CN115623531A - Hidden monitoring equipment discovering and positioning method using wireless radio frequency signal - Google Patents

Hidden monitoring equipment discovering and positioning method using wireless radio frequency signal Download PDF

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Publication number
CN115623531A
CN115623531A CN202211504490.XA CN202211504490A CN115623531A CN 115623531 A CN115623531 A CN 115623531A CN 202211504490 A CN202211504490 A CN 202211504490A CN 115623531 A CN115623531 A CN 115623531A
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time
equipment
hidden monitoring
hidden
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CN115623531B (en
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陈垣毅
王德志
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Zhejiang University City College ZUCC
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Zhejiang University City College ZUCC
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    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0257—Hybrid positioning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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Abstract

The invention relates to a method for discovering and positioning hidden monitoring equipment by utilizing wireless radio frequency signals, which comprises the following steps: constructing a hidden monitoring equipment classification model in an off-line manner; setting a time period for discovering the hidden monitoring equipment, and carrying out time slice division to discover an active communication channel; designing a self-adaptive spectrum sensing method of the equipment, and determining a communication channel for hiding the discovery and positioning of the monitoring equipment; collecting wireless flow data packets on line, grouping according to physical addresses, and identifying the type of the hidden monitoring equipment; and the equipment positioning is realized by utilizing the wireless flow bit rate and the wireless signal strength of the hidden monitoring equipment. The invention has the beneficial effects that: the method can be deployed on mobile equipment such as a smart phone or a tablet personal computer of a user, has the advantages of universality, easiness in deployment, automation in discovery process and high positioning accuracy, and has great social public benefit significance and economic value.

Description

Hidden monitoring equipment discovering and positioning method using wireless radio frequency signal
Technical Field
The invention relates to the technical field of hidden device discovery, in particular to a method for discovering and positioning hidden monitoring devices by utilizing wireless radio frequency signals.
Background
The existing detection method for hiding the monitoring equipment comprises the following steps: 1) Based on the detection method of light reflection, signs of minute reflection from the camera lens are detected. There are studies to detect hidden monitoring devices by detecting infrared rays emitted by an infrared camera in dark light, but this technology can only detect in dark light and can only detect the infrared camera. The research also uses an LED lattice to actively emit laser, and hidden equipment is found by detecting the image of the laser lattice after being refracted by a lens, but the technology can only detect under the condition of dim light and is easy to misjudge; 2) Electromagnetic leakage from an operating camera is identified based on a detection method of the electromagnetic field. The method has the main defects that the interference items are too many, and the high magnetic field can be detected when the interference items approach an electronic device or a piece of metal, so that the false alarm is frequently detected; 3) The detection method based on wireless spectrum sensing, but the prior art fails in two cases, namely, a lawless person can use a separate wireless network for the hidden monitoring device and provide the user with access to a separate guest network, and in another case, the hidden monitoring device is allocated to a different 802.11 wireless channel, encryption is enabled (for example, WPA2/WPA 3), and the network SSID of the device connection is hidden.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a hidden monitoring device discovering and positioning method by utilizing wireless radio frequency signals. The technical principle of the invention is that video and audio can be continuously generated in the process of hiding the data recorded by the monitoring equipment, and the audio and the video can generate unique audio and video stream data under the action of a check coding mechanism. Thus, if these invisible network traffic pattern characteristics can be captured, the presence of the hidden camera can be sent and further located. The method comprises the following specific steps:
s1, collecting transmission data packets of various hidden monitoring devices in an off-line stage, designing device identification characteristics by utilizing inherent flow patterns and time fingerprints of different devices, and training a multi-classification model to find the types of the hidden monitoring devices;
s2, setting a time period for discovering the hidden monitoring equipment, dividing the time period into a plurality of time slices with fixed lengths, adopting periodic polling to each time slice, and taking a communication channel receiving a polling reply beacon as an active communication channel;
s3, designing a self-adaptive frequency spectrum sensing method of the equipment, selecting a wireless communication channel for hiding the discovery and positioning of the monitoring equipment from all active communication channels of each time slice, and collecting all wireless flow data packets of the communication channel;
s4, grouping the wireless traffic data packets collected in the step S3 according to the physical addresses of the devices, and inputting each group of wireless traffic data packets into the device classification model established in the step S1 to obtain the device types corresponding to the group of wireless traffic data packets;
s5, in the step S4 of detecting the environment movement record, hiding the rising and falling conditions of the wireless flow bit rate of the monitoring equipment, and carrying out coarse-grained position location on the hidden monitoring equipment; and then, combining the wireless signal strength measurement value with an inertial ranging technology to obtain a final positioning result of the hidden monitoring equipment.
