CN106376020A - Method for recognizing user type in encrypted MANET (Mobile Ad Hoc Network) - Google Patents
Method for recognizing user type in encrypted MANET (Mobile Ad Hoc Network) Download PDFInfo
- Publication number
- CN106376020A CN106376020A CN201610788603.1A CN201610788603A CN106376020A CN 106376020 A CN106376020 A CN 106376020A CN 201610788603 A CN201610788603 A CN 201610788603A CN 106376020 A CN106376020 A CN 106376020A
- Authority
- CN
- China
- Prior art keywords
- user
- manet
- type
- encryption
- sequence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention relates to a method for recognizing a user type in an encrypted MANET (Mobile Ad Hoc Network). The method comprises the following steps: firstly, sorting sub-networks in an MANET network; distinguishing nodes in the network by using radio positioning according to a sub-network sorting result; recognizing a relay forwarding node on the basis that the network nodes are distinguished; recognizing an encrypted MANET service type based on traffic measurement; and recognizing the user type specific to a service type recognition result. Through adoption of the recognition method, the problems that an existing user type recognition method needs a large amount of historical data, is relatively low in recognition speed and cannot be applied to the encrypted MANET are solved.
Description
Technical field
The present invention relates to radio network technique field, the identification side of the user type in more particularly, to a kind of encryption manet
Method.
Background technology
Manet (mobile ad hoc network), the multi-hop network without center being made up of one group of wireless mobile node,
Node can freely and dynamically form arbitrarily provisional network structure, has very strong anti-blast phase and from group property, application
Extensively.
Identification for user type is mainly by being monitored analysis, Jin Ertong for a long time to the flow in network
Cross certain method and realize the identification to user type.Identification currently for user type is focused mainly on the Internet, is applied to
Personalized recommendation, including search engine and ecommerce etc..The technology recommended mainly includes recommendation based on correlation rule, is based on
The recommendation of content and the recommendation based on collaborative filtering, the index being mainly used in matching degree measurement mainly includes vectorial angle cosine
Value, maximum entropy, ratio of gains etc..Generally by methods such as data mining and machine learning, bulk information is processed, will
There is the information that the user of similar background liked with it or commodity are realized associate, and then structure user model, in recommendation process
In the user vector that formed by user history information to be identified carry out matching degree calculating with the user vector having built in model,
User type to be measured is judged to the user type in matching degree highest model, and then corresponding information or commodity are entered to user
Row is recommended, and improves the accuracy of recommendation.
Although said method more effectively can be identified to user type, however it is necessary that substantial amounts of historical data,
Recognition speed is slower, and cannot realize encrypting the identification of user type in manet.
Content of the invention
In view of above-mentioned analysis, the present invention is intended to provide in a kind of encryption manet user type recognition methodss, in order to solve
Certainly existing user type recognition methodss need substantial amounts of historical data, and recognition speed is slower, and cannot be applied to encrypt manet's
Problem.
The purpose of the present invention is mainly achieved through the following technical solutions:
A kind of recognition methodss of user type in encryption manet are provided, comprise the following steps:
S1. the subnet in manet network is sorted；
S2. nodes are made a distinction using radio position finding radio directional bearing according to subnet separation results；
S3. on the basis of network node is distinguished, relaying forward node is identified；
S4. the identification of manet type of service is encrypted based on flow measurement；
S5. for type of service recognition result, user type is identified.
Wherein, described step s1 further includes:
Using broadband receiver, signal in working frequency range is received；
The signal receiving is carried out with signal detection and pretreatment, rejects garbage signal therein；
Obtain signal chirp rate, modulation system by signal analysis；
By parameter extraction, subnet is made a distinction.
Described step s2 further includes: using compound angle localization method or digital method in same subnet
Different nodes makes a distinction.
Described step s3 further includes: realizes flow of information tracking according to flowing tracer technique end to end, realizes to information
The identification of the relay forwarding node in transmitting procedure.
Described step s4 further includes: asks for network node convergence factor average；Recognition rule using type of service
The type of service of transmission information in encryption manet is identified.
Described step s5 further includes:
S5.1. structuring user's sequence；
S5.2. carry out sequence conversion for user's sequence and generate symbol sebolic addressing；
S5.3. it is directed to the Frequent episodes collection that symbol sebolic addressing excavates unique user；
S5.4. it is directed to the Frequent episodes collection of multiple users, ask for the public frequent sequence sets of dissimilar user；
That s5.5. asks for user's Frequent episodes set pair characteristic sequence collection to be measured comprises rate；
S5.6 determines the type of user according to comprising rate.
