CN115021970B - Data self-adaptive safety monitoring method suitable for big data center of Internet of things - Google Patents
Data self-adaptive safety monitoring method suitable for big data center of Internet of things Download PDFInfo
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- 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
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- G—PHYSICS
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- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y30/00—IoT infrastructure
- G16Y30/10—Security thereof
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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- H—ELECTRICITY
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- 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
- H04L63/1441—Countermeasures against malicious traffic
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Abstract
The application discloses a data self-adaptive safety monitoring method suitable for a big data center of the Internet of things, and belongs to the field of big data of the Internet of things; the method comprises the following steps: establishing a corresponding state monitoring model for each type of terminal equipment in the Internet of things system to form a terminal model database; the data acquisition module monitors real-time network behavior data of the terminal of the Internet of things; performing terminal model database model matching on the Internet of things terminal acquired by the data acquisition module; if the matching is successful, judging the safety of the terminal equipment, and carrying out safe operation and monitoring according to the parameters of the model database; if the matching fails, the control system enters an adaptive safety monitoring mode. The method can bring high-efficiency and safe operation modes to the Internet of things system.
Description
Technical Field
The application relates to the field of management of the Internet of things, in particular to a data self-adaptive safety monitoring method suitable for a big data center of the Internet of things.
Background
The Internet of things is two network forms which extend and extend the user end of the Internet of things to any article and between articles to exchange information and communicate. The definition is as follows: through information sensing equipment such as radio frequency identification, an infrared sensor, a global positioning system, a laser scanner and the like, any article is connected with the Internet according to a stipulated protocol to exchange information and communicate, so as to realize a network concept of intelligent identification, positioning, tracking, monitoring and management. The internet of things organically combines the information flow and the 'logistics' through the internet. The Internet of things is widely applied to intelligent application and/or network business of various industries. The internet of things utilizes a public network (commonly referred to as the internet) to realize secure communication, automatic control and wide contact of people and/or objects and/or machines. The internet of things can be widely applied to all fields of safely and reliably developing business (application, industry, travel, building, agriculture, business, government affairs and the like) by utilizing the internet. In the current application field of the internet of things, various aspects of intelligent home, intelligent campus, intelligent traffic, intelligent city and the like exist.
There are a large number of limited capability or resource limited terminals of the internet of things in the internet of things system, and these terminals generally have the characteristics of small flow, low speed, low power consumption, low cost and the like due to the small memory or limited power supply, and typical limited capability terminals of the internet of things have narrowband internet of things (Narrow band internet of things, NB-IoT) terminals, such as devices of water meters, electricity meters, gas meters and the like, into which NB-IoT chips are inserted. Because the capability-limited internet of things terminal cannot support performance consumption caused by an authentication scheme (such as DTLS) with a complex flow, an internet of things platform or an internet of things server generally adopts an application layer protocol to authenticate the terminal in a registration flow, such as a Lightweight M2M protocol (LWM 2M).
In the communication of the internet of things, the current general method is that an interworking gateway receives a registration request from an internet of things terminal sent by a core network gateway, wherein the registration request comprises an address and a device identifier of the internet of things terminal, and the interworking gateway and the core network gateway are in the same IP sub-network or private network; and the intercommunication gateway authenticates the terminal of the Internet of things, and after the authentication is passed, the intercommunication gateway sends the registration request to the server of the Internet of things. Specifically, the interworking gateway authenticates the internet of things equipment according to the address and the equipment identifier of the internet of things terminal in the registration request, and if the address and the equipment identifier in the registration request are the same as the address and the equipment identifier locally recorded by the interworking gateway, the authentication is passed. For example, the interworking gateway may query the local record for the device identifier corresponding to the address according to the address in the registration request, and if the device identifier of the local record of the interworking gateway is the same as the device identifier in the registration request, the authentication is passed; or the intercommunication gateway can summarize and inquire the corresponding address in the local record according to the equipment identifier in the registration request, and if the address of the local record of the intercommunication gateway is the same as the address in the registration request, the authentication is passed.
