CN108920356A - A kind of sensing node method for detecting abnormality of task based access control execution track model - Google Patents
A kind of sensing node method for detecting abnormality of task based access control execution track model Download PDFInfo
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- CN108920356A CN108920356A CN201810548226.3A CN201810548226A CN108920356A CN 108920356 A CN108920356 A CN 108920356A CN 201810548226 A CN201810548226 A CN 201810548226A CN 108920356 A CN108920356 A CN 108920356A
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- 238000000034 method Methods 0.000 title claims abstract description 24
- 230000005856 abnormality Effects 0.000 title claims abstract description 11
- 230000002159 abnormal effect Effects 0.000 claims abstract description 61
- 238000012549 training Methods 0.000 claims description 5
- 238000007689 inspection Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 abstract description 24
- 238000001514 detection method Methods 0.000 abstract description 11
- 238000012706 support-vector machine Methods 0.000 abstract description 4
- 238000010835 comparative analysis Methods 0.000 abstract description 2
- 238000011156 evaluation Methods 0.000 abstract description 2
- 230000000875 corresponding effect Effects 0.000 description 16
- 230000002596 correlated effect Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
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- 230000007547 defect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 206010000117 Abnormal behaviour Diseases 0.000 description 1
- 206010033799 Paralysis Diseases 0.000 description 1
- 238000005314 correlation function Methods 0.000 description 1
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- 230000002265 prevention Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3684—Test management for test design, e.g. generating new test cases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
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Abstract
The present invention provides a kind of sensing node method for detecting abnormality of task based access control execution track model, analysis is carried out to task execution track and establishes task execution model, and the abnormal section of task execution locus model progress executed extremely is detected to realize using a category support vector machines disaggregated model and quickly positions exception section and abnormal generation moment；Significance test further is carried out to all tasks executed in application program using hypothesis testing after determining abnormal ranges, comparative analysis, which is normally executed, determines possible abnormal execution task with the abnormal difference for executing task execution situation；Model accuracy rate with higher and low false detection rate can be determined by Performance Evaluation of the testing result to OCSVM disaggregated model.
Description
Technical field
The present invention relates to wireless sensor network technology field, in particular to a kind of sense of task based access control execution track model
Know node anomaly detection method.
Background technique
In recent years, wireless sensor network using more and more extensive so that its network structure becomes increasingly complex, function is got over
Come that more perfect, the degree of automation is also higher and higher, promotes the fast development of intelligent transportation system.However, due to traffic system
Itself it is one to contain much information, the strong complex gigantic system of dynamic, so that wireless sensor network is in the application by various nothings
The factor that method avoids influences and application environment is complicated severe, causes wireless sensor network to be likely to occur environment in the process of running dry
It disturbs, hardware failure and the problems such as software failure.These problems not only will affect network performance, while also resulting in node and occurring not
Predictable exception causes heavy losses even network paralysis.Meanwhile the complete and network knot of wireless sensor network functionally
Structure becomes increasingly complex, and to wireless sensor network fault detection, more stringent requirements are proposed.
Sensor network fault can substantially be divided into three classes：Application failure, network failure and node failure [4].Using event
Hindering possible manifestation mode has perception data missing, detection delay are longer and life cycle is too short etc.；And link failure, data packet
Loss, network congestion, route loop and network fracture then belong to network failure scope；Node failure include restart, damage, hardware
Failure, without response, energy pre-mature exhaustion, sensor reading failure and faulty software behavior.Accurately and effectively method for diagnosing faults
Diagnosis, prevention and reparation can be made to various abnormalities in time, improve sensor network reliability of operation, safety and
Validity ensures complete function and the long-term and effective operation of sensor network.
Exception definition is a kind of mode not being inconsistent with desired normal behaviour.Therefore, a kind of most direct for abnormal behaviour
Effective detection method is to define the range or correlated characteristic of an expression normal behaviour, then observes in data and be not belonging to normal model
The data enclosed or do not have correlated characteristic under normal circumstances can be determined as abnormal data.However, wireless sensor network
The where the shoe pinches of the realization of abnormality detection are not to judge whether data belong to normal range (NR) and whether data have normal number
According to correlated characteristic, it is mostly important and what is be difficult to realize is determining normal range (NR) and obtains correlated characteristic under normal circumstances.It is different
Normal detection field scholar needs to overcome lot of challenges in terms of determining normal behaviour mode, mainly includes：
(1) be difficult to define one include all possible normal behaviour range.
(2) some exceptions from malicious attacker to become its identification by the forms of expression of normal mode
It is difficult.
(3) development of the advanced technologies such as information technology and radio technology is so that wireless sensor is also gradually applied to newly
Field, and existing exception definition is not appropriate for the abnormality detection of new application field.
(4) it cannot go to define the exception in different application fields with a determining exception definition.
(5) it is difficult to obtain accurate, representative training dataset and validation data set.
Summary of the invention
Aiming at the problems existing in the prior art, the object of the present invention is to provide a kind of task based access control execution track moulds
The sensing node method for detecting abnormality of type, this method can accurately obtain the abnormal task in the source code in sensing node.
To achieve the goals above, the present invention adopts the following technical scheme that：
A kind of sensing node method for detecting abnormality of task based access control execution track model, this method is for obtaining sensing node
In source code in abnormal task, include the following steps：
Step 1, the experience table of running in abnormal source code and normal source code for task is extracted respectively, point
It is not denoted as abnormal task execution record and normal tasks execute record；Experience table includes timestamp information and task I D；
Step 2：It is based respectively on abnormal task execution record and normal tasks executes record, utilize and execute the building of task track
Method constructs abnormal task execution track model and normal tasks execution track model；
Step 3：OCSVM classification mould is obtained by the method training of feature extraction using normal tasks execution track model
Type；
Step 4, by abnormal task execution track mode input into OCSVM disaggregated model, an abnormal section is exported, it should
Abnormal section includes multiple sections；
Step 5, for each task, execution frequency of the task in each section in abnormal section is extracted, is formed
The corresponding abnormal task of the task executes frequency vector；For each task, the task is extracted in normal tasks execution track mould
The execution frequency in each section in type forms the corresponding normal tasks of the task and executes frequency vector；
Step 6, for each task, frequency vector sum normal tasks is executed to the corresponding abnormal task of the task and execute frequency
Number vector carries out double sample T inspection, and exporting the task is normal tasks or abnormal task, and all abnormal tasks can be obtained.
Specifically, the execution task track construction method in the step 2, includes the following steps：
Using timestamp information, record is executed to abnormal task according to the time window being sized and carries out interval division, is obtained
To multiple sections；For each section, the execution frequency of each task in the section, the execution frequency shape of all tasks are counted
At the corresponding section frequency vector in the section；The corresponding section in all sections executes frequency vector and forms abnormal task execution track
Model；
Using timestamp information, record is executed to normal tasks according to the time window being sized and carries out interval division, is obtained
To multiple sections；For each section, the execution frequency of each task in the section, the execution frequency shape of all tasks are counted
At the corresponding section frequency vector in the section；The corresponding section in all sections executes frequency vector and forms normal tasks execution track
Model.
Compared with prior art, the present invention has the following technical effects：
The present invention carries out analysis to task execution track and establishes task execution model, and uses a category support vector machines
(OCSVM) disaggregated model realizes quickly positioning exception to the abnormal section detection of the task execution locus model progress executed extremely
Section and abnormal generation moment；Further utilize hypothesis testing to all executed in application program after determining abnormal ranges
Business carries out significance test, and comparative analysis, which is normally executed, determines that possible exception is held with the abnormal difference for executing task execution situation
Row task；By Performance Evaluation of the testing result to OCSVM disaggregated model can determine model accuracy rate with higher and
Low false detection rate.
Explanation and illustration in further detail is made to the solution of the present invention with reference to the accompanying drawings and detailed description.
Detailed description of the invention
Fig. 1 is the network topological diagram in embodiment.
Specific embodiment
The present invention provides a kind of sensing node method for detecting abnormality of task based access control execution track model, and this method is for obtaining
The abnormal task in the source code in sensing node is taken, source code is run in sensing node, is included the following steps：
Step 1, the experience table of running in abnormal source code and normal source code for task is extracted respectively, point
It is not denoted as abnormal task execution record and normal tasks execute record；Experience table includes timestamp information and task I D.
Wherein, the experience table of running in abnormal source code and normal source code for task, the side of use are extracted respectively
Method is as follows：
Code pitching pile, compilation run are carried out to source code, obtain journal file, task execution note is extracted from journal file
Record, which includes timestamp information and task I D.Experience table and task correlation function call record group
At journal file.
Step 2：It is based respectively on abnormal task execution record and normal tasks executes record, utilize and execute the building of task track
Method constructs abnormal task execution track model and normal tasks execution track model.
Step 3：OCSVM classification mould is obtained by the method training of feature extraction using normal tasks execution track model
Type.
Step 4, by abnormal task execution track mode input into OCSVM disaggregated model, an abnormal section is exported, it should
Abnormal section includes multiple sections.
Step 5, for each task, execution frequency of the task in each section in abnormal section is extracted, is formed
The corresponding abnormal task of the task executes frequency vector；For each task, the task is extracted in normal tasks execution track mould
The execution frequency in each section in type forms the corresponding normal tasks of the task and executes frequency vector.
Step 6, for each task, frequency vector sum normal tasks is executed to the corresponding abnormal task of the task and execute frequency
Number vector carries out double sample T inspection, and exporting the task is normal tasks or abnormal task, and all abnormal tasks can be obtained.
Specifically, in another embodiment, the execution task track construction method in step 2, includes the following steps：
Using timestamp information, record is executed to abnormal task according to the time window being sized and carries out interval division, is obtained
To multiple sections；For each section, the execution frequency of each task in the section, the execution frequency shape of all tasks are counted
At the corresponding section frequency vector in the section；The corresponding section in all sections executes frequency vector and forms abnormal task execution track
Model；
Using timestamp information, record is executed to normal tasks according to the time window being sized and carries out interval division, is obtained
To multiple sections；For each section, the execution frequency of each task in the section, the execution frequency shape of all tasks are counted
At the corresponding section frequency vector in the section；The corresponding section in all sections executes frequency vector and forms normal tasks execution track
Model.
Embodiment
The TelosB node that experiment runs TestDissemination (TinyOS application program) by 4 forms, setting section
The communication radius of point is 50 meters.
One for this example has node network topology of the version of system defect in normal execute as shown in Figure 1.
Wherein No. 1 node is distribution node, periodically sends data to its child node, and No. 2 nodes directly communicates with No. 1 node, and No. 3 with
No. 4 nodes can only realize the communication with No. 1 node by No. 2 nodes.
In order to test process performing of No. 2 nodes after losing distribution node, two test cases, two tests are devised
The simulation time of use-case is all 1 hour.Test case 1 is that No. 1 is deleted when program runs 45min after repairing fault code
Node terminates after operation 1 hour, herein it is assumed that program can guarantee to operate normally after having repaired defect code.Test
Use-case 2 then uses fault code, and No. 1 node is equally deleted in 45min, terminates after program is run 1 hour.Experiment uses
Information records when COOJA simulator is to the operations of No. 2 nodes.
The experience table in test case 1 and test case 2 in No. 2 nodes is extracted, constructs task execution rail respectively
Mark model.One category support vector machines model of model training is estimated using the task execution of test case 1, by test case 2
Task execution locus model is input in a category support vector machines model, the available section executed extremely.Such as 1 institute of table
Show, for according to different time windows, to task execution locus model carry out abnormal section detection as a result, binding time window
The Base Serial Number in size and abnormal section, can calculate the period occurred extremely, can verify a classification branch by calculating
Holding vector machine model can detecte out the period occurred extremely.
The abnormal section testing result of 1 node of table 2
When by the test case 1 of No. 2 nodes and the corresponding task execution sequence of test case 2 according to the starting occurred extremely
It carves and carries out cutting, the test case 1 and the corresponding task execution sequence of test case 2 after only exception occurs are according to specified time
Window divides, and constructs task execution locus model respectively.The execution frequency to each task when normal and abnormal is examined using T
T inspection is carried out, refusal null hypothesis then shows that exception has occurred in the task execution.It is by the abnormal task that T is examined
0x000a(VirtualizeTimerC__0__updateFromTimer)。
By the way that after deleting distribution node, the task execution of test case 1 and test case 2 is sent out known to verifying
Variation.Test case 1 only has task 0x0009 (AlarmToTimerC__0__ after deleting No. 1 node in No. 2 nodes
Fired) executed with task 0x000a, and test case 2 is deleting 10 minutes after No. 1 node in, execute 0x0009 and
0x000a, the execution frequency of task 0x000a sharply increases later.Illustrate that exception has occurred in the execution of task 0x000a really.
Claims (2)
1. a kind of sensing node method for detecting abnormality of task based access control execution track model, this method is for obtaining in sensing node
Source code in abnormal task, which is characterized in that include the following steps：
Step 1, the experience table for extracting running in abnormal source code and normal source code for task respectively, remembers respectively
Record is executed for abnormal task and normal tasks execute record；Experience table includes timestamp information and task ID；
Step 2：It is based respectively on abnormal task execution record and normal tasks executes record, utilize and execute task track building side
Method constructs abnormal task execution track model and normal tasks execution track model；
Step 3：OCSVM disaggregated model is obtained by the method training of feature extraction using normal tasks execution track model；
Step 4, by abnormal task execution track mode input into OCSVM disaggregated model, an abnormal section, the exception are exported
Section includes multiple sections；
Step 5, for each task, execution frequency of the task in each section in abnormal section is extracted, forms this
Be engaged in corresponding abnormal task executes frequency vector；For each task, the task is extracted in normal tasks execution track model
Each section in execution frequency, form the corresponding normal tasks of the task and execute frequency vector；
Step 6, for each task, to the corresponding abnormal task of the task execute frequency vector sum normal tasks execute frequency to
Amount carries out double sample T inspection, and exporting the task is normal tasks or abnormal task, and all abnormal tasks can be obtained.
2. the sensing node method for detecting abnormality of task based access control execution track model as described in claim 1, feature exist
In execution task track construction method in the step 2 includes the following steps：
Using timestamp information, record is executed to abnormal task according to the time window being sized and carries out interval division, is obtained more
A section；For each section, the execution frequency of each task in the section is counted, the execution frequency of all tasks is formed should
The corresponding section frequency vector in section；The corresponding section in all sections executes frequency vector and forms abnormal task execution track mould
Type；
Using timestamp information, record is executed to normal tasks according to the time window being sized and carries out interval division, is obtained more
A section；For each section, the execution frequency of each task in the section is counted, the execution frequency of all tasks is formed should
The corresponding section frequency vector in section；The corresponding section in all sections executes frequency vector and forms normal tasks execution track mould
Type.
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