Preferably, S1 comprises:
s101, when different monitoring devices communicate with a gateway, the gateway records a plurality of communication data traffic packets of different monitoring device setting stages, and analyzes traffic packet metadata, wherein attribute information extracted from each data packet comprises: the length of the physical address frame; controlling a frame; a duration of time; a physical address of a network access point; a source device physical address; a physical address of the destination device; a link layer protocol; a transport layer protocol; packet length; an IP address;
s102, constructing a time aggregation characteristic for each attribute information; designing a multi-time scale feature scheme to select a time window suitable for each device transmission mode; first, a maximum sensing time window is defined
Figure 710751DEST_PATH_IMAGE001
For each characteristic attribute, for each time instant
Figure 893470DEST_PATH_IMAGE002
Defining different time offsets
Figure 79732DEST_PATH_IMAGE003
To set time windows of different lengths
Figure 271679DEST_PATH_IMAGE004
Wherein
Figure 226997DEST_PATH_IMAGE005
(ii) a Calculating the characteristics of each attribute in each time window by using an aggregation function according to the attribute information of each time window, and splicing and connecting calculation results in series to obtain the equipment identification characteristics of the window; the aggregation function specifically comprises an average value, a standard deviation, a median, a maximum value, a minimum value, a sum, an entropy and a histogram;
s103, standardizing the time aggregation characteristics and eliminating the correlation redundant characteristics;
s104, for each monitoring device
Figure 846197DEST_PATH_IMAGE006
The gateway also records the data packet from the equipment
Figure 519755DEST_PATH_IMAGE007
Time of arrival of
Figure 249814DEST_PATH_IMAGE008
Adding it to the packet arrival time series
Figure 325217DEST_PATH_IMAGE009
To the end of (c). L is a preset empirical threshold, and when the gateway receives L +1 pieces of coming equipment
Figure 849739DEST_PATH_IMAGE006
Data packet of
Figure 338489DEST_PATH_IMAGE010
After, profit
Figure 482026DEST_PATH_IMAGE011
Computing packet arrivalsSequence of time intervals
Figure 536570DEST_PATH_IMAGE012
Wherein, in the step (A),
Figure 169676DEST_PATH_IMAGE013
the calculation method is as follows:
Figure 145722DEST_PATH_IMAGE014
s105, calculating time interval sequencePAIThe distribution characteristics are obtained by the sectional density, and the calculation method comprises the following steps: time interval
Figure 621179DEST_PATH_IMAGE015
Average score
Figure 999070DEST_PATH_IMAGE016
Sub-time intervals
Figure 740761DEST_PATH_IMAGE017
. Calculating outPAIThe number of time intervals falling within each subinterval is recorded as
Figure 672945DEST_PATH_IMAGE018
. Order toSUMIs composed ofPAIThe time interval in (1) falls within the time interval
Figure 282918DEST_PATH_IMAGE019
The segment density calculating method is as follows:
Figure 187420DEST_PATH_IMAGE020
the
Figure 490226DEST_PATH_IMAGE021
Dimension sequence
Figure 175285DEST_PATH_IMAGE022
Is marked asDevice
Figure 464315DEST_PATH_IMAGE023
The time fingerprint of (c).
S106, for each data packet, the time aggregation characteristics and the time series distribution characteristics processed in the step S103
Figure 347957DEST_PATH_IMAGE024
Splicing to form sample characteristic vectors, and establishing a hidden monitoring equipment classification model by adopting the following method: and constructing an integrated classifier by using the Hough tree as a base classifier, creating a classifier pool, and training the examples. When the data stream is input, a fast Hough drift detection method is used for detection, if concept drift occurs, the current data stream instance is cached in a window, a current base classifier is reset, the base classifier is retrained by using the instance in the window, and the base classifier is added into a classifier pool. And updating the base classifier according to the classification accuracy of the test data, and deleting the base classifier with the weight value smaller than the preset threshold while increasing according to the principle of increasing one base classifier at a time when increasing the base classifiers. When the base classifier is reduced, the base classifier with the worst performance is deleted according to the threshold value. Finally, the number of base classifiers for the best ensemble classifier is determined.
Preferably, S3 comprises:
s301, for all active wireless communication channels in the time slice
Figure 697030DEST_PATH_IMAGE025
Randomly initializing its selection score between 0 and 1
Figure 666123DEST_PATH_IMAGE026
；
S302, each device related to the time slice collection data packet
Figure 493265DEST_PATH_IMAGE027
Calculate its selection score
Figure 496993DEST_PATH_IMAGE028
；
S303, utilizing equipment
Figure 282546DEST_PATH_IMAGE027
Is selected as a score
Figure 738936DEST_PATH_IMAGE028
Obtaining and apparatus
Figure 366838DEST_PATH_IMAGE027
And selecting the communication channel with the highest selection score as a data packet acquisition channel for discovering the hidden monitoring device in the time slice.
Preferably, S302 includes:
s3021 if it is provided with
Figure 959494DEST_PATH_IMAGE027
The sent data packet is larger than a preset threshold value
Figure 978265DEST_PATH_IMAGE029
Then the device in the time slice is used
Figure 797317DEST_PATH_IMAGE027
Is selected as a score
Figure 356474DEST_PATH_IMAGE028
Set to 0;
s3022, if the slave device
Figure 944581DEST_PATH_IMAGE027
The sent data packet is less than the preset threshold value
Figure 930992DEST_PATH_IMAGE029
Extracting device
Figure 237340DEST_PATH_IMAGE027
The data packet in the time slice is used as the output, and the equipment is obtained through the equipment classification model established in the off-line stage in the step S1
Figure 334609DEST_PATH_IMAGE027
The device type of (d); counting the time slice device
Figure 42802DEST_PATH_IMAGE027
Average packet inter-arrival time of (D), noted
Figure 137797DEST_PATH_IMAGE030
(ii) a Calculating the device using the following formula
Figure 56074DEST_PATH_IMAGE027
Selection score of (2):
Figure 832400DEST_PATH_IMAGE031
wherein the content of the first and second substances,
Figure 254154DEST_PATH_IMAGE032
indicating that the last received from the device within the time slice
Figure 457734DEST_PATH_IMAGE027
The time of the data packet of (a),
Figure 863307DEST_PATH_IMAGE002
which indicates the current time of day,
Figure 443324DEST_PATH_IMAGE033
presentation pair
Figure 985164DEST_PATH_IMAGE034
The result of the calculation of (a) is rounded up.