However, with the development of networks, network security has also been greatly regulated. Various network attacks are also endless. As a new and developed network, the internet of things is also subject to various network attacks. Such as illegal and counterfeit internet of things devices, the border closure and management of all individual devices is a major challenge for internet of things security. The popularity and rapid increase in the amount of manufacturing of internet of things devices has raised problems with home networks. Users are unauthorised to install illegal and counterfeit internet of things devices in the protected network. These devices either replace the original devices or are integrated into the network to capture sensitive information and data, thereby breaking the network boundaries. These devices may be configured to act as malicious access points, cameras, thermostats, and other types of devices to steal communication data without the user's knowledge.
Meanwhile, terminal equipment of the Internet of things is generally low-energy-consumption equipment, and energy consumption control of a system plays a key role in normal operation of the Internet of things. In the current control system of the internet of things, the energy consumption control of a terminal is an important research direction. Of course, the current energy crisis is not at all, but the Internet of things combines a plurality of intelligent objects together to achieve coordinated control so as to achieve the effect of energy conservation.
When the two problems of safety and energy consumption are combined together, the following problems exist in the current situation of the Internet of things. When a user connects with a new terminal device in the internet of things system, judging the safety of the device is important to the whole internet of things system. Meanwhile, the operation of the new terminal needs to be controlled by the Internet of things system, especially in the aspect of energy consumption. In addition, the matching problem between the new terminal and the whole Internet of things system is also particularly remarkable.
Disclosure of Invention
In view of the above analysis, the present application aims to provide a data adaptive security monitoring method suitable for a big data center of the internet of things, which solves the problems of security and operation mode control of the system due to access of a new terminal in the internet of things system.
The aim of the application is mainly realized by the following technical scheme:
the application discloses a data self-adaptive safety monitoring method suitable for a big data center of the Internet of things, which comprises the following steps:
establishing a corresponding state monitoring model for each type of terminal equipment in the Internet of things system to form a terminal model database;
the data acquisition module monitors real-time network behavior data of the terminal of the Internet of things;
performing terminal model database model matching on the Internet of things terminal acquired by the data acquisition module;
if the matching is successful, judging the safety of the terminal equipment, and carrying out safe operation and monitoring according to the parameters of the model database;
if the matching fails, the control system enters an adaptive safety monitoring mode.
Wherein the adaptive security monitoring mode comprises the steps of:
the system continuously monitors whether the operation of the terminal is in a stable state or not and whether the operation of the terminal is in a safe state or not;
if the terminal is running stably, the system sends a message to the terminal user to inquire whether the equipment is trusted or not;
if the reply of the user is obtained, the system monitors 24-hour data of the terminal and compares the data with the data of the terminal model database to find an approximate terminal model database;
selecting data of confidence intervals meeting requirements, and combining an approximate terminal model database to calculate a temporary terminal model database of the equipment;
continuing to optimize the temporary terminal model database of the equipment, selecting proper long-time operation data, and generating a final terminal model database of the equipment;
and operating the equipment according to the terminal model database, and judging whether the terminal equipment is in a normal safe operation mode.
Further, determining whether the terminal is in a secure state includes determining whether the IP address of the terminal is in a terminal model database, and if not, determining that it is a new address, the system makes a special flag for the terminal. The special mark can give an alarm to an administrator of the big data center of the Internet of things at a certain interval time, so that the administrator is prompted to track and confirm the safety of the terminal equipment.
Further, the terminal equipment is properly selected for one year, the proper operation mode of the terminal equipment is calculated by monitoring temporary operation data of one year, and a final terminal model database of the equipment is determined. And the administrator sets the confidence interval according to the type of the terminal equipment, eliminates the non-ideal operation data and forms the temporary operation data.
Further, the confidence interval varies according to the needs of a particular user.
Further, the big data center control module of the Internet of things obtains a final terminal model database of the equipment by considering the running environment, energy consumption and user requirements of the terminal equipment.
Furthermore, the machine learning module compares the temporary terminal model database in the operation data optimization of the terminal through learning, and the control module eliminates unreasonable operation parameters through the system optimal control principle to calculate the optimal control mode of the terminal.
The application has the following beneficial effects:
the whole operation of the Internet of things system is monitored by establishing a terminal model database, and meanwhile, the control module utilizes machine learning and an artificial intelligence technology to irregularly optimize the terminal model database, so that each terminal in the system is ensured to be in an optimal operation mode state at all times.
The safety of the new terminal is judged, so that the running state safety of the whole Internet of things system is ensured, and the data are reasonable.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the application, like reference numerals being used to refer to like parts throughout the several views.
Fig. 1 is a flowchart of a data self-adaptive security monitoring method suitable for a big data center of the internet of things in an embodiment of the application.
Fig. 2 is a flow chart in the adaptive security monitoring mode.
Detailed Description
Preferred embodiments of the present application are described in detail below with reference to the attached drawing figures, which form a part of the present application and are used in conjunction with embodiments of the present application to illustrate the principles of the present application.
The following embodiment describes a specific implementation control method suitable for data self-adaptive security monitoring of a big data center of the internet of things by using one of the applications of the internet of things, namely an intelligent home. The applicant needs to say that the application of the smart home cannot form any limiting effect on the present application, because the method is applicable to any application category of the internet of things, and the smart home only plays a role of an explanation case.
The application discloses a data self-adaptive safety monitoring method suitable for a big data center of the Internet of things, which is shown in fig. 1 and comprises the following steps:
step 101, establishing a corresponding state monitoring model for each type of terminal equipment in the internet of things system to form a terminal model database. The intelligent household Internet of things big data center is provided with an optimal operation parameter database of various terminals and an operation database of various brands of various household appliances. For example, the household air conditioning data contains optimal operation parameters of various brands and models such as Dajin, gri, mei and the like.
And 102, monitoring real-time network behavior data of the terminal of the Internet of things by a data acquisition module. The data acquisition module of the intelligent household Internet of things big data center monitors implementation data of various terminals connected to the Internet of things system so as to judge the types of the terminals and the operation parameters of the terminals.
And step 103, performing terminal model database model matching on the Internet of things terminal acquired by the data acquisition module. And (3) analyzing the operation data acquired in the step (102) in real time and matching the operation data with a terminal model database.
Step 1031, if the matching is successful, judging the safety of the terminal equipment, and performing safe operation and monitoring according to the parameters of the model database. And (3) analyzing the operation data acquired in the step (102) in real time, successfully matching with a terminal model database, and judging the type of the operation terminal. And controlling the operation mode and the operation parameters of the terminal according to the parameters of the model database so as to enable the terminal to operate in a safe and efficient mode.
In step 1032, if the match fails, the control system enters an adaptive security monitoring mode. And (3) analyzing the operation data acquired in the step (102) in real time, if the operation data is not successfully matched with the terminal model database, the operation terminal is a terminal device which is never seen by the intelligent home Internet of things system, and possibly the device is an attack object from an unsafe mode, and the Internet of things system needs to start an adaptive safety monitoring mode.
Wherein the adaptive security monitoring mode comprises the steps of:
in step 201, the system continuously monitors whether the operation of the terminal is in a stable state or in a safe state. Whether the internet of things is in a safe state or not comprises judging whether the IP address of the terminal is in a terminal model database or not, if not, determining that the IP address is a new address, and marking the terminal by the system. The special mark can give an alarm to an administrator of the big data center of the Internet of things at a certain interval time, so that the administrator is prompted to track and confirm the safety of the terminal equipment. The monitoring module of the big data center of the Internet of things can also judge whether the running mode of the whole system is in a stable state or not after the unknown terminal enters the system, and whether the terminal equipment is suddenly in an abnormal state mode or not so as to judge whether the unknown terminal attacks the normally running terminal equipment or not.
If the terminal is operating stably, the system sends a message to the end user asking if the device is trusted, step 202. If the access of the unknown terminal is performed, the trusted terminal of the big data center of the Internet of things is in a stable running state, and the system defaults that the position terminal is safe equipment with high probability. The system will send a message to each end user asking if the device is trusted or not and if it is the end device that the end user is newly connected to the system.
Step 203, if a reply of the user is obtained, the system monitors 24 hours data of the terminal and compares the data with the data of the terminal model database to find an approximate terminal model database. When the end user replies, the system confirms that the terminal is a trusted terminal. The system monitors the operation parameters of the terminal for 24 hours, compares the operation parameters with the data of the terminal model database, and searches for an approximate terminal model database according to the operation performance and the operation characteristics of the terminal model database.
And 204, selecting data of a confidence interval meeting the requirement, and combining the data with the approximate terminal model database to calculate a temporary terminal model database of the equipment. The manager processes the operation data, selects proper confidence differences according to experience, selects operation parameters, eliminates unreliable data and assembles a temporary terminal database. The confidence interval varies according to the needs of a particular user.
And 205, continuing to optimize the temporary terminal model database of the equipment, selecting the proper long-time operation data, and generating a final terminal model database of the equipment. Typically, the data center will periodically optimize the temporary terminal model database, e.g., in a smart home, typically taking into account different time period factors of the terminal, different users, and different device types. The system can also store a temporary terminal model database for at least one year, and is convenient for the dynamic comparison of the data by the management center so as to determine the optimal control operation mode. And (3) by monitoring temporary operation data for one year, calculating a proper operation mode of the terminal equipment, and determining a final terminal model database of the equipment. The control module eliminates unreasonable operation parameters through a system optimal control principle, and calculates an optimal control mode of the terminal.
And 206, operating the equipment according to the terminal model database, and judging whether the terminal equipment is in a normal safe operation mode.
The present application is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present application are intended to be included in the scope of the present application.
Claims (8)
1. A data self-adaptive safety monitoring method suitable for a big data center of the Internet of things comprises the following steps:
1) Establishing a corresponding state monitoring model for each type of terminal equipment in the Internet of things system to form a terminal model database;
2) The data acquisition module monitors real-time network behavior data of the terminal of the Internet of things;
3) Performing terminal model database model matching on the Internet of things terminal acquired by the data acquisition module;
4) If the matching is successful, judging the safety of the terminal equipment, and carrying out safe operation and monitoring according to the parameters of the model database;
5) If the matching fails, the control system enters an adaptive safety monitoring mode; the method is characterized in that: the adaptive security monitoring mode comprises the following steps:
a) The system continuously monitors whether the operation of the terminal is in a stable state or not and whether the operation of the terminal is in a safe state or not;
b) If the terminal is running stably, the system sends a message to the terminal user to inquire whether the equipment is trusted or not;
c) If the reply of the user is obtained, the system monitors 24-hour data of the terminal and compares the data with the data of the terminal model database to find an approximate terminal model database;
d) Selecting data of confidence intervals meeting requirements, and combining an approximate terminal model database to calculate a temporary terminal model database of the equipment;
e) Continuing to optimize the temporary terminal model database of the equipment, selecting proper long-time operation data, and generating a final terminal model database of the equipment;
f) And operating the equipment according to the terminal model database, and judging whether the terminal equipment is in a normal safe operation mode.
2. The adaptive security monitoring method of claim 1, wherein the step of a) of determining whether the IP address of the terminal is in a secure state comprises determining whether the IP address is in a terminal model database, and if not, determining that the IP address is a new address, the system marking the terminal specifically.
3. The adaptive security monitoring method of claim 2, wherein the special mark gives an alarm to an administrator of the big data center of the internet of things at a certain interval, so as to prompt the administrator to track and confirm the security of the terminal device.
4. The adaptive security monitoring method of claim 1, wherein the suitable period of time is one year, and the terminal model database of the terminal device is determined by monitoring temporary operation data for one year, and calculating a suitable operation mode of the terminal device.
5. The adaptive security monitoring method of claim 4, wherein an administrator sets the confidence interval according to the type of the terminal device, and eliminates the undesirable operation data to form the temporary operation data.
6. The adaptive security monitoring method of claim 5, wherein the confidence interval varies according to the needs of a particular user.
7. The method for self-adaptive security monitoring according to claim 1, wherein the big data center control module of the internet of things obtains the final terminal model database of the equipment in consideration of the operation environment of the terminal equipment, energy consumption and user requirements.
8. The adaptive security monitoring method of claim 1, wherein the machine learning module optimizes the temporary terminal model database in step e) by comparing the operation data of the terminal through learning, and the control module eliminates unreasonable operation parameters through a system optimal control principle to calculate an optimal control mode of the terminal.
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