Described step s5.1 further includes: extracts all data of same id first, carries out according to time order and function order
Arrangement；After removing object id and time information, construct the packet sequence being made up of user behavior type and type of service.
Described step s5.3 further includes: excavates frequency therein using aprioriall algorithm and is more than the frequent of threshold value
Sequence, constructs Frequent episodes set by Frequent episodes.
Described step s5.4 further includes: by setting the threshold value of frequent degree, asks for public frequent sequence sets.
Described step s5.6 further includes: the bag of communicating pair x and y Frequent episodes set pair main users characteristic sequence collection
It is ir containing ratexAnd iryIf, irx＞ iryThen x is judged to main users, user y is judged to secondary user, otherwise judges x
For secondary user, user y is judged to main users.
The present invention has the beneficial effect that: provides a kind of recognition methodss of user type in encryption manet, to user type
The accuracy rate of identification is high, and need not historical data in a large number, improve recognition speed, can effectively realize user class in encryption manet
The identification of type.
Other features and advantages of the present invention will illustrate in the following description, and, partial becoming from description
Obtain it is clear that or being understood by implementing the present invention.The purpose of the present invention and other advantages can be by the explanations write
In book, claims and accompanying drawing, specifically noted structure is realizing and to obtain.
Brief description
Accompanying drawing is only used for illustrating the purpose of specific embodiment, and is not considered as limitation of the present invention, in whole accompanying drawing
In, identical reference markss represent identical part.
Fig. 1 is the flow chart for encryption manet user type identification；
Fig. 2 is the manet simulated environment schematic diagram for user type identification.
Specific embodiment
To specifically describe the preferred embodiments of the present invention below in conjunction with the accompanying drawings, wherein, accompanying drawing constitutes the application part, and
It is used for together with embodiments of the present invention explaining the principle of the present invention.
The specific embodiment of the invention discloses a kind of recognition methodss of user type in encryption manet, such as Fig. 1, concrete bag
Include following steps:
S1: the subnet in manet network is sorted；Specifically,
Using broadband receiver, signal in working frequency range is received；
The signal receiving is carried out with signal detection and pretreatment, rejects garbage signal therein, such as noise etc.；
Obtain the parameters such as signal chirp rate, modulation system by signal analysis；
Different methods are taken to realize sub-network division by signal analysis, for synchronized orthogonal network and asynchronous network, tool
Body is clustered by transient response method or k-means algorithm, realizes the differentiation to subnet.
Wherein, for synchronized orthogonal network, in the whole network synchronizing process, different sub-network adopts synchronizing frequency presence poor
Different, and there is very strong relatedness in the synchronizing frequency adopting inside same subnet, completed (to each jump by transient response method
The discrete wavelet coefficient of frequency period transient state response carries out denoising) sorting to subnet.
For asynchronous network, after networking, the hop period of same network is identical it is possible to according to cycle-skipping
The difference of phase is clustered using k-means algorithm and then is completed the differentiation of heterogeneous networks.
S2: using radio position finding radio directional bearing, nodes are made a distinction according to subnet separation results；Specifically,
Using compound angle localization method or digital method；
Wherein, compound angle localization method is based on direction-finding station and works, by multiple radio monitoring websites to same
Individual signal carries out direction finding, and the intersection using direction finding ray (angle) is positioned；
Digital method then reaches the time of monitoring station based on signal, carries out intersection by time gap conversion fixed
Position；
And then different nodes in same subnet is made a distinction.
S3: on the basis of network node is distinguished, relaying forward node is identified；
Specifically, due to having more obvious time succession relation, recipient in manet between Frame and response frame
Confirmation or response frame can be sent as early as possible after receiving the data issuing oneself, therefore can be continued using frame and be formed in time
Incidence relation to communicating pair realize judge.Because in manet, the time delay of information transfer is equal with the maximum hop count that can pass through
There is certain restriction, there is specific threshold value；Realize flow of information tracking according to flowing tracer technique end to end, realize information is passed
The identification of relay forwarding node during defeated.
S4: the identification of manet type of service is encrypted based on flow measurement；Specifically, including following sub-step:
S4.1 asks for network node convergence factor average；
First node data is processed, generate multi-hop signal sequence, use for the multi-hop signal sequence generating
Lpvg networking, and then ask for the average of all-network node rendezvous coefficient.
