CN114610581B - Data processing system for acquiring application software - Google Patents
Data processing system for acquiring application software Download PDFInfo
- Publication number
- CN114610581B CN114610581B CN202210265107.3A CN202210265107A CN114610581B CN 114610581 B CN114610581 B CN 114610581B CN 202210265107 A CN202210265107 A CN 202210265107A CN 114610581 B CN114610581 B CN 114610581B
- Authority
- CN
- China
- Prior art keywords
- application software
- target
- time
- time window
- type
- 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.)
- Active
Links
- 238000012545 processing Methods 0.000 title claims abstract description 22
- 230000000694 effects Effects 0.000 claims abstract description 44
- 230000002159 abnormal effect Effects 0.000 claims abstract description 21
- 238000004590 computer program Methods 0.000 claims abstract description 9
- 230000004913 activation Effects 0.000 claims abstract description 5
- 238000001994 activation Methods 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 12
- 230000006870 function Effects 0.000 claims description 2
- 230000008859 change Effects 0.000 abstract description 18
- 238000012544 monitoring process Methods 0.000 abstract description 9
- 230000000875 corresponding effect Effects 0.000 description 54
- 238000009434 installation Methods 0.000 description 6
- 238000004883 computer application Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000007123 defense Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003213 activating effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3051—Monitoring arrangements for monitoring the configuration of the computing system or of the computing system component, e.g. monitoring the presence of processing resources, peripherals, I/O links, software programs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- General Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Computer Hardware Design (AREA)
- Computer Security & Cryptography (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention relates to a data processing system for acquiring application software, which comprises the following components: the system comprises a database, a processor and a memory storing a computer program, wherein the database comprises application software IDs and the number of activations corresponding to the application software IDs, and when the computer program is executed by the processor, the following steps are realized: according to the ID of the target application software, a first activity number list and a plurality of first activity number lists are obtained to determine the first type of activity and the second type of activity so as to construct an intermediate activity list; traversing the intermediate liveness list, and determining the first type time window as a target time window when all the second type liveness is not more than the first type liveness; when the second-class liveness is larger than the first-class liveness, the second-class time window is determined to be the target time window, so that the accuracy of determining abnormal conditions of the application software and the accuracy of deducing the state of the application software to change trend can be improved, and the third-party monitoring and the safe use of users are facilitated.
Description
Technical Field
The invention relates to the technical field of application software processing, in particular to a data processing system for acquiring application software.
Background
With the continuous development of information technology, the popularization of national economy and national defense and military information technology application, the computer application system is in the basic supporting position of economy and national defense, and the safety problem of the computer application system becomes the key for determining the economy and the national defense safety, and once the computer application system is destroyed, the computer application system can cause catastrophic results. As an important ring of computer application system safety protection, the traditional computer protection software based on malicious code behavior feature matching can not timely detect malicious codes through hidden technologies such as polymorphism, shell adding, deformation, anti-tracking and the like.
Currently, the method for identifying application software includes: 1) The application software is identified manually, so that the application software with malicious codes is determined, but the efficiency is too low, and a large amount of identification can not be carried out on the application software; 2) Based on the same or similar APK as the application software with the malicious code, determining the application software with the malicious code, analyzing each APK, and ensuring that the user cannot be prompted in time due to the general recognition efficiency, so as to determine the development state of the application software; therefore, how to identify the application software with malicious codes, improve the identification efficiency and predict the development state of the application software is a technical problem to be solved.
Disclosure of Invention
The invention aims to provide a data processing system for acquiring application software, which improves the accuracy of determining the activity, further improves the accuracy of determining the abnormal condition of the application software and the accuracy of deducing the change trend of the state of the application software, and is beneficial to the monitoring of a third party and the safe use of a user.
In one aspect, the present invention provides a data processing system for acquiring application software, the system comprising: the system comprises a database, a processor and a memory storing a computer program, wherein the database comprises application software IDs and the number of activations corresponding to the application software IDs, and when the computer program is executed by the processor, the following steps are realized:
s1, according to the ID of target application software, a first active frequency list A= (A) in a first type time window is obtained from a database 1 ，A 2 ，A 3 ，……，A p )，A q Refers to the number of active times of the target application software corresponding to the q first time nodes, i= … … p, p is the number of the first time nodes, and based on A, the first type activity corresponding to the target application software ID is determined
S2, according to the ID of the target application software, the slave numberObtaining second active times list in m second type time windows in a database, and constructing a second active times set B= (B) 1 ，B 2 ，B 3 ，……，B m ) X= … … m, m is the number of time windows of the second type, where B x Refers to a second active times list in a second type y time window, and acquires B x ＝(B x1 ，B x2 ，B x3 ，……，B xn ) Wherein B is xy Refers to the number of active times of the target application software corresponding to the y-th second-class time node in the second active times list, y= … … n, n being the number of the second-class time nodes and based on B x Determining second-class liveness corresponding to target application software ID
S3, based onAnd all->Constructing an intermediate liveness list->
S4, traversing F and when eachWhen the first type of time window is determined as a target time window;
s5, whenWhen the second type time window is determined as the target time window
Compared with the prior art, the invention has obvious advantages and beneficial effects. By means of the technical scheme, the data processing system for acquiring the application software can achieve quite technical progress and practicality, has wide industrial application value, and has at least the following advantages:
according to the data processing system for acquiring the application software, the activity degree of the application software in a preset time window can be determined as a threshold value through the application software ID and the corresponding activity times in the system, abnormal application software in the application software can be accurately determined, the installation or the use of the abnormal application software by a user is reduced, and the safety of the user is improved;
meanwhile, according to the activity of the application software, the activity rate of the application software is determined, so that the change trend of the application software in a certain time period is obtained according to the activity rate of the application software, and the analysis of a third party or the corresponding application software of a user is facilitated, on one hand, abnormal application software can be determined according to the change trend of the application software, the condition that missing or error occurs in judgment of the application software is avoided, on the other hand, the monitoring of the application software can be realized, and the safety prompt to a user side is facilitated;
in addition, through the activity degree calculation corresponding to different time windows, the optimized selected application software activity times are determined, the accuracy of determining the activity degree is improved, and further, the accuracy of determining abnormal conditions of the application software and the accuracy of deducing the state of the application software to change trend are improved, so that the monitoring of a third party and the safe use of a user are facilitated.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention, as well as the preferred embodiments thereof, together with the following detailed description of the invention, given by way of illustration only, together with the accompanying drawings.
Drawings
FIG. 1 is a flowchart illustrating steps performed by a data processing system for acquiring application software according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating steps performed by another data processing system for acquiring application software according to an embodiment of the present invention.
FIG. 3 is a flowchart illustrating steps performed by another data processing system for acquiring application software according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects of the present invention for achieving the predetermined object, the following detailed description refers to a specific embodiment of a data processing system for acquiring a target location according to the present invention and its effects, with reference to the accompanying drawings and preferred embodiments.
Examples
The embodiment provides an abnormal application software acquisition system based on active behavior of application software, which comprises: the system comprises a database, a processor and a memory storing a computer program, wherein the database comprises application software IDs and the number of activations corresponding to the application software IDs, and when the computer program is executed by the processor, the following steps are realized as shown in figure 1:
s100, according to the target application software ID, obtaining a target active times list corresponding to the target application software ID in N target time windows from a database to construct a target data set D= (D) 1 ，D 2 ，D 3 ，……，D N )，D i Refers to a target active number list in the ith target time window, i= … … N, N being the number of target time windows.
Specifically, the application ID refers to a unique identity of the application.
Specifically, the activation times include one or more combinations of installation times of application software, uploading information times of application software, starting times of application software and closing times of application software; preferably, the number of active times includes the number of installation times of the application software, the number of uploading information of the application software, the number of starting times of the application software and the number of closing times of the application software, which can be understood as: when any application software executes any instruction in installation, uploading information, starting and closing, the number of active times is increased, the number of active times of the application software can be accurately obtained, and the situation that some omission or errors exist is avoided.
Specifically, D 1 Corresponding target time window < D 2 Corresponding target time window < D 3 Corresponding target time window is less than … … and less than D N Corresponding target time windows, and the time intervals between adjacent target time windows are consistent.
S200, based on D i Acquiring target liveness F corresponding to target application software ID i And according to F i Constructing a target liveness list F= (F) corresponding to the target application software ID 1 ，F 2 ，F 3 ，……，F N )。
Specifically F i It can be understood that the target activity is within the ith target time window, and F i The corresponding target time window has a mark value T i And so on, wherein T 1 ＜T 2 ＜T 3 ＜……＜T N Wherein the mark difference T between adjacent target time window mark values 0 And consistent.
