CN111949390A - Multi-type large-scale task automatic scheduling method and system based on affair map - Google Patents
Multi-type large-scale task automatic scheduling method and system based on affair map Download PDFInfo
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Abstract
The embodiment of the invention discloses a method and a system for automatically scheduling various large-scale tasks based on a physics map. The method comprises A1, constructing a basic affair atlas knowledge base; the A1 includes: abstracting network behaviors and asset entities in the application scene according to the application scene needing to be scheduled, so as to construct a map model; filling specific knowledge data into the map model to form a basic affair map knowledge base; a2, constructing a distributed driving framework required by the realization of scheduling and a required decision model; and A3, executing scheduling, extracting an execution result through dynamic support, and merging the execution result into the basic affair map knowledge base so as to dynamically update the operation data of the decision model. The system is used for realizing the method. The embodiment of the invention can solve the problems that the fine-grained dynamic optimization scheduling execution of large-batch and multi-type tasks is difficult to expand, maintain, control, dynamically optimize and the like under the automatic attack and defense confrontation scene in the field of network security at present.
Description
Technical Field
The invention relates to the technical field of network security, in particular to a method and a system for automatically scheduling multiple types of large-scale tasks based on a physics map.
Background
Task scheduling technologies adopted by a plurality of current internet enterprises mainly comprise: distributed timing scheduling techniques (such as Elastic-jobs, Quartz scheduling frameworks, etc.) and scheduling techniques based on regular workflows (such as MapReduce scheduling models, etc.). The two scheduling technologies can be better applied to scenes with clear flows and fixed task execution rules, such as various E-commerce transactions and the like.
However, in the field of network security, due to the numerous and complicated network assets, various attack and defense countermeasures, modes, tools, and the like are endless.
The above background disclosure is only for the purpose of assisting understanding of the inventive concept and technical solutions of the present invention, and it is not necessarily prior art to the present invention, and should not be used for evaluating the novelty and inventive step of the present invention in the case that there is no clear evidence that the above disclosure has been made before the filing date of the present application.
Disclosure of Invention
In order to guarantee the security of network assets and resist network attacks, various protective devices, technical strategies and the like are needed for linkage disposal. A large number of task scheduling and execution scenarios are involved, such as: asset scanning, vulnerability detection, firewall log analysis, and the like. By utilizing the current implementation mode of the scheduling technology, the fine control of various and large-scale tasks is difficult to realize, such as: performing optimized decision processing on the next action based on the scanning detection result, filtering invalid task scheduling and the like; meanwhile, in network security attack and defense countermeasure, a higher-level countermeasure scene mainly depends on the experience of technical staff at present, how the experience is quickly converted to form a defense strategy, and finally the defense strategy is reflected on linkage defense scheduling, so that the scheduling strategy can learn the experience of the technical staff and imitate the technical staff to schedule and solve the security problem, and the current scheduling technology cannot be well adapted to the scene.
The invention provides a method and a system for automatically scheduling multiple types of large-scale tasks based on a matter graph, which can solve the problems that the fine-grained dynamic optimization scheduling execution of large-scale and multiple types of tasks is difficult to expand, maintain, control, dynamically optimize and the like under the automatic attack and defense confrontation scene in the field of network security at present.
In a first aspect, the invention provides a method for automatically scheduling multiple types of large-scale tasks based on a affair map, which comprises the following steps:
a1, constructing a basic affair map knowledge base;
the A1 includes:
a11, abstracting network behaviors and asset entities in an application scene according to the application scene needing to be scheduled, so as to construct a map model;
a12, filling specific knowledge data into the graph model to form a basic affair graph knowledge base;
a2, constructing a distributed driving framework required by the realization of scheduling and a required decision model;
and A3, executing scheduling, extracting an execution result through dynamic support, and merging the execution result into the basic affair map knowledge base so as to dynamically update the operation data of the decision model.
In some preferred embodiments, the abstracting of the network behavior and the asset entity in the application scenario in a11 includes: and abstracting the affair entity, the entity attribute and the potential relation involved in the application scene.
In some preferred embodiments, the a3 includes:
a31, taking out scene tasks from a queue of a scene task pool, and decomposing the affair entity tasks contained in the scene tasks;
a32, analyzing and evaluating the decomposed task, judging whether the task needs to be executed, if so, generating a subtask, otherwise, terminating the task flow;
a33, distributing the subtasks to a task execution pool for execution, and reporting the task execution state dynamically and reporting the task execution result when the task is completed;
a34, completing task scheduling analysis decision data support according to the dynamic report content, and realizing dynamic feedback adjustment scheduling logic;
a35, performing knowledge mapping processing on the task execution result, filling the basic affair map knowledge base, submitting the task execution result to a task decomposition process, and repeatedly executing A32, A33, A34 and A35 to form a closed loop.
In some preferred embodiments, the analysis and evaluation decision in a32 specifically includes: empirical model based analytical decisions, common sense model based analytical decisions, and algorithmic model based analytical decisions.
In some preferred embodiments, the a1 further comprises: defining basic entities, attributes and relationships;
the empirical model-based analytical decision and the common sense model-based analytical decision evaluate task scheduling as a function of the entity, the attributes, and the relationships.
In some preferred embodiments, the particle size of the entities can be adjusted arbitrarily.
In some preferred embodiments, the specific knowledge data includes technician experience and underlying asset data.
In a second aspect, the invention provides a multi-type large-scale task automatic scheduling system based on a affair map, which is used for realizing the method.
In some preferred embodiments, a knowledge layer, a framework layer, a scheduling layer, a decision layer, and an adaptation layer are included.
In a third aspect, the present invention provides a computer readable storage medium having stored therein program instructions which, when executed by a processor of a computer, cause the processor to perform the above-described method.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
abstracting network behaviors and asset entities in an application scene to construct a map model; filling specific knowledge data into the spectrum model to form a basic affair spectrum knowledge base; scheduling is executed based on the constructed distributed driving framework and the decision model; extracting the execution result through dynamic support, merging the execution result into a basic affair atlas knowledge base, and dynamically updating the operation data of the decision model; therefore, knowledge processing of the scheduling basic driving data can be achieved, the scheduling strategy can learn the experience of technicians, and the technicians are simulated to schedule and solve the safety problem.
Drawings
FIG. 1 is a schematic diagram of an architecture of a multi-type large-scale task automation scheduling system based on a physics map according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for automatically scheduling a plurality of types of large-scale tasks based on a case map according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a closed loop of task decomposition scheduling according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of a construction process of a scheduling engine according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the embodiments of the present invention more clearly apparent, the present invention is further described in detail below with reference to fig. 1 to 4 and the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the description of the embodiments of the present invention, "a plurality" means two or more unless specifically limited otherwise.
The embodiment provides a method and a system for automatically scheduling various large-scale tasks based on a matter graph, and can realize automatic scheduling of large-scale network behaviors and action tasks in automatic attack and defense confrontation based on a software virtual robot.
Referring to fig. 1, the multiple-type large-scale task automation scheduling system based on the event graph of the present embodiment includes a plurality of logic layers, which are a knowledge layer, a framework layer, a scheduling layer, a decision layer and an adaptation layer. Each logical layer performs a different functional role definition.
And the knowledge layer realizes the construction of a network behavior affair knowledge base. Wherein the knowledge base provides basic scheduling execution reasoning data support.
The framework layer mainly comprises a distributed driving framework. The distributed driving framework provides basic communication, storage and calculation support for task scheduling, and the part can be constructed by adopting a current mainstream distributed system construction mode or an existing distributed framework system of a user, but the following design specifications are required to be met:
supporting a module plugging mechanism, enabling or uninstalling corresponding functional components as required, and facilitating integration and expansion of new components, such as: decision models, etc.;
secondly, standard external integrated interfaces are provided, including SDK (Software Development Kit), communication API (Application Programming Interface), and the like, which are convenient for linking systems, equipment and the like related to functional units, such as RESTful API Interface, cross-platform SDK and the like;
and thirdly, elastic expansion is supported, and the expansion supports a magnetic disk, bandwidth, computing power and the like.
The scheduling layer mainly comprises a task scheduling engine and can realize task scheduling. And the scheduling layer carries out analysis evaluation decision on the tasks through the analysis decision model unit.
The decision layer mainly comprises an analysis decision unit and a knowledge processing unit, wherein the analysis decision unit is an analysis decision model unit described in the scheduling layer; and the knowledge processing unit completes formatting processing of the task execution result and writes the result into a knowledge base.
The adaptation layer mainly comprises task execution interface adaptation and provides a standard data communication API interface or an SDK integration development kit. The part can be customized according to the existing systems, equipment and the like which need to be integrated, and is compatible with the conventional interface specifications, including RESTful API, gRPC, websocket and the like. The interface functions include task distribution, task control, status reporting, result reporting, and the like.
The system of the present embodiment is used to implement the method of the present embodiment. Referring to fig. 2 and 4, the method for automatically scheduling various large-scale tasks based on a concept graph of the present embodiment includes steps a1 to A3.
And A1, constructing a basic affair graph knowledge base. This step is done at the knowledge level.
In this embodiment, a case map is used as a storage structure definition description of knowledge data, and the case map is a subset of the knowledge map and is used for expressing a case logic knowledge base and describing evolution rules and modes between events, which is a technology known in the art at present. The event graph is generally used for tasks such as event prediction, common sense reasoning, consumption intention mining, dialog generation, question-answering system, aid decision making and the like.
Step a1 specifically includes step a11 and step a 12.
Step A11, abstracting the network behaviors and the asset entities in the application scene according to the application scene needing to be scheduled, thereby constructing the atlas model.
Abstracting and mapping modeling the related network behaviors and asset entities in the network security field, in particular to abstracting mapping models of affair entities, entity attributes, potential relations and the like involved in application scenes,
basic entities, attributes, relationships need to be defined. The basic entity is marked Er, and the affair entity is marked Ea. The attribute is labeled P. Empirical class relationships are labeled Rj and factual relationships are labeled Rs.
The entities are divided into two types, one type is a basic concept entity, such as IP, ports, domain names and the like; and the other is a network behavior type entity, such as RDP blasting, an HttpGet request URL, an Elasticissearch unauthorized access vulnerability detection and the like. According to the specific scene requirements, the granularity of the entity definition can be adjusted at will.
And step A12, filling concrete knowledge data into the graph model to form a basic affair graph knowledge base.
Step a12 is to generate and import knowledge data, and specifically, to fill specific knowledge data, including knowledge data such as technical staff experience (for example, domain name collection is required to be performed first for asset inventory, and then IP and port service collection are performed), basic asset data (for example, IP and domain name, etc.), etc., into the graph structure constructed in step a 11.
Step A2, constructing a distributed driving framework required by the realization of scheduling and a required decision model.
The distributed driving framework provides basic functions of basic distributed communication, storage, calculation, safety guarantee and the like. The decision model provides specific scheduling strategy generation for scheduling, comprises three categories of an experience model, a common sense model and an algorithm model, performs physical reasoning calculation on the experience knowledge, the common sense knowledge and big data algorithm analysis, and takes the calculation result as the basis of next scheduling execution decision.
And step A3, executing scheduling, extracting the execution result through dynamic support, and merging the execution result into the basic affair map knowledge base so as to dynamically update the operation data of the decision model.
Firstly, accessing a specific task execution unit, and uniformly standardizing an adaptive interface of a function execution unit, wherein the adaptive interface comprises task distribution and execution state result feedback interface specifications. The execution unit receives a task execution instruction issued by the scheduling engine and executes the task; in the executing process, the executing unit dynamically feeds back the task executing progress, the self state of the executing unit and the executing result, the executing result is extracted through dynamic support and is merged into the basic affair map knowledge base, the feedback result can be ensured to be in effect immediately, the decision model operation data is dynamically updated, and dynamic scheduling optimization is realized.
Task scheduling is divided into scene task- > entity subtask two levels. The scene task faces to the business scene, namely the initial scene of the affair scheduling execution; the entity subtask is a subtask constructed according to the event entity decomposed from the scene task.
Referring to fig. 3, a scene task is taken as an example to describe a specific processing flow of the task in the process of executing the scheduling engine, including step a31 to step a 35.
And A31, taking out the scene tasks from the queues of the scene task pool, and decomposing the affair entity tasks contained in the scene tasks.
When system resources also have idle executable task resources, taking out a scene task from a queue of a scene task pool, and decomposing the affair entity tasks contained in the scene task; such as: assuming that the scene task is the detection intranet risk point, the task of acquiring all stock host nodes of the current intranet can be resolved according to the scene task.
Step A32, analyzing and evaluating the decomposed task, judging whether the task needs to be executed, if so, generating a subtask, otherwise, terminating the task flow.
And D, analyzing and evaluating the task decomposed in the step A31 by an analyzing and deciding unit of the decision layer to judge whether the task needs to be executed, if so, generating a subtask, otherwise, terminating the task flow. The technical means of analyzing and evaluating decisions are divided into three categories: empirical model based analytical decisions, common sense model based analytical decisions, and algorithmic model based analytical decisions.
Wherein the empirical model based analysis decision evaluates the task scheduling mainly according to the (Er, Ea) -Rj- > (Ea) chain, such as: empirically, after scanning a domain name, the IP should be scanned. The trial model-based analysis decision evaluates task scheduling mainly according to the relationship of (Er) -Rs- (Er, P), such as: if the domain name bound by the IP is A, a plurality of IP analyzed by A exist, and the general knowledge of CDN characteristics is satisfied, so that a scanning port service task related to the IP should be terminated (the CDN is an operator acceleration node, and the scanning acceleration node has no practical significance). The analysis decision based on the algorithm model is mainly based on algorithms such as big data statistical analysis, machine learning, deep neural network learning and the like.
Step A33, distributing the subtasks to a task execution pool for execution, and reporting the task execution state dynamically and reporting the task execution result when the task is completed.
Distributing the subtasks obtained by decomposition in the step A32 to a task execution pool for execution, wherein the task execution pool is a task queue executed by the final specific task unit; in the process of executing the task, the task execution pool can dynamically report the task execution state and report the task execution result when the task is completed.
And step A34, according to the dynamic report content, perfecting task scheduling analysis decision data support and realizing dynamic feedback adjustment scheduling logic.
And (4) according to the dynamic report content of the task execution state in the step A33, perfecting the task scheduling analysis decision data support in the step A32 and realizing dynamic feedback adjustment scheduling logic.
And step A35, performing knowledge mapping processing on the task execution result, filling the basic affair map knowledge base, submitting the task execution result to a task decomposition flow, and repeatedly executing A32, A33, A34 and A35 to form a closed loop.
The large-scale task scheduling engine constructed based on the affair map is adopted in the embodiment, and a brand-new breakthrough mode can be brought to the problems of solidified scheduling process, coarse control granularity, insufficient scene migration capability, difficulty in flexible expansion and the like of the current task scheduling technology. In the embodiment, by using the network behavior executor and the executive action units supported by the network behavior executor in a manner of a case map, the relation between the executor and the executive action can be described from any granularity and multiple dimensions, so that the knowledge processing of the scheduling basic driving data can be realized, and the experience of technicians can be effectively converted into a knowledge data body which can be identified, calculated, reasoned and decided by a computer program; meanwhile, the same knowledge graph (the fact graph is a subset of the knowledge graph) processing standard system is adopted, so that the intelligent algorithm models such as a current big data processing analysis model, a machine learning model, a deep neural network model and the like can be seamlessly fused, a technical method specification can be provided for the automation and the intellectualization of the scheduling process, the scheduling efficiency is improved, and the next optimal scheduling execution strategy can be dynamically evaluated according to the feedback of the state result of the task execution of each step. Therefore, various types and large-scale task scheduling execution is supported natively, and especially various execution unit cluster cooperative task execution scenes are supported natively.
The method can solve the problems that under the automatic attack and defense confrontation scene in the current network security field, the fine-grained dynamic optimization scheduling execution of large-batch and various tasks is difficult to expand, maintain, control, dynamically optimize and the like.
Those skilled in the art will appreciate that all or part of the processes of the embodiments methods may be performed by a computer program, which may be stored in a computer-readable storage medium and executed to perform the processes of the embodiments methods. And the aforementioned storage medium includes: various media capable of storing program codes, such as ROM or RAM, magnetic or optical disks, etc.
The foregoing is a more detailed description of the invention in connection with specific/preferred embodiments and is not intended to limit the practice of the invention to those descriptions. It will be apparent to those skilled in the art that various substitutions and modifications can be made to the described embodiments without departing from the spirit of the invention, and these substitutions and modifications should be considered to fall within the scope of the invention.
Claims (10)
1. A multi-type large-scale task automatic scheduling method based on a physics map is characterized by comprising the following steps:
a1, constructing a basic affair map knowledge base;
the A1 includes:
a11, abstracting network behaviors and asset entities in an application scene according to the application scene needing to be scheduled, so as to construct a map model;
a12, filling specific knowledge data into the graph model to form a basic affair graph knowledge base;
a2, constructing a distributed driving framework required by the realization of scheduling and a required decision model;
and A3, executing scheduling, extracting an execution result through dynamic support, and merging the execution result into the basic affair map knowledge base so as to dynamically update the operation data of the decision model.
2. The method according to claim 1, wherein the abstracting network behaviors and asset entities in the application scenario in the A11 comprises: and abstracting the affair entity, the entity attribute and the potential relation involved in the application scene.
3. The method of claim 1, wherein said a3 comprises:
a31, taking out scene tasks from a queue of a scene task pool, and decomposing the affair entity tasks contained in the scene tasks;
a32, analyzing and evaluating the decomposed task, judging whether the task needs to be executed, if so, generating a subtask, otherwise, terminating the task flow;
a33, distributing the subtasks to a task execution pool for execution, and reporting the task execution state dynamically and reporting the task execution result when the task is completed;
a34, completing task scheduling analysis decision data support according to the dynamic report content, and realizing dynamic feedback adjustment scheduling logic;
a35, performing knowledge mapping processing on the task execution result, filling the basic affair map knowledge base, submitting the task execution result to a task decomposition process, and repeatedly executing A32, A33, A34 and A35 to form a closed loop.
4. The method according to claim 3, wherein the analysis evaluation decision in A32 specifically comprises: empirical model based analytical decisions, common sense model based analytical decisions, and algorithmic model based analytical decisions.
5. The method of claim 4,
the a1 further includes: defining basic entities, attributes and relationships;
the empirical model-based analytical decision and the common sense model-based analytical decision evaluate task scheduling as a function of the entity, the attributes, and the relationships.
6. The method of claim 5, further comprising: the granularity of the entity can be adjusted at will.
7. The method of claim 1, further comprising: the specific knowledge data includes technician experience and underlying asset data.
8. A multi-type large-scale task automatic scheduling system based on a physics map is characterized in that: for carrying out the method according to any one of claims 1 to 7.
9. The system of claim 8, wherein: the system comprises a knowledge layer, a framework layer, a scheduling layer, a decision layer and an adaptation layer.
10. A computer-readable storage medium, comprising: the computer-readable storage medium has stored therein program instructions which, when executed by a processor of a computer, cause the processor to carry out the method according to any one of claims 1 to 7.
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| CN114124859A (en) * | 2021-08-17 | 2022-03-01 | 北京邮电大学 | Intelligent customer service robot of network maintenance platform and maintenance method |
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