S4.2: carry out type of service identification using recognition rule；
The recognition rule of type of service is used to transmission information in encryption manet according to the node rendezvous Coefficient Mean asked for
Type of service be identified, the recognition rule of business information is as shown in table 1.
| Type of service | Judgment threshold 1 | Judgment threshold 2 |
| Type of service 1 | n/a | (0.8011,0.8601) |
| Type of service 3 | >0.8325 | <0.8011 |
| Type of service 2 | n/a | >0.8601 |
Table 1 traffic identification rule
S5: user type is identified for type of service recognition result；Specifically, including following sub-step:
S5.1: structuring user's (ubt, bus_t) sequence.
Based on the premise of identifying relay forwarding node, only considering communicating pair, then only remaining in user behavior type
Send and receive.Each Transaction Information includes four attributes: object id, user behavior, type of service, when sending or receiving
Carve, extract all data of same id in data processing first, arranged according to time order and function order, due in reality
Only consider the priority problem in relevant information sequential during the analysis of border, do not consider the concrete time, so in relevant issues data
After sequencing sequence, time attribute is removed.For user's data within a certain period of time, in removal object id with the case of the moment
The packet sequence that structuring user's behavior type (ubt) and type of service (bus_t) form.
S5.2: carry out sequence conversion for user (ubt, bus_t) sequence and generate symbol sebolic addressing.
Packet is indicated complete sequence conversion using specific character, transformational rule is as follows: For receipt Other
S5.3: excavate the Frequent episodes collection of unique user for symbol sebolic addressing.
Aprioriall algorithm is used to excavate the Frequent episodes that frequency therein is more than certain threshold value for symbol sebolic addressing, will
Frequent episodes construct Frequent episodes set.
If given threshold takes 10 times for example in mining process, then if sequence occurrence frequency is big in mining process
Assert that this sequence is Frequent episodes in 10 times, and then the Frequent episodes of the unique user excavated are constructed Frequent episodes collection
Close.
S5.4: the public frequent sequence sets of dissimilar user asked for by the Frequent episodes collection for multiple users.
Set the threshold value of frequent degree, ask for the public frequent sequence sets that multiple user's Frequent episodes are concentrated, i.e. characteristic sequence
Collection (feature sequence set, fss)
Wherein, the sequence sets that take place frequently for the multiple user of same type are excavated, given threshold (threshold value can take 10),
Ask in multiple users all occurring and number of times is more than the sequence of threshold value, and then will be dissimilar for the Frequent episodes excavated construction
The corresponding public frequent sequence sets of user.
S5.5: that asks for user's Frequent episodes set pair characteristic sequence collection to be measured comprises rate.
Asking for characteristic sequence concentrates sequence to comprise rate ir in user's Frequent episodes concentration,Wherein numirFor d1
Middle sequence and d2The equal quantity of middle sequence, num is d1Middle sequence sum.
S5.6: the type that rate determines user is comprised to particular type of user characteristic sequence collection for user to be measured.
Communicating pair x and y Frequent episodes set pair main users characteristic sequence collection is calculated in user type judge process
Comprise rate irxAnd iryIf, irx＞ iryThen x is judged to main users, user y is judged to secondary user, otherwise judges x
For secondary user, user y is judged to main users.
In encryption manet of the present invention, the technique effect of the recognition methodss of user type is taken by using exata software
Build the method that simulated environment as shown in Figure 2 tested to be verified:
Step 1. builds scene: builds the manet of layering using exata software.
Step 2. builds experimental situation: using the interaction of business software finishing service information.Scene is main in running
With the addition of random function for node deployment position, that the network building every time is all existed is certain due to the change of position
Difference.
Step 3. basic parameter configures: in simulated environment, simulating area size is 30 × 45km2, using itm model
(irregular terrain model) calculates to path loss, using the decline of lognormal model computational shadowgraph.Imitative
The parameter setting of true environment interior joint is as shown in table 2.
| Parameter | Parameter value |
| phy-model | phy-abstract |
| data-rate | 1.2kbps |
| tx power | 5w |
| rx sensitivity | -80db |
The basic parameter setting of table 2 node
Step 4. generates experimental data: experiment is carried out 30 times, and wherein bus1 business is generally transmitted to subordinate by main users and uses
Family, secondary user periodicity transmits bus2 business to main users.