Specifically, in step S200, the method further comprises the steps of determining F i ：
S201, according to the target application software ID, obtaining a target active times list D corresponding to the target application software ID in any target time window from a database i ＝(D i1 ，D i2 ，D i3 ，……，D iM )，D ij Refers to the number of active times of the jth target time node, j= … … M, and M is the number of target time nodes.
S203 based on D i Acquiring target activity F of target application software i Wherein F is i Meets the following conditions:
wherein a is i0 、b i0 And c i0 Are all target parameters.
Specifically, D i1 Corresponding time node < D i2 Corresponding toTime node < D i3 Corresponding time node is less than … … and less than D iM Corresponding time nodes, and the time intervals between adjacent time nodes are consistent.
Preferably, in step S203, the method further includes the steps of determining the target parameter:
according to D i Obtaining an intermediate value H (D ij ) Wherein H (D ij ) Meets the following conditions:
wherein, H () is a preset function;
according to H (D ij ) Obtaining a i0 、b i0 And c i0 Wherein, the method comprises the steps of, wherein,and->
S300, according to F, obtaining a target activity rate list K= (K) corresponding to the target application software ID 1 ，K 2 ，K 3 ，……，K N ) Wherein K is i It can be understood that the target activity rate, K, corresponding to the target application software ID in the ith target time window i Meets the following conditions:
s400, traversing K and when K i When the target activity is more than 0, the target activity corresponding to the maximum target activity rate is taken as a target activity threshold F 0 Further understood is that: traversing K and taking the target liveness corresponding to the first maximum target liveness in the K sequence as a target liveness threshold when a plurality of maximum target liveness exists in the K, so that the threshold of liveness can be accurately determined, and abnormal application software and the current development state of the application software can be determined based on the liveness.
S500, traversing F and F i ≤F 0 And determining the target application software as abnormal application software.
Specifically, the abnormal application is an application with malicious code or other non-conventional code.
According to the embodiment, the activity degree of the application software in the preset time window can be determined as the threshold value through the application software ID and the corresponding activity times in the system, so that the abnormal application software in the application software can be accurately determined, the installation or the use of the abnormal application software by a user is reduced, and the safety of the user is improved.
In a specific embodiment, when the computer program is executed by the processor, the following steps are further implemented, where the steps S100-S500 may refer to the specific implementation manner in the above embodiment, and are not repeated herein, and other steps are shown in fig. 2:
s600, obtaining a change degree R= (R) corresponding to the target application software ID 1 ，R 2 ，R 3 ，……，R N ) And go through F and F i ≤F 0 And R is i When the value is more than 0, the state of the target application software is unknown, R i It can be understood that the degree of change corresponding to the target application software ID in the ith target time window, where R i Meets the following conditions:
specifically, the unknown state refers to a trend state in which the target application software cannot be determined.
S700, when F i ＞F 0 When R is taken i And comparing the target application software key values with the sample application software key values in all key time windows to determine the state of the target application software.
Specifically, in step S700, the method further includes the steps of:
s701 according to F 0 Corresponding target time window, the first target time window is up toF 0 Each target time window between the corresponding target time windows is used as a key time window, the change degree of all sample application software in each key time window is obtained, an intermediate data list is constructed, and an intermediate data set is constructed according to all intermediate data lists;
s703, according to the intermediate data set q= (Q 1 ，Q 2 ，Q 3 ，……，Q s )，Q t Refers to an intermediate data list corresponding to the t-th key time window, t= … … s, s is the number of key time windows, and Q is obtained t ＝(Q t1 ，Q t2 ，Q t3 ，……，Q tz ) Wherein Q is tr Refers to the degree of change in the application software at the (r) th sample to depend on Q t Determining the appointed change degree value corresponding to the t-th key time windowWherein (1)>Meets the following conditions:
s705 whenWhen the state of the target application software is unchanged;
s707 whenAnd when the state of the target application software is an increasing state.
Further, the increasing state includes a flat increasing state, a slow increasing state and a free increasing state; the step S707 further includes the following steps:
when (when)When the state of the target application software is a flat increment state;
when (when)When the state of the target application software is a gradual increase state;
when (when)And when the state of the target application software is a hiking state.
Specifically, the sample application software is application software other than the target application software among all application software in the database.
Specifically, the degree of change Q of any sample application software tr Can adopt the change degree R with the target application software i The same method is determined and will not be described in detail here.
Specifically, Q 1 Corresponding critical time window < Q 2 Corresponding critical time window < Q 3 Corresponding key time window is less than … … and less than Q s And the corresponding key time windows, wherein the time intervals between the adjacent key time windows are consistent.
Specifically, when the state of the target application software is a ramp-up state or a bare-up state, the target application software is also determined to be an abnormal application software.
According to the method and the device for monitoring the application software, the activity rate of the application software is determined according to the activity of the application software, the change trend of the application software in a certain time period is obtained according to the activity rate of the application software, analysis of a third party or the application software corresponding to a user is facilitated, on one hand, abnormal application software can be determined according to the change trend of the application software, missing or error conditions of judgment of the application software are avoided, on the other hand, monitoring of the application software can be achieved, and safety prompt to a user side is facilitated.
In another specific embodiment, before step S100, the method further includes the following steps, as shown in fig. 3:
s1, according to the ID of target application software, a first active frequency list A= (A) in a first type time window is obtained from a database 1 ，A 2 ，A 3 ，……，A p )，A q Refers to the number of active times of target application software corresponding to the q-th first time node, i= … … p, p being the first time node
ˉ
The number is based on A, and the first type of activity F corresponding to the target application software ID is determined 0 ；
S2, acquiring second active times lists in m second type time windows from a database according to the ID of the target application software, and constructing a second active times set B= (B) 1 ，B 2 ，B 3 ，……，B m ) X= … … m, m is the number of time windows of the second type, where B x Refers to a second active times list in a second type y time window, and acquires B x ＝(B x1 ，B x2 ，B x3 ，……，B xn ) Wherein B is xy Refers to the number of active times of the target application software corresponding to the y-th second-class time node in the second active times list, y= … … n, n being the number of the second-class time nodes and based on B x Determining second-class liveness corresponding to target application software ID
S3, based onAnd all->Constructing an intermediate liveness list->
S4, traversing F and when eachWhen the first type of time window is determined as a target time window;
s5, whenAnd determining the second type of time window as a target time window.
Specifically, A 1 Corresponding first-class time node < A 2 Corresponding first-class time node < A 3 Corresponding first-class time node is less than … … and less than A p The corresponding first type time nodes are in a range of 7-15 days; preferably, the first type time window is 7 days, wherein the first type time node refers to a single first time slice in the first type time window, wherein the range of the first time slice is 1-2 days, and preferably, the first time slice is 1 day.
Specifically, B 1 Corresponding time window of the second class < B 2 Corresponding time window of the second class < B 3 Corresponding time window of the second type is less than … … less than B m The corresponding second time window ranges from 1 month to 2 months, preferably, the first time window ranges from 1 month, wherein the second time node refers to a single second time slice in the second time window, the second time slice ranges from 5 days to 7 days, and preferably, the second time slice ranges from 5 days.
In particular, the method comprises the steps of,and->Acquisition method of (1) and target activity degree F of target application software i The same method is adopted and will not be described in detail here.
According to the embodiment, the optimal number of times of activating the selected application software can be determined through the liveness calculation corresponding to different time windows, so that the accuracy of determining the liveness is improved, the accuracy of determining abnormal conditions of the application software and the accuracy of deducing the state of the application software to change trend are further improved, and monitoring of a third party and safe use of a user are facilitated.
The embodiment provides an abnormal application software acquisition system based on the active behavior of application software, which can determine the activity degree of the application software in a preset time window as a threshold value through the application software ID and the corresponding active times in the system, can accurately determine the abnormal application software in the application software, reduces the installation or use of the abnormal application software by a user, and improves the safety of the user;
meanwhile, according to the activity of the application software, the activity rate of the application software is determined, so that the change trend of the application software in a certain time period is obtained according to the activity rate of the application software, and the analysis of a third party or the corresponding application software of a user is facilitated, on one hand, abnormal application software can be determined according to the change trend of the application software, the condition that missing or error occurs in judgment of the application software is avoided, on the other hand, the monitoring of the application software can be realized, and the safety prompt to a user side is facilitated;
in addition, through the activity degree calculation corresponding to different time windows, the optimized selected application software activity times are determined, the accuracy of determining the activity degree is improved, and further, the accuracy of determining abnormal conditions of the application software and the accuracy of deducing the state of the application software to change trend are improved, so that the monitoring of a third party and the safe use of a user are facilitated.
The present invention is not limited to the above-mentioned embodiments, but is intended to be limited to the following embodiments, and any modifications, equivalents and modifications can be made to the above-mentioned embodiments without departing from the scope of the invention.
Claims (10)
1. A data processing system for retrieving application software, the system comprising: the system comprises a database, a processor and a memory storing a computer program, wherein the database comprises application software IDs and the number of activations corresponding to the application software IDs, and when the computer program is executed by the processor, the following steps are realized:
s1, according to the ID of target application software, a first active frequency list A= (A) in a first type time window is obtained from a database 1 ，A 2 ，A 3 ，……，A p )，A q Refers to the number of active times of the target application software corresponding to the q first time nodes, i= … … p, p is the number of the first time nodes, and based on A, the first type activity corresponding to the target application software ID is determined
S2, acquiring second active times lists in m second type time windows from a database according to the ID of the target application software, and constructing a second active times set B= (B) 1 ，B 2 ，B 3 ，……，B m ) X= … … m, m is the number of time windows of the second type, where B x Refers to a second active times list in a second type y time window, and acquires B x ＝(B x1 ，B x2 ，B x3 ，……，B xn ) Wherein B is xy Refers to the number of active times of the target application software corresponding to the y-th second-class time node in the second active times list, y= … … n, n being the number of the second-class time nodes and based on B x Determining second-class liveness corresponding to target application software ID
S3, based onAnd all->Constructing an intermediate liveness list->
S4, traversing F and when eachWhen the first type of time window is determined as a target time window;
s5, whenAnd determining the second type of time window as a target time window.
2. The data processing system for acquiring application software according to claim 1, wherein a 1 Corresponding first-class time node < A 2 Corresponding first-class time node < A 3 Corresponding first-class time node is less than … … and less than A p Corresponding time nodes of the first type.
3. The data processing system for acquiring application software according to claim 1, wherein the first type of time window ranges from 7 days to 15 days.
4. The data processing system for acquiring application software according to claim 1, wherein the first type of time node is a single first time slice within a first type of time window and the first time slice ranges from 1 day to 2 days.
5. The data processing for acquiring application software according to claim 1A system, characterized in that B 1 Corresponding time window of the second class < B 2 Corresponding time window of the second class < B 3 Corresponding time window of the second type is less than … … less than B m A corresponding time window of a second type.
6. The data processing system for acquiring application software according to claim 1, wherein the second type of time window ranges from 1 month to 2 months.
7. The data processing system for acquiring application software according to claim 1, wherein the second class of time nodes is a single second time slice within a second time window and the second time slice ranges from 5 days to 7 days.
8. The data processing system for acquiring application software according to claim 1, further comprising the steps of, after step S5:
s100, according to the target application software ID, obtaining a target active times list corresponding to the target application software ID in N target time windows from a database to construct a target data set D= (D) 1 ，D 2 ，D 3 ，……，D N )，D i The target active times list in the ith target time window is indicated, i= … … N, N is the number of target time windows;
s200, based on D i Acquiring target liveness F corresponding to target application software ID i And according to F i Constructing a target liveness list F= (F) corresponding to the target application software ID 1 ，F 2 ，F 3 ，……，F N )；
S300, according to F, obtaining a target activity rate list K= (K) corresponding to the target application software ID 1 ，K 2 ，K 3 ，……，K N )，K i Meets the following conditions:
T 0 marking differences between adjacent target time window marking values;
s400, traversing K and when K i When the target activity is more than 0, the target activity corresponding to the maximum target activity rate is taken as a target activity threshold F 0 ；
S500, traversing F and F i ≤F 0 And determining the target application software as abnormal application software.
9. The data processing system for acquiring application software according to claim 8, further comprising the step of determining F in step S200 i ：
S201, according to the target application software ID, obtaining a target active times list D corresponding to the target application software ID in any target time window from a database i ＝(D i1 ，D i2 ，D i3 ，……，D iM )，D ij The j-th target time node is the active times, j= … … M, and M is the number of the target time nodes;
s203 based on D i Acquiring target activity F of target application software i Wherein F is i Meets the following conditions:
wherein a is i0 、b i0 And c i0 Are all target parameters.
10. The data processing system for acquiring application software according to claim 9, further comprising the step of determining the target parameter in step S203:
according to D i Obtaining an intermediate value H (D ij ) Wherein H (D ij ) Meets the following conditions:
wherein, H () is a preset function;
according to H (D ij ) Obtaining a i0 、b i0 And c i0 Wherein, the method comprises the steps of, wherein,and->
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210265107.3A CN114610581B (en) | 2022-03-17 | 2022-03-17 | Data processing system for acquiring application software |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210265107.3A CN114610581B (en) | 2022-03-17 | 2022-03-17 | Data processing system for acquiring application software |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114610581A CN114610581A (en) | 2022-06-10 |
| CN114610581B true CN114610581B (en) | 2024-04-12 |
Family
ID=81865733
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210265107.3A Active CN114610581B (en) | 2022-03-17 | 2022-03-17 | Data processing system for acquiring application software |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114610581B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115408160B (en) * | 2022-09-16 | 2023-05-05 | 湖南胜云光电科技有限公司 | Data processing system for process monitoring |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20050065125A (en) * | 2003-12-24 | 2005-06-29 | 한국전자통신연구원 | Apparatus and method for sorting data flow based on bandwidth |
| CN108509979A (en) * | 2018-02-28 | 2018-09-07 | 努比亚技术有限公司 | A kind of method for detecting abnormality, server and computer readable storage medium |
| CN113283486A (en) * | 2021-05-14 | 2021-08-20 | 杭州云深科技有限公司 | WiFi-based equipment type determination method, electronic equipment and storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10284453B2 (en) * | 2015-09-08 | 2019-05-07 | Uber Technologies, Inc. | System event analyzer and outlier visualization |
| US10691572B2 (en) * | 2017-08-30 | 2020-06-23 | Nvidia Corporation | Liveness as a factor to evaluate memory vulnerability to soft errors |
-
2022
- 2022-03-17 CN CN202210265107.3A patent/CN114610581B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20050065125A (en) * | 2003-12-24 | 2005-06-29 | 한국전자통신연구원 | Apparatus and method for sorting data flow based on bandwidth |
| CN108509979A (en) * | 2018-02-28 | 2018-09-07 | 努比亚技术有限公司 | A kind of method for detecting abnormality, server and computer readable storage medium |
| CN113283486A (en) * | 2021-05-14 | 2021-08-20 | 杭州云深科技有限公司 | WiFi-based equipment type determination method, electronic equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 在线软件生态系统用户活跃度的经验研究;刘文沛;崔强;杨达;;计算机工程与设计;20130116(第01期);第185-189页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114610581A (en) | 2022-06-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109379379B (en) | Network intrusion detection method based on improved convolutional neural network | |
| US6289328B2 (en) | Chemical sensor pattern recognition system and method using a self-training neural network classifier with automated outlier detection | |
| Ronen et al. | netSmooth: Network-smoothing based imputation for single cell RNA-seq | |
| Bauldry | Structural equation modeling | |
| WO2002067075A2 (en) | Stochastic modeling of spatial distributed sequences | |
| CN114610581B (en) | Data processing system for acquiring application software | |
| Rossel et al. | Automatic specimen identification of Harpacticoids (Crustacea: Copepoda) using Random Forest and MALDI‐TOF mass spectra, including a post hoc test for false positive discovery | |
| CN110912908A (en) | Network protocol anomaly detection method and device, computer equipment and storage medium | |
| Shu et al. | Performance assessment of kernel density clustering for gene expression profile data | |
| CN114595456A (en) | Abnormal application software acquisition system based on active behavior of application software | |
| CN110166422B (en) | Domain name behavior recognition method and device, readable storage medium and computer equipment | |
| CN114266046A (en) | Network virus identification method and device, computer equipment and storage medium | |
| CN114611101B (en) | Data processing system for acquiring trend of application software | |
| CN113779568A (en) | Abnormal behavior user identification method, device, equipment and storage medium | |
| CN113959477A (en) | Environmental control monitoring reminding method and system based on Internet of things | |
| CN113468017A (en) | Online service state detection method applied to block chain and service server | |
| CN112528306A (en) | Data access method based on big data and artificial intelligence and cloud computing server | |
| CN111783088A (en) | Malicious code family clustering method and device and computer equipment | |
| CN116340936A (en) | ICS intrusion detection system and method integrating reinforcement learning and feature selection optimization | |
| Kazemi et al. | Estimating the drift time for processes subject to linear trend disturbance using fuzzy statistical clustering | |
| CN113380330B (en) | PHMM model-based differential identifiability gene sequence clustering method | |
| CN114266045A (en) | Network virus identification method and device, computer equipment and storage medium | |
| EP3274820B1 (en) | Systems and methods for detecting copied computer code using fingerprints | |
| CN113282927A (en) | Malicious code detection method, device and equipment and computer readable storage medium | |
| Casimiro et al. | An analysis of the positional distribution of DNA motifs in promoter regions and its biological relevance |
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 |