Preferably, in S4, for any device, the center of the time window is defined as
Figure 356715DEST_PATH_IMAGE002
Corresponding to the packet arrival time and extracting the feature vector for that time segment according to step S102
Figure 187268DEST_PATH_IMAGE035
Then the device type prediction probability is:
Figure 430030DEST_PATH_IMAGE036
where K is the number of device types,
Figure 701743DEST_PATH_IMAGE037
device type specific to step S1
Figure 371758DEST_PATH_IMAGE038
The integrated classifier of (a) is provided,
Figure 361711DEST_PATH_IMAGE039
is in time slices
Figure 142585DEST_PATH_IMAGE002
Classify the device into
Figure 534383DEST_PATH_IMAGE040
The probability of (d); feature vector
Figure 375301DEST_PATH_IMAGE035
Is marked as the final predicted equipment label
Figure 118129DEST_PATH_IMAGE041
：
Figure 702694DEST_PATH_IMAGE042
Within a given time period for discovering hidden monitoring devices
Figure 948998DEST_PATH_IMAGE041
And performing majority voting to obtain a final type prediction label of the equipment.
Preferably, S5 comprises:
s501, automatically extracting an intervention time window by using an accelerometer on intelligent equipment, recording correspondingly found rising and falling conditions of the wireless flow bit rate of the hidden monitoring equipment through multiple movements and stops of a user, judging whether the hidden monitoring equipment and the user are located in the same space, and realizing coarse-grained positioning of the hidden monitoring equipment;
s502, collecting when the user walks in the detection environment
Figure 960817DEST_PATH_IMAGE043
A sample, where x and y are coordinates obtained by the inertial navigation estimation method with respect to the initial point of the user' S initial walking, and RSS represents the signal strength of the hidden monitoring device found in step S4. The method comprises the following steps of carrying out grid division on a walking area of a user, and estimating grids without observing signal strength based on the following steps:
first, for mesh that is not observed
Figure 659782DEST_PATH_IMAGE044
Selecting a rectangular region including N meshes centered on the mesh
Figure 48038DEST_PATH_IMAGE045
Secondly, according to the spatial similarity of the wireless radio frequency information, grid coordinates are matched
Figure 883270DEST_PATH_IMAGE046
And its signal strength
Figure 65990DEST_PATH_IMAGE047
The mapping function of (a) is expressed as follows:
Figure 514901DEST_PATH_IMAGE048
wherein the content of the first and second substances,
Figure 441269DEST_PATH_IMAGE049
is a multi-quadrilateral radial basis function, parameter
Figure 396587DEST_PATH_IMAGE050
For the distance between the two grids to be calculated,
Figure 15787DEST_PATH_IMAGE051
to represent
Figure 689345DEST_PATH_IMAGE046
Grid to grid
Figure 419404DEST_PATH_IMAGE052
N is the number of meshes of the rectangular area.
Figure 229228DEST_PATH_IMAGE053
The following 3 rd order polynomial is used for calculation:
Figure 19329DEST_PATH_IMAGE054
wherein
Figure 180183DEST_PATH_IMAGE055
To estimate the parameters.
Will be provided with
Figure 448354DEST_PATH_IMAGE056
Rewriting is in matrix form:
Figure 378263DEST_PATH_IMAGE057
wherein:
Figure 339266DEST_PATH_IMAGE058
Figure 721837DEST_PATH_IMAGE059
。
Figure 793698DEST_PATH_IMAGE060
Figure 843694DEST_PATH_IMAGE061
and substituting all networks with known signal strength in the rectangular area into the matrix equation, and calculating all estimation parameters.
In S502, all areas are divided into grids
Figure 710019DEST_PATH_IMAGE043
Fitting the data set to a form of
Figure 842535DEST_PATH_IMAGE062
Sampling to generate a small-area grid (such as 1 x 1 cm) as a query point, generating a three-dimensional linear curved surface on each point based on triangulation interpolation, and extracting the maximum value of the area of the curved surface as a final positioning result of the device position.
The beneficial effects of the invention are: the hidden monitoring equipment discovering and positioning method utilizing the wireless radio frequency signals can be deployed on mobile equipment such as a smart phone or a tablet personal computer of a user, has the advantages of universality and easiness in deployment, automatic discovering process and high positioning accuracy, and has great social and economic significance.
Drawings
FIG. 1 is a schematic diagram of an application scenario provided herein;
FIG. 2 is a flow chart providing hidden monitoring device discovery and location of the present application;
FIG. 3 is a flow chart of a hidden monitoring device classification model configuration provided herein;
fig. 4 is a flowchart of adaptive spectrum sensing for a hidden monitoring device provided in the present application;
FIG. 5 is a flow chart of hidden monitoring device positioning provided herein;
FIG. 6 is a schematic diagram of a user walking track area meshing provided by the present application;
fig. 7 is a surface fitting graph for regional wireless rf signal strength estimation provided herein.
Detailed Description
The present invention will be further described with reference to the following examples. The following examples are set forth merely to provide an understanding of the invention. It should be noted that modifications can be made to the invention by a person skilled in the art without departing from the principle of the invention, and these modifications and modifications also fall within the scope of the claims of the invention.
Example 1:
a suitable scenario for the present invention involves a lawbreaker placing a hidden monitoring device to monitor a user in an unfamiliar environment (e.g., a hotel room or public restroom, etc.), as shown in fig. 1. A lawbreaker would like to hide monitoring devices to monitor users/guests entering this strange environment, and the user would like to discover and locate these hidden monitoring devices. Lawbreakers and users all need to interact with three key resources: physical environment, hidden monitoring devices and wireless networks. The physical environment applicable to the invention can be a single room in a hotel or a complex multi-room setting in a public place. Hidden monitoring devices may be of various types, such as cameras, voice pens, speakers, plugs, vacuum cleaners, and the like. The hidden monitoring equipment carries out flow transmission through a wireless network controlled by a lawless person. The capabilities and resources of the lawbreakers are expressed as follows:
physical environment: lawbreakers have completely controlled the environment in advance, installing and hiding the monitoring devices.
Hiding the monitoring device: lawbreakers purchase and place off-the-shelf monitoring devices to monitor users and can control various device settings, such as resolution, sensitivity, etc., through the device API. Lawbreakers can also physically disguise devices such as cameras hidden in thermostats or smart plugs doubling as cameras.
A wireless network: lawbreakers can gain full access to 802.11 wireless networks and access points and can take a number of measures to hide the internet of things devices. For example, a separate wireless network may be used for the internet of things device and the user may be provided access to a separate guest network, and the device may be assigned to a different 802.11 wireless channel, enabling encryption (e.g., WPA2/WPA 3) to hide the SSID of the network to which the internet of things device is connected.
The capabilities and resources of the user are expressed as follows:
physical environment: the user can search and move around in the physical environment.
Hiding the monitoring device: the user has no knowledge of the hidden monitoring devices and does not know how many hidden monitoring devices are in this unfamiliar environment.
A wireless network: user access to the wireless network is limited; for example, access may be allowed to a guest network that may be different from the network in which the hidden monitoring device operates, but encrypted broadcast wireless traffic packets may be sniffed wirelessly.
Example 2:
considering that a user mostly does not carry expensive hardware or special detection equipment in an unfamiliar environment (such as a hotel or a resident), the invention provides a hidden monitoring equipment discovering and positioning method by using a wireless radio frequency signal, the hidden monitoring equipment can be discovered and positioned only by using mobile equipment such as a smart phone or a tablet personal computer of the user, and the method has great social and social benefits and economic values.
Specifically, as shown in fig. 2, the steps of the present invention include:
s1, collecting transmission data packets of various hidden monitoring devices in an off-line stage, designing device identification characteristics by using inherent flow patterns and time fingerprints of different devices, and training a multi-classification model to find the classes of the hidden monitoring devices;
s2, setting a time period for discovering the hidden monitoring equipment, dividing the time period into a plurality of time slices with fixed lengths, adopting periodic polling to each time slice, and taking a communication channel receiving a polling reply beacon as an active communication channel;
s3, designing a self-adaptive frequency spectrum sensing method of the equipment, selecting a wireless communication channel for hiding the discovery and positioning of the monitoring equipment from all active communication channels of each time slice, and collecting all wireless flow data packets of the communication channel;
s4, grouping the wireless flow data packets collected in the step S3 according to the physical addresses of the devices, and inputting each group of wireless flow data packets into the device classification model established in the step S1 to obtain the device types corresponding to the group of wireless flow data packets;
s5, in the step S4 of detecting environment movement record, hiding the rising and falling conditions of the wireless flow bit rate of the monitoring equipment, and carrying out coarse-grained position location on the hidden monitoring equipment; and then, combining the wireless signal strength measurement value with an inertial ranging technology to obtain a final positioning result of the hidden monitoring equipment.
As shown in fig. 3, S1 includes:
s101, when different monitoring devices communicate with a gateway, the gateway records a plurality of communication data traffic packets of different monitoring device setting stages, and analyzes traffic packet metadata, wherein attribute information extracted from each data packet comprises: the length of the physical address frame; controlling a frame; a duration of time; a physical address of a network access point; a source device physical address; a physical address of the destination device; a link layer protocol; a transport layer protocol; packet length; an IP address;
s102, constructing time aggregation characteristics for each attribute information; designing a multi-time scale feature scheme to select a time window suitable for each device transmission mode; first, a maximum sensing time window is defined
Figure 452508DEST_PATH_IMAGE063
For each characteristic attribute, for each time instant
Figure 357010DEST_PATH_IMAGE064
Defining different time offsets
Figure 394236DEST_PATH_IMAGE065
To set time windows of different lengths
Figure 16979DEST_PATH_IMAGE066
Wherein
Figure 430643DEST_PATH_IMAGE067
(ii) a Calculating the characteristics of each attribute in each time window by using an aggregation function according to the attribute information of each time window, splicing and connecting the calculation results in series,obtaining the equipment identification characteristics of the window; the aggregation function specifically comprises an average value, a standard deviation, a median, a maximum value, a minimum value, a sum, an entropy and a histogram;
s103, standardizing the time aggregation characteristics and eliminating the correlation redundant characteristics;
s104, for each monitoring device
Figure 189651DEST_PATH_IMAGE023
The gateway also records the data packet from the equipment
Figure 663358DEST_PATH_IMAGE068
Time of arrival of
Figure 507817DEST_PATH_IMAGE069
Adding it to the packet arrival time series
Figure 459593DEST_PATH_IMAGE070
To the end of (c). L is a preset empirical threshold, and when the gateway receives L +1 pieces of coming equipment
Figure 135425DEST_PATH_IMAGE023
Data packet of
Figure 389820DEST_PATH_IMAGE071
Then use
Figure 846209DEST_PATH_IMAGE070
Calculating a sequence of data packet arrival intervals
Figure 477041DEST_PATH_IMAGE072
Wherein, in the step (A),
Figure 335276DEST_PATH_IMAGE073
the calculation method is as follows:
Figure 23222DEST_PATH_IMAGE014
s105, calculating time interval sequencePAIThe distribution characteristics are obtained by the sectional density, and the calculation method comprises the following steps: time interval
Figure 966907DEST_PATH_IMAGE074
Is divided into
Figure 870272DEST_PATH_IMAGE075
Sub-time intervals of (
Figure 583013DEST_PATH_IMAGE076
). Calculating outPAIThe number of time intervals falling within each subinterval is recorded as
Figure 444790DEST_PATH_IMAGE077
. Order toSUMIs composed ofPAIThe time interval in (2) falls within the time interval
Figure 875771DEST_PATH_IMAGE078
The segment density calculation method is as follows:
Figure 848406DEST_PATH_IMAGE079
the
Figure 415654DEST_PATH_IMAGE021
Dimension sequence
Figure 448332DEST_PATH_IMAGE022
Recording as equipment
Figure 632189DEST_PATH_IMAGE080
Time fingerprint of (2).
S106, for each data packet, the time aggregation characteristics and the time series distribution characteristics processed in the step S103
Figure 142936DEST_PATH_IMAGE022
Splicing to form a sample characteristic vector, and establishing a hidden monitoring equipment classification model by adopting the following method: using HoughAnd building an integrated classifier by using the T-tree as a base classifier, creating a classifier pool, and training the examples. And detecting by using a fast Hough drift detection method when the data stream is input, caching the current data stream instance into a window if the concept drift occurs, resetting the current base classifier, retraining the base classifier by using the instance in the window, and adding the base classifier into a classifier pool. And updating the base classifier according to the classification accuracy of the test data, and deleting the base classifier with the weight value smaller than a preset threshold value while increasing according to the principle of increasing one base classifier at a time when increasing the base classifier. When the base classifiers are reduced, the base classifier with the worst performance is deleted according to the threshold value. Finally, the number of base classifiers for the best ensemble classifier is determined.
As shown in fig. 4, S3 includes:
s301, for all active wireless communication channels in the time slice
Figure 830269DEST_PATH_IMAGE081
Randomly initializing its selection score between 0 and 1
Figure 33848DEST_PATH_IMAGE082
；
S302, each device related to the time slice collection data packet
Figure 173842DEST_PATH_IMAGE083
Calculate its selection score
Figure 753860DEST_PATH_IMAGE084
；
S303, utilization device
Figure 295699DEST_PATH_IMAGE083
Is selected as a score
Figure 998076DEST_PATH_IMAGE084
Obtaining and apparatus
Figure 521241DEST_PATH_IMAGE083
Of associated communication channelsAnd selecting the score, and taking the communication channel with the highest selected score as a data packet acquisition channel for discovering the hidden monitoring device in the time slice.
If the active wireless communication channel is present in S303
Figure 498424DEST_PATH_IMAGE081
If there is a transmitting device, the maximum value of all device selection scores transmitted by the wireless channel is used as the frequency channel
Figure 35716DEST_PATH_IMAGE081
Is selected score
Figure 705731DEST_PATH_IMAGE082
。
S302 comprises:
s3021, if yes, providing
Figure 961263DEST_PATH_IMAGE083
The sent data packet is larger than a preset threshold value
Figure 742137DEST_PATH_IMAGE029
Then the device in the time slice is used
Figure 133936DEST_PATH_IMAGE083
Is divided into
Figure 974853DEST_PATH_IMAGE084
Set to 0;
s3022, if the slave equipment
Figure 779998DEST_PATH_IMAGE083
The sent data packet is less than the preset threshold value
Figure 239929DEST_PATH_IMAGE029
Extracting device
Figure 610867DEST_PATH_IMAGE083
The data packets in the time slice are used as the output, and the equipment classification module is established in the off-line stage through the step S1Mold forming apparatus
Figure 763631DEST_PATH_IMAGE083
The device type of (d); counting the time slice device
Figure 587231DEST_PATH_IMAGE083
Average inter-packet arrival time of (D), is noted
Figure 850853DEST_PATH_IMAGE030
(ii) a Calculating the device using the following formula
Figure 76298DEST_PATH_IMAGE083
Selection score of (2):
Figure 134384DEST_PATH_IMAGE031
wherein, the first and the second end of the pipe are connected with each other,
Figure 914121DEST_PATH_IMAGE085
indicating the last time the time slice received from the device
Figure 840489DEST_PATH_IMAGE083
The time of the data packet of (a),
Figure 792877DEST_PATH_IMAGE086
which indicates the current time of day,
Figure 412077DEST_PATH_IMAGE087
pair of representations
Figure 85635DEST_PATH_IMAGE088
The result of the calculation of (a) is rounded up.
In S4, for any equipment, the center of the time window is defined as
Figure 550114DEST_PATH_IMAGE089
Which corresponds to the packet arrival time, and extracts the feature vector for the time period according to step S102
Figure 625518DEST_PATH_IMAGE090
Then the device type prediction probability is:
Figure 415619DEST_PATH_IMAGE091
where K is the number of device types,
Figure 310894DEST_PATH_IMAGE092
device type specific to step S1
Figure 844643DEST_PATH_IMAGE093
The multi-classifier of (1) is,
Figure 40132DEST_PATH_IMAGE094
is in time slices
Figure 735556DEST_PATH_IMAGE095
Classify the device into
Figure 383706DEST_PATH_IMAGE093
The probability of (d); feature vector
Figure 455567DEST_PATH_IMAGE090
Is marked as the final predicted equipment label
Figure 239984DEST_PATH_IMAGE096
：
Figure 371888DEST_PATH_IMAGE097
Within a given time period for discovering hidden monitoring devices
Figure 569651DEST_PATH_IMAGE096
And performing majority voting to obtain a final type prediction label of the equipment.
As shown in fig. 5, S5 includes:
s501, if the hidden monitoring device directly monitors the user (such as video recording or sound recording), when the user starts moving, the bit rate of the device transmission flow should be displayed to be obviously increased, and when the user stops, the bit rate should be displayed to be reduced. Therefore, the method and the device automatically extract the intervention time window by using the accelerometer on the intelligent device, record correspondingly found rising and falling conditions of the wireless flow bit rate of the hidden monitoring device through multiple movements and stops of the user, judge whether the hidden monitoring device and the user are located in the same space, and realize coarse-grained positioning of the hidden monitoring device;
s502, collecting when the user walks in the detection environment
Figure 320569DEST_PATH_IMAGE043
A sample, where x and y are coordinates obtained by an inertial navigation estimation method with respect to an initial point where a user starts to walk, and RSS represents the signal strength of the hidden monitoring device found in step S4; and (3) carrying out grid division (such as 0.5 m-0.5 m grid division) on a user walking area, calculating an average observed signal intensity value of each grid, and selecting a grid center with the maximum average signal intensity value as a positioning result of the hidden monitoring equipment. By way of example, fig. 6 shows a user walking track area meshing schematic diagram, wherein each black point represents a walking coordinate point of a user. The walking area in fig. 6 is divided into four grids of a, B, C and D, each grid is provided with a plurality of walking coordinate points, the average observation signal intensity values of different grids can be obtained by calculating the signal intensity of all the walking coordinate points in each grid, and the positioning result of the hidden monitoring device can be determined. For example, when the average observed signal strength value of grid a is greater than the average observed signal strength values of grids B, C, and D, grid a is used as the positioning result of the hidden monitoring device.
In S502, the grid for which no signal strength is observed is estimated based on the following steps:
first, for mesh that is not observed
Figure 349705DEST_PATH_IMAGE098
Selecting a rectangular region including N meshes centering on the mesh
Figure 259368DEST_PATH_IMAGE099
；
Secondly, according to the spatial similarity of the wireless radio frequency information, grid coordinates are matched
Figure 6744DEST_PATH_IMAGE100
And its signal strength
Figure 295774DEST_PATH_IMAGE101
The mapping function of (c) is expressed as follows:
Figure 179416DEST_PATH_IMAGE102
wherein, the first and the second end of the pipe are connected with each other,
Figure 262910DEST_PATH_IMAGE103
is a multi-quadrilateral radial basis function, parameter
Figure 232003DEST_PATH_IMAGE104
For the distance between the two grids to be calculated,
Figure 324724DEST_PATH_IMAGE105
to represent
Figure 62873DEST_PATH_IMAGE106
Grid to grid
Figure 582847DEST_PATH_IMAGE107
N is the number of meshes of the rectangular area;
Figure 39236DEST_PATH_IMAGE108
the following polynomial of order 3 is used for calculation:
Figure 670069DEST_PATH_IMAGE109
wherein
Figure 528303DEST_PATH_IMAGE110
To estimate the parameters;
will be provided with
Figure 219179DEST_PATH_IMAGE111
Rewriting is in matrix form:
Figure 569389DEST_PATH_IMAGE112
in which
Figure 331808DEST_PATH_IMAGE113
，
Figure 44549DEST_PATH_IMAGE114
；
Figure 903396DEST_PATH_IMAGE115
Figure 334378DEST_PATH_IMAGE116
And substituting all networks with known signal strength in the rectangular area into the matrix equation, and calculating all estimation parameters.
In S502, pair
Figure RE-420241DEST_PATH_IMAGE124
The data set is fitted to a form of
Figure RE-150300DEST_PATH_IMAGE125
Sampling to generate a small-area grid (such as 1 x 1 cm) as a query point, generating a three-dimensional linear curved surface on each point based on triangulation interpolation, and extracting the maximum value of the area of the curved surface as a final positioning result of the device position. Illustratively, FIG. 7 shows a surface fitting graph for regional radio frequency signal strength estimation, dividing a walking region into an A region, a B region, a C region and a D region, and determining the walking region according to the area in each region
Figure RE-209391DEST_PATH_IMAGE124
Fitting the data set to obtain corresponding
Figure RE-265072DEST_PATH_IMAGE125
A curved surface. In the surface, the X, Y, axis represents the planar position of the query point, and the Z axis represents the signal strength of the query point. And then, the final positioning result of the equipment position can be determined according to the maximum value of the area of the curved surface.
In order to verify effectiveness, 50 hidden monitoring devices are selected for experimental testing in an actual scene, and the experimental testing is as follows: a camera: 25 in number; 15 recording pens are provided; a microphone: 10, each category having multiple devices of the same type to avoid overfitting the experimental results to a particular supplier. The experimental environment included 4: 1) 50 square meters of office; 2) Single apartment of 26 and 35 square meters; 3) 300 square meters laboratory.
The experimental results are shown in the table below, and the experimental results show that the method can achieve the precision equivalent to the method assuming the whole network access.
Table 1 hidden monitoring device discovery and localization effects
Figure 860933DEST_PATH_IMAGE122
In summary, the method and the device for detecting the hidden monitoring device in the mobile phone or the tablet computer rapidly identify whether the hidden monitoring device exists in the space and accurately position the hidden monitoring device through detecting whether the network flow mode specific to the hidden monitoring device exists in the environment, the device network does not need to be accessed in the detection process, any information does not need to be provided by a user, the device can be arranged on the mobile phone or the tablet computer carried by the user, and the method and the device have great social public benefit and economic value.

Claims (8)

1. A hidden monitoring device discovering and positioning method using wireless radio frequency signals is characterized by comprising the following steps:
s1, collecting transmission data packets of various hidden monitoring devices in an off-line stage, designing device identification characteristics by using inherent flow patterns and time fingerprints of different devices, and training a multi-classification model to find the classes of the hidden monitoring devices;
s2, setting a time period for discovering the hidden monitoring equipment, dividing the time period into a plurality of time slices with fixed lengths, adopting periodic polling to each time slice, and taking a communication channel receiving a polling reply beacon as an active communication channel;
s3, designing a self-adaptive frequency spectrum sensing method of the equipment, selecting a wireless communication channel for hiding the discovery and positioning of the monitoring equipment from all active communication channels of each time slice, and collecting all wireless flow data packets of the communication channel;
s4, grouping the wireless flow data packets collected in the step S3 according to the physical addresses of the devices, and inputting each group of wireless flow data packets into the device classification model established in the step S1 to obtain the device types corresponding to the group of wireless flow data packets;
s5, in the step S4 of detecting environment movement record, hiding the rising and falling conditions of the wireless flow bit rate of the monitoring equipment, and carrying out coarse-grained position location on the hidden monitoring equipment; and then, combining the wireless signal strength measurement value with an inertial ranging technology to obtain a final positioning result of the hidden monitoring equipment.
2. The hidden monitoring device discovering and locating method using wireless radio frequency signals according to claim 1, wherein S1 comprises:
s101, when different monitoring devices communicate with a gateway, the gateway records a plurality of communication data traffic packets of different monitoring device setting stages, and analyzes traffic packet metadata, wherein attribute information extracted from each data packet comprises: the length of the physical address frame; controlling a frame; a duration of time; a physical address of a network access point; a source device physical address; a physical address of the destination device; a link layer protocol; a transport layer protocol; packet length; an IP address;
s102, constructing a time aggregation characteristic for each attribute information; designing a multi-time scale feature scheme to select a time window suitable for each device transmission mode;first, a maximum sensing time window is defined
Figure 156545DEST_PATH_IMAGE001
For each characteristic attribute, for each time instant
Figure 198451DEST_PATH_IMAGE003
Defining different time offsets
Figure 823467DEST_PATH_IMAGE004
To set time windows of different lengths
Figure 354943DEST_PATH_IMAGE005
Wherein
Figure 167041DEST_PATH_IMAGE006
(ii) a Calculating the characteristics of each attribute in each time window by using an aggregation function according to the attribute information of each time window, and splicing and connecting calculation results in series to obtain the equipment identification characteristics of the window; the aggregation function specifically comprises an average value, a standard deviation, a median, a maximum value, a minimum value, a sum, an entropy and a histogram;
s103, standardizing the time aggregation characteristics and eliminating the correlation redundant characteristics;
s104, for each monitoring device
Figure 543796DEST_PATH_IMAGE007
The gateway also records the data packet from the device
Figure 23318DEST_PATH_IMAGE008
Time of arrival of
Figure 460116DEST_PATH_IMAGE009
Adding it to the packet arrival time series
Figure 22160DEST_PATH_IMAGE010
End of (3); l is a preset empirical threshold, and when the gateway receives L +1 pieces of coming equipment
Figure 202605DEST_PATH_IMAGE007
Data packet of
Figure 802214DEST_PATH_IMAGE011
，
Figure 409913DEST_PATH_IMAGE012
，…,
Figure 462183DEST_PATH_IMAGE013
Then use
Figure 446319DEST_PATH_IMAGE014
Calculating a sequence of data packet arrival intervals
Figure 900434DEST_PATH_IMAGE015
Figure 679034DEST_PATH_IMAGE016
Wherein, in the step (A),
Figure 953021DEST_PATH_IMAGE017
the calculation method is as follows:
Figure 475269DEST_PATH_IMAGE018
s105, calculating time interval sequencePAIThe distribution characteristics are obtained by the sectional density, and the calculation method comprises the following steps: time interval
Figure 783891DEST_PATH_IMAGE019
Is divided into
Figure 733392DEST_PATH_IMAGE021
A sub-time interval; computingPAIThe number of time intervals falling within each subinterval is recorded as
Figure 963516DEST_PATH_IMAGE022
(ii) a Order toSUMIs composed ofPAIThe time interval in (1) falls within the time interval
Figure 820614DEST_PATH_IMAGE023
The segment density calculation method is as follows:
Figure 452583DEST_PATH_IMAGE024
the
Figure 570057DEST_PATH_IMAGE026
Dimension sequence
Figure 84215DEST_PATH_IMAGE027
Recording as equipment
Figure 151528DEST_PATH_IMAGE007
A time fingerprint of (a);
s106, for each data packet, the time aggregation characteristics and the time series distribution characteristics processed in the step S103
Figure 434741DEST_PATH_IMAGE027
Splicing to form a sample characteristic vector, and establishing a hidden monitoring equipment classification model by adopting the following method: constructing an integrated classifier by using the Hough tree as a base classifier, creating a classifier pool, and training an example; detecting by using a fast Hough drift detection method when a data stream is input, caching a current data stream instance into a window if concept drift occurs, resetting a current base classifier, retraining the base classifier by using the instance in the window, and adding the base classifier into a classifier pool; classifying the basis according to the classification accuracy of the test dataUpdating the base classifier, and deleting the base classifier with the weight value smaller than the preset threshold while increasing according to the principle of increasing one base classifier at a time when increasing the base classifiers; when the base classifiers are reduced, deleting the base classifier with the worst performance according to a threshold value; finally, the number of base classifiers for the best ensemble classifier is determined.
3. The hidden monitoring device discovering and locating method using wireless radio frequency signals according to claim 1, wherein S3 comprises:
s301, for all active wireless communication channels in the time slice
Figure 522783DEST_PATH_IMAGE028
Randomly initializing its selection score between 0 and 1
Figure 461920DEST_PATH_IMAGE029
；
S302, each device related to the time slice collection data packet
Figure 129662DEST_PATH_IMAGE031
Calculate its selection score
Figure 267382DEST_PATH_IMAGE032
；
S303, utilization equipment
Figure 995167DEST_PATH_IMAGE034
Is selected as a score
Figure 218338DEST_PATH_IMAGE032
Obtaining and apparatus
Figure 689770DEST_PATH_IMAGE034
And selecting the communication channel with the highest selection score as a data packet acquisition channel for discovering the hidden monitoring device in the time slice.
4. The hidden monitoring device discovering and locating method according to claim 3, wherein S302 comprises:
s3021, if the slave equipment
Figure 681997DEST_PATH_IMAGE031
The sent data packet is larger than the preset threshold value
Figure 315104DEST_PATH_IMAGE036
Then the device in the time slice is used
Figure 25571DEST_PATH_IMAGE031
Is selected as a score
Figure 35115DEST_PATH_IMAGE032
Set to 0;
s3022, if the slave equipment
Figure 878919DEST_PATH_IMAGE031
The sent data packet is less than the preset threshold value
Figure 682927DEST_PATH_IMAGE036
Extracting device
Figure 615111DEST_PATH_IMAGE031
The data packet in the time slice is used as input, and the equipment is obtained through the equipment classification model established in the off-line stage in the step S1
Figure 225084DEST_PATH_IMAGE031
The device type of (d); counting the time slice device
Figure 926323DEST_PATH_IMAGE031
Average inter-packet arrival time of (D), is noted
Figure 166812DEST_PATH_IMAGE037
(ii) a Calculating the device using the following formula
Figure 320713DEST_PATH_IMAGE031
Selection score of (2):
Figure 672060DEST_PATH_IMAGE038
wherein the content of the first and second substances,
Figure 493385DEST_PATH_IMAGE040
indicating the last time the time slice received from the device
Figure 170354DEST_PATH_IMAGE031
The time of the data packet of (a),
Figure 811551DEST_PATH_IMAGE042
which indicates the current time of day,
Figure 966589DEST_PATH_IMAGE043
presentation pair
Figure 908000DEST_PATH_IMAGE044
The result of the calculation of (2) is rounded up.
5. The hidden monitoring device discovering and locating method according to claim 2 wherein in S4, for any device, the center of the time window is defined as
Figure 224712DEST_PATH_IMAGE046
Which corresponds to the packet arrival time, and extracts the feature vector for the time period according to step S102
Figure 884363DEST_PATH_IMAGE047
Then the device typeThe prediction probability is:
Figure 577513DEST_PATH_IMAGE048
where K is the number of device types,
Figure 107851DEST_PATH_IMAGE049
device type specific to step S1
Figure 858114DEST_PATH_IMAGE051
The integrated classifier of (a) is provided,
Figure 739482DEST_PATH_IMAGE052
is in time slices
Figure 236323DEST_PATH_IMAGE042
Classify the device into
Figure 621168DEST_PATH_IMAGE051
The probability of (d); feature vector
Figure 545261DEST_PATH_IMAGE054
Is marked as the final predicted equipment label
Figure 913926DEST_PATH_IMAGE055
：
Figure 948878DEST_PATH_IMAGE056
Within a given time period for discovering hidden monitoring devices
Figure 719388DEST_PATH_IMAGE055
And performing majority voting to obtain a final type prediction label of the equipment.
6. The hidden monitoring device discovering and locating method according to claim 1 utilizing wireless radio frequency signals, wherein S5 comprises:
s501, an intervention time window is automatically extracted by an accelerometer on intelligent equipment, the rising and falling conditions of the wireless flow bit rate of the hidden monitoring equipment correspondingly found are recorded through multiple movements and stops of a user, whether the hidden monitoring equipment and the user are located in the same space or not is judged, and coarse-grained positioning of the hidden monitoring equipment is achieved;
s502, collecting when the user walks in the detection environment
Figure 814383DEST_PATH_IMAGE057
A sample, wherein x and y are coordinates relative to an initial point where a user starts to walk obtained by an inertial navigation calculation method, and RSS represents the signal strength of the hidden monitoring device found in step S4; and carrying out grid division on a walking area of the user, calculating an average observation signal intensity value of each grid, and selecting a grid center with the maximum average signal intensity value as a positioning result of the hidden monitoring equipment.
7. The hidden monitoring device discovering and locating method according to claim 6, wherein in S502, the grid without observed signal strength is estimated based on the following steps:
first, for mesh that is not observed
Figure 139185DEST_PATH_IMAGE058
Selecting a rectangular region including N meshes centering on the mesh
Figure 977828DEST_PATH_IMAGE059
Secondly, according to the spatial similarity of the wireless radio frequency information, grid coordinates are matched
Figure 602844DEST_PATH_IMAGE060
And its signal is strongDegree of rotation
Figure 868740DEST_PATH_IMAGE061
The mapping function of (c) is expressed as follows:
Figure 946418DEST_PATH_IMAGE062
wherein, the first and the second end of the pipe are connected with each other,
Figure 323173DEST_PATH_IMAGE063
is a multi-quadrilateral radial basis function, parameter
Figure 599433DEST_PATH_IMAGE065
For the distance between the two grids to be calculated,
Figure 236563DEST_PATH_IMAGE066
to represent
Figure 801537DEST_PATH_IMAGE067
Grid to grid
Figure 981982DEST_PATH_IMAGE067
N is the number of meshes of the rectangular area;
Figure 112750DEST_PATH_IMAGE068
the following 3 rd order polynomial is used for calculation:
Figure 720448DEST_PATH_IMAGE069
wherein
Figure 772718DEST_PATH_IMAGE070
To estimate the parameters;
will be provided with
Figure 960117DEST_PATH_IMAGE071
Rewriting is in matrix form:
Figure 945390DEST_PATH_IMAGE072
wherein:
Figure 723991DEST_PATH_IMAGE073
，
Figure 263556DEST_PATH_IMAGE074
；
Figure 785805DEST_PATH_IMAGE075
Figure 94426DEST_PATH_IMAGE076
and substituting all networks with known signal strength in the rectangular area into the matrix equation, and calculating all estimation parameters.
8. The hidden monitoring device discovering and locating method according to claim 7, wherein in S502, the area is gridded
Figure 43928DEST_PATH_IMAGE077
Fitting the data set to a form of
Figure 70789DEST_PATH_IMAGE078
Sampling to generate a small-area grid as a query point, generating a three-dimensional linear curved surface at each point based on triangulation interpolation, and extracting the maximum value of the area of the curved surface as a final positioning result of the device position.
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