Step 5. analysis of experimental data: the sequence sets that user to be measured is produced are processed, and calculate it to characteristic sequence collection
Comprise rate, and then user type is judged, under the premise of information service type identification is accurate, user type is identified
Rate of accuracy reached to 90%.
In sum, a kind of recognition methodss of user type in encryption manet are embodiments provided, to user class
The accuracy rate of type identification is high, need not historical data in a large number, improve recognition speed.
It will be understood by those skilled in the art that realizing all or part of flow process of above-described embodiment method, can be by meter
Calculation machine program to complete come the hardware to instruct correlation, and described program can be stored in computer-readable recording medium.Wherein, institute
Stating computer-readable recording medium is disk, CD, read-only memory or random access memory etc..
The above, the only present invention preferably specific embodiment, but protection scope of the present invention is not limited thereto,
Any those familiar with the art the invention discloses technical scope in, the change or replacement that can readily occur in,
All should be included within the scope of the present invention.
Claims (10)
1. in a kind of encryption manet user type recognition methodss it is characterised in that include step:
S1. the subnet in manet network is sorted；
S2. nodes are made a distinction using radio position finding radio directional bearing according to subnet separation results；
S3. on the basis of network node is distinguished, relaying forward node is identified；
S4. the identification of manet type of service is encrypted based on flow measurement；
S5. for type of service recognition result, user type is identified.
2. in encryption manet according to claim 1 the recognition methodss of user type it is characterised in that described step s1
Further include:
Using broadband receiver, signal in working frequency range is received；
The signal receiving is carried out with signal detection and pretreatment, rejects garbage signal therein；
Obtain signal chirp rate, modulation system by signal analysis；
Transient response method and k-means algorithm is taken to be clustered respectively for synchronized orthogonal network and asynchronous network, to distinguish
Subnet.
3. in encryption manet according to claim 1 the recognition methodss of user type it is characterised in that described step s2
Further include: area is carried out to different nodes in same subnet using compound angle localization method or digital method
Point.
4. in encryption manet according to claim 1 the recognition methodss of user type it is characterised in that described step s3
Further include: realize flow of information tracking according to flowing tracer technique end to end, realize the relaying in message transmitting procedure is turned
Send out the identification of node.
5. in encryption manet according to claim 1 the recognition methodss of user type it is characterised in that described step s4
Further include:
Ask for network node convergence factor average；
Recognition rule using type of service is identified to the type of service of transmission information in encryption manet.
6. in encryption manet according to claim 1 the recognition methodss of user type it is characterised in that described step s5
Further include:
S5.1. structuring user's sequence；
S5.2. carry out sequence conversion for user's sequence and generate symbol sebolic addressing；
S5.3. it is directed to the Frequent episodes collection that symbol sebolic addressing excavates unique user；
S5.4. it is directed to the Frequent episodes collection of multiple users, ask for the public frequent sequence sets of dissimilar user；
That s5.5. asks for user's Frequent episodes set pair characteristic sequence collection to be measured comprises rate；
S5.6 determines the type of user according to comprising rate.
7. in encryption manet according to claim 6 the recognition methodss of user type it is characterised in that described step
S5.1 further includes: extracts all data of same id first, is arranged according to time order and function order；Removing object id
After time information, construct the packet sequence being made up of user behavior type and type of service.
8. in encryption manet according to claim 6 the recognition methodss of user type it is characterised in that described step
S5.3 further includes: excavates, using aprioriall algorithm, the Frequent episodes that frequency therein is more than threshold value, by Frequent episodes
Construction Frequent episodes set.
9. in encryption manet according to claim 6 the recognition methodss of user type it is characterised in that described step
S5.4 further includes: by setting the threshold value of frequent degree, asks for public frequent sequence sets.
10. in encryption manet according to claim 6 the recognition methodss of user type it is characterised in that described step
S5.6 further includes: what communicating pair x and y Frequent episodes set pair main users characteristic sequence integrated comprises rate as irxAnd iry, such as
Fruit irx＞ iryThen x is judged to main users, user y is judged to secondary user, otherwise x is judged to secondary user, user y
It is judged to main users.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610788603.1A CN106376020B (en) | 2016-08-30 | 2016-08-30 | A kind of recognition methods encrypting user type in MANET |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610788603.1A CN106376020B (en) | 2016-08-30 | 2016-08-30 | A kind of recognition methods encrypting user type in MANET |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106376020A true CN106376020A (en) | 2017-02-01 |
| CN106376020B CN106376020B (en) | 2019-05-14 |
Family
ID=57899010
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610788603.1A Expired - Fee Related CN106376020B (en) | 2016-08-30 | 2016-08-30 | A kind of recognition methods encrypting user type in MANET |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106376020B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109767437A (en) * | 2019-01-09 | 2019-05-17 | 电子科技大学 | Thermal-induced imagery defect characteristic extracting method based on k mean value dynamic multi-objective |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1657854A1 (en) * | 2003-08-19 | 2006-05-17 | Keio University | Radio communication device, ad hoc system, and communication system |
| CN101867988A (en) * | 2010-06-07 | 2010-10-20 | 北京邮电大学 | Centralized self-adaptation network manager node selection algorithm in Ad Hoc network |
| CN103210676A (en) * | 2010-11-15 | 2013-07-17 | 高通股份有限公司 | Geo-location aided sensing |
-
2016
- 2016-08-30 CN CN201610788603.1A patent/CN106376020B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1657854A1 (en) * | 2003-08-19 | 2006-05-17 | Keio University | Radio communication device, ad hoc system, and communication system |
| CN101867988A (en) * | 2010-06-07 | 2010-10-20 | 北京邮电大学 | Centralized self-adaptation network manager node selection algorithm in Ad Hoc network |
| CN103210676A (en) * | 2010-11-15 | 2013-07-17 | 高通股份有限公司 | Geo-location aided sensing |
Non-Patent Citations (2)
| Title |
|---|
| 叶春明: "基于节点行为特征分析的网络流量分类方法", 《电子与信息学报》 * |
| 常慧君: "基于分段、聚类和时序关联分析的用户行为分析", 《计算机应用研究》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109767437A (en) * | 2019-01-09 | 2019-05-17 | 电子科技大学 | Thermal-induced imagery defect characteristic extracting method based on k mean value dynamic multi-objective |
| CN109767437B (en) * | 2019-01-09 | 2021-03-12 | 电子科技大学 | Infrared thermal image defect feature extraction method based on k-means dynamic multi-target |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106376020B (en) | 2019-05-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Huang et al. | A power-efficient routing protocol for underwater wireless sensor networks | |
| Lin et al. | A scheme for delay-sensitive spatiotemporal routing in SDN-enabled underwater acoustic sensor networks | |
| Beghdad et al. | Boundary and holes recognition in wireless sensor networks | |
| CN101252538B (en) | Method of distributed real time detecting wireless self-grouping network topological structure | |
| Bhalaji | Reliable data transmission with heightened confidentiality and integrity in IOT empowered mobile networks | |
| CN101510841B (en) | Method and system for recognizing end-to-end flux | |
| CN102299863B (en) | Method and equipment for clustering network flow | |
| Wen et al. | Energy-efficient and delay-aware distributed routing with cooperative transmission for Internet of Things | |
| Bendjima et al. | Wormhole attack detection in wireless sensor networks | |
| Kamruzzaman et al. | Acoustic sensor networks in the Internet of Things applications | |
| Zhou et al. | A robust boundary detection algorithm based on connectivity only for 3D wireless sensor networks | |
| Anower et al. | A node estimation technique in underwater wireless sensor network | |
| CN101483935A (en) | Topology drawing algorithm for wireless Ad-Hoc network | |
| CN106376020A (en) | Method for recognizing user type in encrypted MANET (Mobile Ad Hoc Network) | |
| Sagduyu et al. | Navigating a mobile social network | |
| CN103974276B (en) | A kind of wireless-sensor network distribution type signal characteristic parameter extracting method | |
| Natarajan et al. | Adaptive Time Difference of Time of Arrival in Wireless Sensor Network Routing for Enhancing Quality of Service. | |
| Paul et al. | Performance analysis of received signal power-based Sybil detection in MANET using spline curve | |
| CN102111298A (en) | Wireless communication tree networking method based on signal strength | |
| CN106211139A (en) | A kind of recognition methods encrypting MANET interior joint type | |
| Wang et al. | SURF: A connectivity-based space filling curve construction algorithm in high genus 3D surface WSNs | |
| Parwekar et al. | Localization of sensors by base station in wireless sensor networks | |
| CN106231625B (en) | A kind of user type identifying system encrypted in MANET | |
| CN104348559B (en) | Periodical quick positioning method of underwater communication network based on centralized topology structure | |
| Wang et al. | RSSI-based node selection using neural network parameterised by particle swarm optimisation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | 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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20190514 Termination date: 20210830 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |