequency of the threat behaviors, selecting a plurality of experts to evaluate the threat frequency, and sequentially evaluating the threat frequency in different time periods by each expert;
s4: and calculating the trust weight of each expert threat frequency evaluation result to form a threat frequency vector and realize the system threat assignment of the industrial robot.
2. The time-sharing frequency based industrial robot system security threat assessment method according to claim 1, characterized by the following specific steps:
s1: identifying potential threats of an industrial robot system according to system logs and historical behavior information, classifying the threats according to threat expression forms, and determining a threat subject and a threat approach;
s2: counting the occurrence frequency of the threat behaviors in time T aiming at one threat behavior selected by the industrial robot system to form a threat frequency sequence diagram of the industrial robot system;
s3: averagely dividing the total statistical time T of the occurrence frequency of the threat behaviors into m time intervals according to the demand of evaluation granularity;
s4: selecting n experts, and grading the threat frequency of the industrial robot system in m periods by each expert in a grading manner;
s5: synthesizing the expert threat evaluation result to form an initial time-sharing threat frequency matrix Fn×m；
S6: initial time-sharing threat frequency matrix Fn×mSumming elements of each row, calculating a total threat value of each time period in time T, and sequentially arranging m data from large to small to form a threat vector f and a standard ordinal number vector X;
s7: f is to ben×mThe elements in each row are arranged from big to small in sequence, and the element in the ith row forms a standard ordinal number vector Xi；
S8: carrying out trust weight calculation on the ith row threat evaluation result, and recording k as 0 and j as 1;
s9: judging whether j is less than or equal to 0.5m (m-1), if yes, entering S10; if not, go to S12;
s10: judging the corresponding elements of X sequence numbers a and b and XiIf the corresponding elements are identical in sequence, if so, making k equal to k +1 and j equal to j +1, and returning to S9; if not, go to S11;
s11: judgment of Xi(a) Whether or not it is equal to Xi(b) If yes, let k be k +0.5, j be j + 1; if not, let j equal j +1, return to S9;
s12: calculating trust weight w of ith expert threat evaluationiCarrying out the same trust weight calculation process on the evaluation results of other experts;
s13: normalizing the n trust weights to obtain w1 (1),w2 (1),…wn (1)；
S14: and calculating the threat evaluation result of each time period considering the trust weight, and selecting the maximum value as the final threat evaluation result.
3. The time-sharing frequency based industrial robot system security threat assessment method according to claim 1, characterized in that w isiThe calculation method comprises the following steps:
Figure FDA0003338770070000021
and k is the accumulated trust value of the ith expert on the threat evaluation, and m is the number of time periods of the statistic time of the threat evaluation.
CN202111301994.7A 2021-11-04 2021-11-04 Industrial robot system security threat evaluation method based on time-sharing frequency Active CN114019942B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111301994.7A CN114019942B (en) 2021-11-04 2021-11-04 Industrial robot system security threat evaluation method based on time-sharing frequency

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111301994.7A CN114019942B (en) 2021-11-04 2021-11-04 Industrial robot system security threat evaluation method based on time-sharing frequency

Publications (2)

Publication Number Publication Date
CN114019942A true CN114019942A (en) 2022-02-08
CN114019942B CN114019942B (en) 2023-08-29

Family

ID=80061181

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111301994.7A Active CN114019942B (en) 2021-11-04 2021-11-04 Industrial robot system security threat evaluation method based on time-sharing frequency

Country Status (1)

Country Link
CN (1) CN114019942B (en)

Citations (42)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020065695A1 (en) * 2000-10-10 2002-05-30 Francoeur Jacques R. Digital chain of trust method for electronic commerce
US20050216764A1 (en) * 2004-03-23 2005-09-29 Norton Marc A Systems and methods for dynamic threat assessment
US20070169194A1 (en) * 2004-12-29 2007-07-19 Church Christopher A Threat scoring system and method for intrusion detection security networks
US20110153383A1 (en) * 2009-12-17 2011-06-23 International Business Machines Corporation System and method for distributed elicitation and aggregation of risk information
US20120030767A1 (en) * 2010-07-29 2012-02-02 Accenture Global Services Limited. System and method for performing threat assessments using situational awareness
CN103400027A (en) * 2013-07-09 2013-11-20 贵州大学 Risk assessment algorithm for information system
US20140095887A1 (en) * 2012-09-29 2014-04-03 Alex Nayshtut Method and apparatus for maintaining trusted time at a client computing device
US20140115659A1 (en) * 2012-07-18 2014-04-24 Sequitur Labs, Inc. System and Methods for Secure Utilization of Attestation in Policy-Based Decision Making for Mobile Device Management and Security
EP2755146A1 (en) * 2013-01-10 2014-07-16 Accenture Global Services Limited Identification of significant terms in data
CN104142679A (en) * 2013-05-09 2014-11-12 洛克威尔自动控制技术股份有限公司 Risk assessment for industrial systems using big data
CN104299116A (en) * 2014-11-14 2015-01-21 国家电网公司 Quantitative evaluation method for security risk of operation of power network
JP2015041167A (en) * 2013-08-21 2015-03-02 日立オートモティブシステムズ株式会社 Evaluation apparatus for evaluating a security threat and method thereof
CN104392391A (en) * 2014-11-14 2015-03-04 国家电网公司 Power grid running safety risk quantification method
US20160034305A1 (en) * 2013-03-15 2016-02-04 Advanced Elemental Technologies, Inc. Methods and systems for purposeful computing
CN105468922A (en) * 2015-12-09 2016-04-06 中国电子科技集团公司第二十九研究所 Site electromagnetic environment signal real time analysis and threat evaluation method
US20160127408A1 (en) * 2014-10-31 2016-05-05 NxLabs Limited Determining vulnerability of a website to security threats
CN105844169A (en) * 2015-01-15 2016-08-10 中国移动通信集团安徽有限公司 Method and device for information safety metrics
TW201629830A (en) * 2015-01-30 2016-08-16 瑟古爾歐尼克斯股份有限公司 Risk scoring for threat assessment
US20160241581A1 (en) * 2014-04-03 2016-08-18 Isight Partners, Inc. System and Method of Cyber Threat Intensity Determination and Application to Cyber Threat Mitigation
KR20160141457A (en) * 2015-06-01 2016-12-09 주식회사 에스씨엘 Risk assessment system for information security management system
US20170093902A1 (en) * 2015-09-30 2017-03-30 Symantec Corporation Detection of security incidents with low confidence security events
CN106656996A (en) * 2016-11-09 2017-05-10 航天科工智慧产业发展有限公司 Information safety risk assessment method
US20170171231A1 (en) * 2015-12-11 2017-06-15 Brightpoint Security, Inc. Computer Network Threat Assessment
CN107239707A (en) * 2017-06-06 2017-10-10 国家电投集团河南电力有限公司技术信息中心 A kind of threat data processing method for information system
US20170293763A1 (en) * 2013-03-15 2017-10-12 Advanced Elemental Technologies, Inc. Methods and systems for secure and reliable identity-based computing
US20180004942A1 (en) * 2016-06-20 2018-01-04 Jask Labs Inc. Method for detecting a cyber attack
CN107689956A (en) * 2017-08-31 2018-02-13 北京奇安信科技有限公司 The intimidation estimating method and device of a kind of anomalous event
US9930059B1 (en) * 2016-03-31 2018-03-27 Lookingglass Cyber Solutions, Inc. Methods and apparatus for analyzing asynchronous cyber-threat event data using discrete time intervals
US20180191768A1 (en) * 2016-12-29 2018-07-05 Bce Inc. Cyber threat intelligence threat and vulnerability assessment of service supplier chain
US20180316713A1 (en) * 2017-04-30 2018-11-01 Splunk Inc. User interface for defining custom threat rules in a network security system
CN109063117A (en) * 2018-07-31 2018-12-21 中南大学 A kind of network security blog classification method and system based on feature extraction
US20190205542A1 (en) * 2017-12-28 2019-07-04 Fmr Llc Automated secure software development management, risk assessment, and risk remediation
CN110334737A (en) * 2019-06-04 2019-10-15 阿里巴巴集团控股有限公司 A kind of method and system of the customer risk index screening based on random forest
CN110417721A (en) * 2019-03-07 2019-11-05 腾讯科技（深圳）有限公司 Safety risk estimating method, device, equipment and computer readable storage medium
CN110458482A (en) * 2019-09-16 2019-11-15 中电科新型智慧城市研究院有限公司 A kind of Evaluation of Fire Protection ability construction method based on big data
CN110866665A (en) * 2018-08-27 2020-03-06 中国石油化工股份有限公司 Risk quantitative evaluation method and system for petrochemical production process
CN110866662A (en) * 2018-08-27 2020-03-06 中国石油化工股份有限公司 Risk quantitative management method and system for petrochemical production process
CN111212067A (en) * 2019-12-31 2020-05-29 南京联成科技发展股份有限公司 Industrial network security risk assessment system based on threat prediction
CN111865982A (en) * 2020-07-20 2020-10-30 交通运输信息安全中心有限公司 Threat assessment system and method based on situation awareness alarm
CN112003272A (en) * 2020-08-11 2020-11-27 东北电力大学 Power system frequency safety control method based on convolutional neural network
CN112734559A (en) * 2020-12-30 2021-04-30 北京知因智慧科技有限公司 Enterprise credit risk evaluation method and device and electronic equipment
CN113191634A (en) * 2021-04-29 2021-07-30 中国电力科学研究院有限公司 Method and system for evaluating health state of electricity consumption behavior of industrial user

Patent Citations (42)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020065695A1 (en) * 2000-10-10 2002-05-30 Francoeur Jacques R. Digital chain of trust method for electronic commerce
US20050216764A1 (en) * 2004-03-23 2005-09-29 Norton Marc A Systems and methods for dynamic threat assessment
US20070169194A1 (en) * 2004-12-29 2007-07-19 Church Christopher A Threat scoring system and method for intrusion detection security networks
US20110153383A1 (en) * 2009-12-17 2011-06-23 International Business Machines Corporation System and method for distributed elicitation and aggregation of risk information
US20120030767A1 (en) * 2010-07-29 2012-02-02 Accenture Global Services Limited. System and method for performing threat assessments using situational awareness
US20140115659A1 (en) * 2012-07-18 2014-04-24 Sequitur Labs, Inc. System and Methods for Secure Utilization of Attestation in Policy-Based Decision Making for Mobile Device Management and Security
US20140095887A1 (en) * 2012-09-29 2014-04-03 Alex Nayshtut Method and apparatus for maintaining trusted time at a client computing device
EP2755146A1 (en) * 2013-01-10 2014-07-16 Accenture Global Services Limited Identification of significant terms in data
US20170293763A1 (en) * 2013-03-15 2017-10-12 Advanced Elemental Technologies, Inc. Methods and systems for secure and reliable identity-based computing
US20160034305A1 (en) * 2013-03-15 2016-02-04 Advanced Elemental Technologies, Inc. Methods and systems for purposeful computing
CN104142679A (en) * 2013-05-09 2014-11-12 洛克威尔自动控制技术股份有限公司 Risk assessment for industrial systems using big data
CN103400027A (en) * 2013-07-09 2013-11-20 贵州大学 Risk assessment algorithm for information system
JP2015041167A (en) * 2013-08-21 2015-03-02 日立オートモティブシステムズ株式会社 Evaluation apparatus for evaluating a security threat and method thereof
US20160241581A1 (en) * 2014-04-03 2016-08-18 Isight Partners, Inc. System and Method of Cyber Threat Intensity Determination and Application to Cyber Threat Mitigation
US20160127408A1 (en) * 2014-10-31 2016-05-05 NxLabs Limited Determining vulnerability of a website to security threats
CN104392391A (en) * 2014-11-14 2015-03-04 国家电网公司 Power grid running safety risk quantification method
CN104299116A (en) * 2014-11-14 2015-01-21 国家电网公司 Quantitative evaluation method for security risk of operation of power network
CN105844169A (en) * 2015-01-15 2016-08-10 中国移动通信集团安徽有限公司 Method and device for information safety metrics
TW201629830A (en) * 2015-01-30 2016-08-16 瑟古爾歐尼克斯股份有限公司 Risk scoring for threat assessment
KR20160141457A (en) * 2015-06-01 2016-12-09 주식회사 에스씨엘 Risk assessment system for information security management system
US20170093902A1 (en) * 2015-09-30 2017-03-30 Symantec Corporation Detection of security incidents with low confidence security events
CN105468922A (en) * 2015-12-09 2016-04-06 中国电子科技集团公司第二十九研究所 Site electromagnetic environment signal real time analysis and threat evaluation method
US20170171231A1 (en) * 2015-12-11 2017-06-15 Brightpoint Security, Inc. Computer Network Threat Assessment
US9930059B1 (en) * 2016-03-31 2018-03-27 Lookingglass Cyber Solutions, Inc. Methods and apparatus for analyzing asynchronous cyber-threat event data using discrete time intervals
US20180004942A1 (en) * 2016-06-20 2018-01-04 Jask Labs Inc. Method for detecting a cyber attack
CN106656996A (en) * 2016-11-09 2017-05-10 航天科工智慧产业发展有限公司 Information safety risk assessment method
US20180191768A1 (en) * 2016-12-29 2018-07-05 Bce Inc. Cyber threat intelligence threat and vulnerability assessment of service supplier chain
US20180316713A1 (en) * 2017-04-30 2018-11-01 Splunk Inc. User interface for defining custom threat rules in a network security system
CN107239707A (en) * 2017-06-06 2017-10-10 国家电投集团河南电力有限公司技术信息中心 A kind of threat data processing method for information system
CN107689956A (en) * 2017-08-31 2018-02-13 北京奇安信科技有限公司 The intimidation estimating method and device of a kind of anomalous event
US20190205542A1 (en) * 2017-12-28 2019-07-04 Fmr Llc Automated secure software development management, risk assessment, and risk remediation
CN109063117A (en) * 2018-07-31 2018-12-21 中南大学 A kind of network security blog classification method and system based on feature extraction
CN110866665A (en) * 2018-08-27 2020-03-06 中国石油化工股份有限公司 Risk quantitative evaluation method and system for petrochemical production process
CN110866662A (en) * 2018-08-27 2020-03-06 中国石油化工股份有限公司 Risk quantitative management method and system for petrochemical production process
CN110417721A (en) * 2019-03-07 2019-11-05 腾讯科技（深圳）有限公司 Safety risk estimating method, device, equipment and computer readable storage medium
CN110334737A (en) * 2019-06-04 2019-10-15 阿里巴巴集团控股有限公司 A kind of method and system of the customer risk index screening based on random forest
CN110458482A (en) * 2019-09-16 2019-11-15 中电科新型智慧城市研究院有限公司 A kind of Evaluation of Fire Protection ability construction method based on big data
CN111212067A (en) * 2019-12-31 2020-05-29 南京联成科技发展股份有限公司 Industrial network security risk assessment system based on threat prediction
CN111865982A (en) * 2020-07-20 2020-10-30 交通运输信息安全中心有限公司 Threat assessment system and method based on situation awareness alarm
CN112003272A (en) * 2020-08-11 2020-11-27 东北电力大学 Power system frequency safety control method based on convolutional neural network
CN112734559A (en) * 2020-12-30 2021-04-30 北京知因智慧科技有限公司 Enterprise credit risk evaluation method and device and electronic equipment
CN113191634A (en) * 2021-04-29 2021-07-30 中国电力科学研究院有限公司 Method and system for evaluating health state of electricity consumption behavior of industrial user

Non-Patent Citations (13)

* Cited by examiner, † Cited by third party
Title
A.WALKER: "Cuckoo’s Malware Threat Scoring and Classification: Friend or Foe", 2019 IEEE 9TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), pages 0678 - 0684 *
B.OKTORIANTO: "Risk Assessment For Enterprise Application In The Insurance Sector", 2021 6TH INTERNATIONAL CONFERENCE ON NEW MEDIA STUDIES (CONMEDIA), pages 124 - 128 *
DOCZI R: "Increasing ROS 1.x communication security for medical surgery robot", IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, pages 4444 - 4449 *
YAZDI M: "Risk assessment based on novel intuitionistic fuzzy hybrid-modified TOPSIS approach", SAFETY SCIENCE, pages 438 *
刘春芬: "基于GA的SVM网络威胁频率预测方法", 中国优秀硕士学位论文全文数据库-信息科技辑, pages 139 - 159 *
刘晓胜: "基于AFDX的航空数据网络终端设备设计", 测控技术, pages 78 - 82 *
刘晓胜: "基于深度强化学习的协作非正交多址接入网络鲁棒安全传输", 中国电机工程学报, pages 4760 - 4775 *
柴继文: "基于层次分析法的信息安全风险评估要素量化方法", 重庆大学学报, pages 44 - 53 *
沈义龙: "基于威胁等级判定的欺骗干扰效果评估方法", 航天电子对抗, pages 16 - 18 *
王鑫: "分时体制多目标干扰系统干扰资源综合管控", 电光与控制, pages 20 - 25 *
肖新祥: "信息系统安全评估评测过程与方法研究", 电脑知识与技术, pages 3 - 5 *
谈博: "机组一次调频特性在线监测方法", 中国电力, pages 68 - 74 *
陈锡禹: "基于LSTM的对接机构故障预测与健康管理系统研究", 中国优秀硕士学位论文全文数据库 *

Also Published As

Publication number Publication date
CN114019942B (en) 2023-08-29

Similar Documents

Publication Publication Date Title
Gitinavard et al. Soft computing-based new interval-valued hesitant fuzzy multi-criteria group assessment method with last aggregation to industrial decision problems
Subba et al. Intrusion detection systems using linear discriminant analysis and logistic regression
KR101876371B1 (en) System and method for call routing of call center
CN106067088A (en) E-bank accesses detection method and the device of behavior
CN109784668B (en) Sample feature dimension reduction processing method for detecting abnormal behaviors of power monitoring system
CN116700172A (en) Industrial data integrated processing method and system combined with industrial Internet
CN108446841A (en) A kind of systems approach determining accident factor hierarchical structure using grey correlation
CN114019942A (en) Industrial robot system security threat evaluation method based on time-sharing frequency
CN114037145A (en) Network security situation prediction method and system
CN116633682B (en) Intelligent identification method and system based on security product risk threat
CN109962916B (en) Multi-attribute-based industrial internet security situation evaluation method
KR102578291B1 (en) Security compliance automation method
Silva et al. A re-identification risk-based anonymization framework for data analytics platforms
Chen et al. Extending sample information for small data set prediction
Suthaharan et al. An approach for automatic selection of relevance features in intrusion detection systems
CN114499957A (en) Network information security dynamic evaluation system and method thereof
CN114172699A (en) Industrial control network security event correlation analysis method
Martinez et al. Machine learning techniques for behavioral feature selection in network intrusion detection systems
Kuwano et al. The Attacker Might Also Do Next: ATT&CK Behavior Forecasting by Attacker-based Collaborative Filtering and Graph Databases
CN114117451B (en) Industrial robot system network attack path segmentation method
JP7272447B2 (en) RISK ANALYSIS RESULT DISPLAY DEVICE, METHOD AND PROGRAM
Yang et al. Monitoring and Diagnosing Process Loss Using a Weighted‐Loss Control Chart
JP2022165207A (en) Security measure planning support device and security measure planning support method
Bhadle et al. Support Vector Machine, Naïve Baye’s, and Recurrent Neural Network to Detect Data Poisoning Attacks on Dataset
Clifford et al. An empirical evaluation of the effect of adversarial labels on classifier accuracy estimation

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant