CN113347633B - Intelligent online monitoring system and method for intrusion of Internet of things - Google Patents
Intelligent online monitoring system and method for intrusion of Internet of things Download PDFInfo
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Abstract
The invention discloses an intelligent online monitoring system and method for intrusion of the Internet of things, belonging to the field of security monitoring of the Internet of things, and comprising the following steps: the system comprises a sound wave detection verification module and a terminal module which are positioned under the same network; the acoustic wave detection verification module comprises an acoustic wave detection unit and a watermark verification unit; the invention designs two pairs of different asymmetric keys A and B which are respectively used for encrypting signals sent by a sound wave detection unit to a terminal module and signals sent by the terminal module to a watermark verification unit, and simultaneously, because the refractive index distribution in the optical fiber is uneven, the positions of Rayleigh scattering points in the optical fiber and the intensity of Rayleigh scattering light signals are randomly distributed.
Description
Technical Field
The invention belongs to the field of security monitoring of the Internet of things, and particularly relates to an intelligent online monitoring system and method for intrusion of the Internet of things.
Background
With the rapid development of the modern society, people pay more and more obvious attention to safety problems, and especially, the security monitoring aiming at a specific area is very important and urgent. In the key areas of military exclusion areas, oil storage depots, industrial parks and stations, illegal invasion, artificial damage, spying and stealing and other behaviors often occur. The invasion and damage not only bring potential safety hazards to daily work and production environment, but also cause economic loss which is difficult to make up. In order to realize the purpose of monitoring various invasion conditions in an area in real time and enhance safety precaution, methods such as man-made patrol, electronic fence, microwave electromagnetic sensor, ultrasonic electromagnetic sensing alarm, vibrating cable, infrared ray correlation, video monitoring and the like are generally adopted at present. However, these methods consume large costs of manpower and material resources, do not achieve the actual effect, and are very susceptible to weather, temperature, electromagnetic interference of electrical equipment, regional environment, and the like.
The distributed optical fiber sensor attracts the research interest of many students with the outstanding advantages of wide measurement range, high measurement precision, long sensing distance, strong anti-interference capability, relatively low cost, convenient installation and maintenance, capability of working in various severe environments and the like. In fields such as perimeter intrusion monitoring widely in recent years, lay sensing optical fiber near monitoring region scope, can produce sound wave vibration signal when the external world has the invasion event, utilize the sensing optical fiber of laying on every side to go surveys these sound wave vibration signals, connect these sound wave vibration signal information and internet and constitute the optic fibre thing networking, send external sound wave vibration signal data to terminal processing platform through the internet and carry out data analysis, and carry out intelligent recognition to the invasion event, realize thing networking invasion intelligence on-line monitoring finally.
The 'internet of things and sensing in the first place' become common consensus in the industry of the internet of things at present. The sensor is like the 'electronic five sense organs' of the Internet of things, is a terminal tool for collecting information of the Internet of things, and is also a foundation stone of the Internet of things. And the sound wave data of the external invasion event detected by the optical fiber distributed sensor is used as the basis for intelligently identifying and distinguishing the terminal invasion event, and when the sound wave data are transmitted in the Internet, the threats of interception, tampering or counterfeiting and the like can be met, so that a plurality of potential safety hazards are brought to the intelligent perimeter invasion monitoring system of the Internet of things. For example, when an illegal member performs an intrusion action, the optical fiber acoustic wave sensing and detecting system detects an acoustic wave vibration signal corresponding to an intrusion event, and transmits acoustic wave data of the acoustic wave vibration signal to the processing terminal through the wireless network, but a hacker can steal acoustic wave data information transmitted in the wireless network, and tamper the acoustic wave data of the intrusion event into corresponding acoustic wave data without abnormality, and then transmit the acoustic wave data to the terminal processing platform, so that even if the optical fiber acoustic wave sensing and detecting system detects the intrusion event, the terminal processing platform mistakenly considers the intrusion event as an abnormal event and does not perform intrusion alarm. In the prior art, an asymmetric encryption algorithm is often adopted to encrypt sound wave information transmitted in a wireless network. Asymmetric encryption algorithms require two keys: public and private keys are simply public and private keys). The acoustic data is encrypted with a public key and the public key encrypted data can only be decrypted by a private key. Therefore, even though a hacker intercepts the sound wave data transmitted in the wireless network, the hacker does not have a private key, so that the sound wave data of the ciphertext cannot be decrypted, and the security of data transmission in the wireless network is improved.
However, the public key in the acoustic sensing detection system is transmitted in the wireless network, and if a hacker intercepts the public key transmitted in the network, the hacker can encrypt the self-forged acoustic data by using the public key and then send the data to the terminal processing platform to realize data spoofing. Therefore, the sound wave data has great safety hazard in the wireless network transmission process.
Disclosure of Invention
Aiming at the defects or the improvement requirements of the prior art, the invention provides an intelligent online monitoring system and method for intrusion of the Internet of things, which are used for solving the technical problem of lower safety in the prior art.
In order to achieve the above object, in a first aspect, the present invention provides an intelligent online monitoring system for intrusion in the internet of things, including: the system comprises a sound wave detection verification module and a terminal module which are positioned under the same network;
the acoustic wave detection verification module comprises an acoustic wave detection unit and a watermark verification unit;
the acoustic wave detection unit is used for sending a detection light pulse signal with a certain time interval to a sensing optical fiber to be monitored, acquiring a backward Rayleigh scattering curve of the whole optical fiber at different moments and sending the backward Rayleigh scattering curve to the watermark verification unit for storage; demodulating the backward Rayleigh scattering curve of the whole optical fiber at different times to obtain external sound wave vibration signals at each position of the optical fiber, encrypting by adopting a public key A in an asymmetric key A to obtain a first encrypted signal, and sending the first encrypted signal to a terminal module;
the terminal module is used for decrypting the first encrypted signal by using a private key A in the asymmetric key A to obtain a first decrypted signal, further encrypting the first decrypted signal by using a public key B in the asymmetric key B to obtain a second encrypted signal, and sending the second encrypted signal to the watermark verification unit;
the watermark verification unit is used for decrypting the second encrypted signal by adopting a private key B in the asymmetric key B to obtain a second decrypted signal; demodulating the backward Rayleigh scattering curve of the whole optical fiber at different moments to obtain demodulated external sound wave vibration signals at each position of the optical fiber, comparing the demodulated external sound wave vibration signals at each position of the optical fiber with the second decryption signal, and judging whether the two signals are consistent; if the network transmission layer intrusion is consistent with the network transmission layer intrusion, judging that the network transmission layer intrusion does not exist, and triggering the terminal module to analyze the intrusion activity; otherwise, judging that the network transmission layer intrusion exists, and alarming the network transmission layer intrusion;
the terminal module is also used for extracting the characteristics of the second decryption signal when being triggered, inputting the characteristics into a pre-trained intrusion behavior judgment model to judge whether a physical layer intrusion behavior exists or not, and if so, carrying out physical layer intrusion alarm;
wherein the second decrypted signal is characterized by: time domain features, frequency domain features, time-frequency features, and time-space features; the intrusion behavior judgment model is a machine learning model; asymmetric key B is different from asymmetric key a.
Further preferably, an arc tangent phase demodulation algorithm is adopted to demodulate the backward rayleigh scattering curve of the whole optical fiber at different times, so as to obtain external acoustic wave vibration signals at each position of the optical fiber.
Further preferably, before the external acoustic vibration signal is encrypted, k-bit significant digits after decimal point of the external acoustic vibration signal are respectively reserved for the signal data at each moment according to a rounding method, and the signal data at each moment of the external acoustic vibration signal is subjected to shaping processing; wherein, the signal data d of t moment in the external sound wave vibration signaltAfter the shaping treatment, it is represented as Dt＝10k+1×at+10k×|dt|；atRepresents a sign bit, if dtIs a negative number, then atThe value is 1; if d istIs a positive number, then atThe value is 2.
Further preferably, after the encrypted external sound wave vibration signal is decrypted, the decrypted data of the external sound wave vibration signal at each moment are respectively restored; wherein, the decryption data D at the t moment in the external sound wave vibration signaltThe restored signal data obtained after restoration is:
further preferably, the method for training the intrusion behavior judgment model includes: collecting external sound wave vibration signals at each position of a plurality of optical fibers when physical layer intrusion behaviors do not exist and different physical layer intrusion behaviors exist, and respectively extracting the characteristics of the external sound wave vibration signals to obtain a training set; inputting the training set into a machine learning model for training to obtain a pre-trained intrusion behavior judgment model; wherein, the physical layer intrusion behavior comprises fence cutting, optical cable treading, fence climbing and illegal border crossing.
Further preferably, the watermark verifying unit performs a data retransmission operation after determining that there is network transport layer intrusion and performing network transport layer intrusion alarm, specifically: the time T at which the comparison results are inconsistentnAnd its corresponding optical fiber position lnSending the signal to the sound wave detection unit, triggering the sound wave detection unit to resend the optical fiber position lnT in external sound wave vibration signalnSignal data of a time; the sound wave detection unit adopts a public key A to the optical fiber position lnT in external sound wave vibration signalnAfter signal data at a moment are encrypted, first encrypted signal data are obtained and sent to a terminal module; the terminal module decrypts the first encrypted signal data by using a private key A to obtain first decrypted signal data, further encrypts the first encrypted signal data by using a public key B to obtain second encrypted signal data, and sends the second encrypted signal data to the watermark verification unit; the watermark verification unit decrypts the second encrypted signal data by using a private key B to obtain second decrypted signal data; for TnDemodulating the backward Rayleigh scattering curve of the whole optical fiber at the moment to obtain the demodulated optical fiber position lnT in external sound wave vibration signalnSignal data of time, the optical fiber position l after demodulationnT in external sound wave vibration signalnComparing the signal data at the moment with the second decrypted signal data, and judging whether the signal data and the second decrypted signal data are consistent; if the first decryption signal is consistent with the second decryption signal, judging that the network transmission layer intrusion does not exist at the moment, and triggering the terminal module to enable the optical fiber position l in the second decryption signalnT in external sound wave vibration signalnReplacing the signal data at the moment with first decrypted signal data, then analyzing the intrusion activity, and ending the operation; otherwise, judging that the network transmission layer intrusion exists at present, carrying out network transmission layer intrusion alarm, and continuously executing data retransmission operation.
In a second aspect, the invention provides an intelligent online monitoring method for intrusion of the internet of things based on the intelligent online monitoring system for intrusion of the internet of things, which comprises the following steps:
s1, sending the detection light pulse signals with a certain time interval to the sensing optical fiber to be monitored by adopting the sound wave detection unit, acquiring backward Rayleigh scattering curves of the whole optical fiber at different moments, and sending the backward Rayleigh scattering curves to the watermark verification unit for storage; demodulating the backward Rayleigh scattering curve of the whole optical fiber at different moments to obtain external sound wave vibration signals at each position of the optical fiber, encrypting the external sound wave vibration signals by using a public key A in an asymmetric key A to obtain first encryption signals, and sending the first encryption signals to a terminal module;
s2, the control terminal module decrypts the first encrypted signal by using a private key A in the asymmetric key A to obtain a first decrypted signal, then further encrypts the first encrypted signal by using a public key B in the asymmetric key B to obtain a second encrypted signal, and sends the second encrypted signal to the watermark verification unit;
s3, controlling the watermark verification unit to decrypt the second encrypted signal by using a private key B in the asymmetric key B to obtain a second decrypted signal; demodulating the backward Rayleigh scattering curve of the whole optical fiber at different moments to obtain demodulated external sound wave vibration signals at each position of the optical fiber, comparing the demodulated external sound wave vibration signals at each position of the optical fiber with the second decryption signal, and judging whether the two signals are consistent; if the network transmission layer intrusion does not exist, judging that the network transmission layer intrusion does not exist, triggering the terminal module to analyze the intrusion activity, and turning to the step S4; otherwise, judging that the network transmission layer intrusion exists, alarming the network transmission layer intrusion, and ending the operation;
s4, after the terminal module is triggered, extracting the characteristics of external sound wave vibration signals at each position of the optical fiber, inputting the characteristics into a pre-trained intrusion behavior judgment model to judge whether a physical layer intrusion behavior exists or not, and if so, performing physical layer intrusion alarm;
wherein the second decrypted signal is characterized by: time domain features, frequency domain features, time-frequency features, and space-time features; the intrusion behavior judgment model is a machine learning model; asymmetric key B is different from asymmetric key a.
Generally, by the above technical solution conceived by the present invention, the following beneficial effects can be obtained:
1. the invention provides an intelligent online monitoring system and method for intrusion of the Internet of things. Based on this, the invention designs a watermark verification unit, which pre-stores the backward Rayleigh scattering curves of the whole optical fiber obtained by the sound wave detection unit at different times, and after receiving the encrypted signal sent by the terminal module, decrypting the external acoustic wave vibration signals to obtain the external acoustic wave vibration signals at the positions of the optical fiber after decryption, simultaneously obtaining the external acoustic wave vibration signals at the positions of the optical fiber after demodulation based on a pre-stored backward Rayleigh scattering curve, since it is known that the acoustic data information cannot reversely derive the corresponding backward rayleigh scattering curve, and the external acoustic vibration signals at the positions of the optical fiber after demodulation correspond to the originally acquired external acoustic vibration signals at the positions of the optical fiber, by comparing the external acoustic vibration signals at the positions of the optical fiber after decryption and demodulation, whether the network transmission layer between the sound wave detection unit and the terminal module is invaded or not can be judged; meanwhile, in order to prevent the network transmission layer between the terminal module and the watermark verification unit from being invaded, the invention designs two pairs of different asymmetric keys A and B, a public key A in the asymmetric key A is adopted to encrypt the external sound wave vibration signals at each position of the optical fiber collected by the sound wave detection unit and send the external sound wave vibration signals to the terminal module, the terminal module is decrypted based on the private key A, and the asymmetric key B is further adopted to encrypt the external sound wave vibration signals at each position of the optical fiber after decryption and send the external sound wave vibration signals to the watermark verification unit; even if the public key A and the public key B are intercepted, a hacker cannot acquire the private key and cannot acquire a signal decrypted by the terminal module, and the sound wave data which cannot be forged by the hacker is used for deceiving the watermark verification unit due to the fact that the asymmetric secret keys A and B are different, so that the safety of a network transmission layer between the terminal module and the watermark verification unit is guaranteed; the two designs complement each other and cooperate with each other to form a complete anti-intrusion system, so that the authenticity of the sound wave data is verified, and the safety is higher.
2. The intelligent online monitoring system and method for intrusion of the Internet of things, provided by the invention, take the fact that most external sound wave vibration signal data at each position of the optical fiber are decimal, and carry out integer processing on each signal data, so that the encryption is convenient and the calculation speed is accelerated.
3. The intelligent online monitoring system and method for intrusion of the internet of things provided by the invention have the advantages that after the existence of a network transmission layer is determined, the sound wave detection unit is triggered to resend the signal data corresponding to the moment when the comparison result is inconsistent, the authenticity of the resent signal is further judged, if the network transmission layer intrusion exists consistently, the alarm is continuously given, the data is resent repeatedly until the network transmission layer intrusion does not exist, the tampered signal data in the first decryption signal of the terminal module is replaced by the real data, and then whether the physical layer intrusion behavior exists is further judged, so that the whole process is more complete and safe.
Drawings
Fig. 1 is a schematic structural diagram of an intelligent online monitoring system for intrusion of the internet of things according to embodiment 1 of the present invention;
fig. 2 is a flowchart of intelligent online monitoring of intrusion of the internet of things according to embodiment 2 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Examples 1,
The optical cable is buried in the ground or hung on a wall to detect sound wave vibration signals generated by illegal intrusion, and the detected sound wave vibration signal data is transmitted to a terminal processing platform through a wireless transmission network to intelligently identify the intrusion event. When the sound wave vibration signal data are transmitted in the network, the data can face the threats of interception, tampering or counterfeiting, and the like, and a plurality of potential safety hazards are brought to the intelligent online monitoring system for the intrusion of the Internet of things. In order to solve the problems, the invention provides an intelligent online monitoring system for intrusion of the Internet of things;
specifically, an intelligent online monitoring system for intrusion of internet of things, as shown in fig. 1, includes: the system comprises a sound wave detection verification module and a terminal module which are positioned under the same network;
the acoustic wave detection verification module comprises an acoustic wave detection unit and a watermark verification unit; in this embodiment, the first port of the acoustic wave detection unit is connected to the first port of the watermark verification unit through a wired transmission network, the second port of the acoustic wave detection unit is connected to the first port of the terminal module through a wireless transmission network, and the second port of the watermark verification unit is connected to the second port of the terminal module through a wireless transmission network;
the intelligent online monitoring system for the intrusion of the Internet of things is used for detecting the situation that the sensing optical fiber in the distributed sensing optical cable is intruded; in this embodiment, the sub-modules are laid on the ground or hung on a wall.
The acoustic wave detection unit is used for sending a detection light pulse signal with a certain time interval to a sensing optical fiber to be monitored, acquiring a backward Rayleigh scattering curve of the whole optical fiber at different moments and sending the backward Rayleigh scattering curve to the watermark verification unit for storage; demodulating the backward Rayleigh scattering curves of the whole optical fiber at different moments to obtain external sound wave vibration signals at each position of the optical fiber, encrypting the external sound wave vibration signals by using a public key A in an asymmetric key A to obtain a first encrypted signal, and sending the first encrypted signal to a terminal module; in this embodiment, an arc tangent phase demodulation algorithm is used to demodulate the backward rayleigh scattering curve of the whole optical fiber at different times, so as to obtain external acoustic vibration signals at each position of the optical fiber. Since the external acoustic vibration signal data is obtained by the corresponding backward Rayleigh scattering curve through the arc tangent phase demodulation algorithm, and the value range of the external acoustic vibration signal data obtained through demodulation is (-pi, pi), before the external acoustic vibration signal is encrypted, the k bit after the decimal point of the external acoustic vibration signal data can be respectively reserved for the signal data at each moment according to a rounding methodEffective numbers are obtained, and signal data of external sound wave vibration signals at all times are subjected to integer processing, so that encryption is facilitated, and the calculation speed is increased; wherein, the signal data d of t moment in the external sound wave vibration signaltAfter the shaping treatment, it is represented as Dt＝10k+1×at+10k×|dt|；atDenotes the sign bit, if dtIs a negative number, then atThe value is 1; if d istIs a positive number, then atThe value is 2. It should be noted that k is a positive integer, and a value thereof is determined as needed, specifically after the calculation speed and the accuracy are balanced.
Specifically, a pair of asymmetric keys a is generated in a wireless transmission network between the acoustic wave detection unit and the terminal module, wherein the asymmetric keys a include a public key a and a private key a, the private key a is fixedly stored in the terminal module, and the public key a is sent to the acoustic wave detection unit through the wireless transmission network. To the signal data D of t moment in the external sound wave vibration signaltFor example, the public key a and the private key a are obtained as follows: first, a pair of different, sufficiently large prime numbers P is selectedA、QACalculate PAAnd QAProduct of (2) NAI.e. NA＝PA*QA(ii) a And ensure NASignal data D as large as possible at time tt. Then according to formula FA＝(PA-1)*(QA-1) calculating FA(ii) a Then at 1 and FABetween values is selected an integer EAAnd ensure EAAnd FAAre relatively prime. Calculating an integer GALet GAAnd EAProduct pair F ofATaking the remainder as 1, i.e. (E)A*GA)mod(FA) 1 is ═ 1; the public key A is therefore (N)A,EA) The private key A is (N)A,GA). Then, the public key A is used for respectively encrypting the external sound wave vibration signals at each position of the optical fiber to obtain first encrypted signal data C at the time tA_t＝(Dt^EA)mod(NA) (ii) a Finally, first encrypted signal data of each moment is obtained to obtain a first encrypted signal CA(ii) a At this time, CAFor the sound wave vibration signal ciphertext encrypted by the public key A, the first encrypted signal C is transmittedAAnd sending the data to the terminal module through a wireless transmission network.
The terminal module is used for decrypting the first encrypted signal by using a private key A in the asymmetric key A to obtain a first decrypted signal (an external sound wave vibration signal at each position of the optical fiber), further encrypting the first decrypted signal by using a public key B in the asymmetric key B to obtain a second encrypted signal, and sending the second encrypted signal to the watermark verification unit; wherein asymmetric key B is different from asymmetric key A;
in particular, for the first encrypted signal CADecrypting and respectively decrypting the first encrypted signal CADecrypting the encrypted signal data at each moment; to the first encrypted signal data C at the time tA_tFor example, the decryption process is as follows: first find the corresponding private key A (N)A,GA) And then the first encrypted signal data C is encrypted by the private key AA_tDecryption is carried out according to the following decryption formula: dt＝(CA_t^GA)mod(NA) To obtain signal data D at time ttThen the reshaped data DtThe restoration is carried out to obtain the restored signal data at the t moment in the external sound wave vibration signal as
When a istWhen 1 is equal, signal data d is restoredtIs a negative number; when a istWhen 2 or more, the signal data d is restoredtIs a positive number; and then obtaining external sound wave vibration signals at each position of the optical fiber.
Encrypting the external sound wave vibration signals at each position of the optical fiber by using a public key B in the asymmetric key B to obtain a second encrypted signal; the external sound wave vibration signal d is recovered at the moment t in the second encrypted signaltFor example, before encryption, the signal data D at time t in the external acoustic vibration signal is obtained by performing the integer conversion in the same manner as described abovetThen, encrypting; the encryption process is as follows: first of all, wireless transmission between a terminal module and a sonogram verification unitAnd generating a pair of asymmetric keys B in the network, wherein the asymmetric keys B comprise a public key B and a private key B, fixedly storing the private key B in the watermark verification unit, and sending the public key B to the terminal module through the wireless transmission network. The specific process for obtaining the public key B and the private key B is as follows: first, a pair of prime numbers P with sufficient size is selectedB、QBAnd let PB、QB，PA、QAThe four numbers are different from each other, and P is calculatedBAnd QBProduct of (2) NBI.e. NB＝PB*QBAnd ensure NBAs large as possible greater than Dt. Then according to formula FB＝(PB-1)*(QB-1) calculating FB(ii) a Then at 1 and FBSelecting a prime number E between the valuesBAnd ensure EBNot equal to EA. Calculating an integer GBLet GBAnd EBProduct pair F ofBTaking the remainder as 1, i.e. (E)B*GB)mod(FB) 1 is ═ 1; the public key B is therefore (N)B,EB) The private key B is (N)B,GB). Then, the public key B is utilized to recover the external sound wave vibration signal D at the time ttPerforming encryption processing to obtain second encrypted signal data C at time tB_t＝(Dt^EB)mod(NB) (ii) a Thereby obtaining a second encrypted signal CBAt this time, CBA second encrypted signal C is finally generated for the sound wave vibration signal ciphertext after the encryption processing of the public key BBAnd sending the information to a watermark verification unit through a wireless transmission network.
The watermark verification unit is used for decrypting the second encrypted signal by adopting a private key B in the asymmetric key B to obtain a second decrypted signal B1(external sound wave vibration signals at each position of the optical fiber after decryption); demodulating the backward Rayleigh scattering curve of the whole optical fiber at different times to obtain an external acoustic vibration signal B at each position of the demodulated optical fiber2Demodulating the external acoustic vibration signals B of the optical fiber at various positions2And a second decryption signal B1Comparing and judging whether the two are consistent; if the network transmission layer intrusion is consistent with the network transmission layer intrusion, judging that the network transmission layer intrusion does not exist, and triggering the terminal moduleCarrying out intrusion activity analysis; otherwise, judging that the network transmission layer intrusion exists, and alarming the network transmission layer intrusion;
in particular, for the second encrypted signal CBDecrypting and respectively decrypting the second encrypted signal CBDecrypting the encrypted signal data at each moment; to the second encrypted signal data C at time tB_tFor example, the decryption process is as follows: first find the corresponding private key B (N)B,GB) And then using private key B to pair cipher text acoustic wave data CB_tDecryption is carried out according to the following decryption formula: dt＝(CB_t^GB)mod(NB) (ii) a Then, restoring according to the restoration method; and finally obtaining external sound wave vibration signals at each position of the optical fiber after decryption. And demodulating the backward Rayleigh scattering curve of the whole optical fiber at different times by adopting an arc tangent phase demodulation algorithm to obtain external sound wave vibration signals at each position of the optical fiber.
It should be noted that, when comparing the demodulated external acoustic wave vibration signal at each position of the optical fiber with the decrypted external acoustic wave vibration signal at each position of the optical fiber, the demodulated external acoustic wave vibration signals at different positions of the optical fiber are respectively compared with the decrypted external acoustic wave vibration signal, and whether there is a time when the comparison results are inconsistent is determined.
The watermark verifying unit is used for verifying the authenticity of the sound wave data and preventing the data from being falsified or forged by hackers in a wireless transmission network. Because the refractive index distribution in the optical fiber is uneven, the positions of Rayleigh scattering points in the optical fiber and the intensity of Rayleigh scattering optical signals are randomly distributed, each detection optical pulse signal corresponds to a specific backward Rayleigh scattering curve, the acoustic wave vibration signal data can be solved through the backward Rayleigh scattering curve, but the acoustic wave vibration signal data cannot be reversely deduced to form the backward Rayleigh scattering curve, and therefore each backward Rayleigh scattering curve can be regarded as a watermark label for verifying the authenticity of the corresponding acoustic wave vibration signal data. In the comparison process, firstly, a backward Rayleigh scattering curve signal at the corresponding moment is found out according to the time information, and then, the backward Rayleigh scattering curve corresponding to the moment is utilizedDemodulating corresponding sound wave vibration signal data, and then reserving k-bit effective digits after decimal point of the sound wave vibration signal data according to a rounding method to obtain data B2Then B is1And B2Performing correspondence matching if B1Is equal to B2Judging the sound wave vibration signal data as real data, if B1Is not equal to B2And judging that the sound wave vibration signal data are tampered, and sending out network transmission layer intrusion alarm.
The terminal module is also used for extracting the characteristics of the second decryption signal when being triggered and inputting the characteristics into a pre-trained intrusion behavior judgment model so as to judge whether a physical layer intrusion behavior exists or not, and if so, carrying out physical layer intrusion alarm; wherein the second decrypted signal is characterized by: time domain features, frequency domain features, time-frequency features, and space-time features; the intrusion behavior judgment model is a machine learning model; the training method of the intrusion behavior judgment model comprises the following steps: collecting external sound wave vibration signals at each position of a plurality of optical fibers when physical layer intrusion behaviors do not exist and different physical layer intrusion behaviors exist, and respectively extracting the characteristics of the external sound wave vibration signals to obtain a training set; inputting the training set into a machine learning model for training to obtain a pre-trained intrusion behavior judgment model; wherein, the physical layer intrusion behavior comprises fence cutting, optical cable treading, fence climbing and illegal border crossing. The above features also include time domain features, frequency domain features, time-frequency domain features, and space-time features.
In this embodiment, the time domain features include: signal time domain bottom noise, signal time domain peak characteristics and short time window energy characteristics; specifically, the method for extracting the signal time domain background noise comprises the following steps: firstly, setting a percentile parameter p, then, taking an absolute value of external sound wave vibration signal data, sequencing the data from small to large, and calculating a corresponding accumulated percentile; the signal data corresponding to the percentile point parameter p is the time domain background noise value T of the external sound wave vibration signalh. Further, the signal time domain peak characteristics include: number of time domain peaks NpTime domain peak-to-average value P and peak-to-noise ratio PSNR. The extraction method of the signal time domain peak value characteristics comprises the following steps: firstly, the peak searching threshold value P is setthAnd minimum distance between two peaksP separationdCarrying out peak searching operation on the external sound wave vibration signal by utilizing a peak searching function to finally obtain the number N of time domain peaks of the external sound wave vibration signalpAnd a peak value P corresponding to each peakN. Then the peak value P corresponding to each peakNAnd summing, and averaging to obtain a time domain peak mean value P of the external sound wave vibration signal. Finally, calculating the time domain peak-to-average value P and the upper time domain background noise threshold value ThThe ratio of the peak to the noise ratio P of the external sound wave vibration signal is obtainedSNR. Further, the method for extracting the short-time window energy features comprises the following steps: setting the width W of a sliding window, setting the step length proportion r of the sliding window, sequentially sliding the sliding window from left to right to obtain waveform data of internal and external sound wave vibration signals of each sliding window, solving the sum of the square of the amplitude of all the waveform data in each sliding window as the energy value of each window, and finding the maximum window value as the energy maximum value T of the short time window of the external sound wave vibration signalsmCharacteristic, i.e. short time window energy characteristic.
The frequency domain features include: frequency band F corresponding to the maximum frequency domain windowbAnd the maximum value F of the signal frequency domain windowmax(ii) a The extraction method in the embodiment comprises the following steps: firstly setting the width W of a frequency spectrum window and the step1 of a sliding window (generally, W is 20Hz, step is 1Hz), then carrying out Fourier transform on an external sound wave vibration signal to obtain a signal frequency spectrum, calculating the total energy value in each sliding window of the signal frequency spectrum, comparing the energy value corresponding to each sliding window, and finding out the maximum energy value FmaxAnd finding out the frequency band F corresponding to the sliding window of the maximum energy valueb。
Time-frequency domain characteristics: firstly, setting a short-time Fourier window length W, a time step2 and an FFT point number n, and then carrying out short-time Fourier transform on an external sound wave vibration signal to obtain a time-frequency two-dimensional graph S (t, f) of the signal; the abscissa of the time-frequency two-dimensional graph represents time information, and the ordinate represents frequency information. Obtaining the frequency band F corresponding to the maximum frequency domain window of the external sound wave vibration signal by adopting the frequency domain characteristic extraction methodbAnd the energy curve distribution of the external sound wave vibration signal along with the time change is further calculated as the signal time-frequency energy integral frequency band:
wherein, Fb＝fH-fL，fHIs FbUpper limit frequency of fLIs FbThe lower limit frequency of (3);
selecting the time-frequency energy level number m, and then finding out ET-fMaximum value E of (t)mThen the energy interval of the ith energy level is [ (i-1) × Em/m，i*Em/m]. Finally, counting the corresponding points in each energy level as a time-frequency characteristic matrix M of the signalT-FReal-time frequency domain characterization.
Space-time characteristics: the method comprises the steps of quantifying detected external sound wave vibration signals through a sound wave inversion technology, and drawing a two-dimensional distribution image according to the sequence of time and space to obtain sound wave signal characteristics, namely real-time space characteristics, of a monitored object at different positions and at different moments.
Further, the watermark verification unit determines that there is network transport layer intrusion, and performs data retransmission operation after performing network transport layer intrusion alarm, specifically: the time T at which the comparison results are inconsistentnAnd its corresponding optical fiber position lnSending the signal to the sound wave detection unit, triggering the sound wave detection unit to resend the optical fiber position lnT in external sound wave vibration signalnSignal data of a time; the sound wave detection unit adopts a public key A to the optical fiber position lnT in external sound wave vibration signalnAfter signal data at a moment are encrypted, first encrypted signal data are obtained and sent to a terminal module; the terminal module decrypts the first encrypted signal data by using a private key A to obtain first decrypted signal data, further encrypts the first encrypted signal data by using a public key B to obtain second encrypted signal data, and sends the second encrypted signal data to the watermark verification unit; the watermark verification unit decrypts the second encrypted signal data by using a private key B to obtain second decrypted signal data; for TnDemodulating the backward Rayleigh scattering curve of the whole optical fiber at the moment to obtain the demodulated optical fiber position lnT in external sound wave vibration signalnSignal data of time of day, will solveAdjusted fiber position lnT in external sound wave vibration signalnComparing the signal data at the moment with the second decrypted signal data, and judging whether the signal data and the second decrypted signal data are consistent; if the first decryption signal is consistent with the second decryption signal, judging that the network transmission layer intrusion does not exist at the moment, and triggering the terminal module to enable the optical fiber position l in the second decryption signalnT in external sound wave vibration signalnReplacing the signal data at the moment with first decrypted signal data, then analyzing the intrusion activity, and ending the operation; otherwise, judging that the network transmission layer intrusion exists at present, carrying out network transmission layer intrusion alarm, and continuously executing data retransmission operation.
The intelligent foreign matter intrusion detection system is suitable for intelligent foreign matter intrusion identification and monitoring in various special scenes such as transformer substations, military bases, airports, border lines and the like, and has the characteristics of high instantaneity, intelligence, high safety protection of sensing data and the like.
Examples 2,
An internet of things intrusion intelligent online monitoring method based on the internet of things intrusion intelligent online monitoring system is shown in fig. 2 and comprises the following steps:
s1, sending the detection light pulse signals with a certain time interval to the sensing optical fiber to be monitored by adopting the sound wave detection unit, acquiring backward Rayleigh scattering curves of the whole optical fiber at different moments, and sending the backward Rayleigh scattering curves to the watermark verification unit for storage; demodulating the backward Rayleigh scattering curve of the whole optical fiber at different times to obtain external sound wave vibration signals at each position of the optical fiber, and encrypting by adopting a public key A in an asymmetric key A to obtain a first encrypted signal CAAnd sending the data to the control terminal module;
s2, the control terminal module adopts the private key A in the asymmetric key A to encrypt the first encrypted signal CADecrypting to obtain a first decrypted signal (external sound wave vibration signal at each position of the optical fiber), and encrypting the first decrypted signal by using a public key B in the asymmetric key B to obtain a second encrypted signal CBAnd sending the data to a watermark verification unit;
s3, controlling the watermark verification unit to adopt the private key B in the asymmetric key B to encrypt the second encrypted signal CBDecrypting to obtain a second decrypted signal B1(external sound wave vibration signals at each position of the optical fiber after decryption); demodulating the backward Rayleigh scattering curve of the whole optical fiber at different times to obtain an external acoustic vibration signal B at each position of the demodulated optical fiber2Demodulating the external sound wave vibration signal B at each position of the optical fiber2And a second decryption signal B1Comparing and judging whether the two are consistent; if the network transmission layer intrusion does not exist, judging that the network transmission layer intrusion does not exist, triggering the terminal module to analyze the intrusion activity, and turning to the step S4; otherwise, judging that the network transmission layer intrusion exists, alarming the network transmission layer intrusion, and ending the operation;
s4, after the terminal module is triggered, extracting the characteristics of external sound wave vibration signals at each position of the optical fiber, inputting the characteristics into a pre-trained intrusion behavior judgment model to judge whether a physical layer intrusion behavior exists or not, and if so, performing physical layer intrusion alarm;
wherein the second decrypted signal is characterized by: time domain features, frequency domain features, time-frequency features, and time-space features; the intrusion behavior judgment model is a machine learning model; asymmetric key B is different from asymmetric key a.
The related technical features are the same as those of embodiment 1, and are not described herein.
It will be understood by those skilled in the art that the foregoing is only an exemplary embodiment of the present invention, and is not intended to limit the invention to the particular forms disclosed, since various modifications, substitutions and improvements within the spirit and scope of the invention are possible and within the scope of the appended claims.
Claims (7)
1. The utility model provides an intelligent online monitoring system of thing networking invasion which characterized in that includes: the system comprises a sound wave detection verification module and a terminal module which are positioned under the same network; the acoustic wave detection verification module comprises an acoustic wave detection unit and a watermark verification unit;
the acoustic wave detection unit is used for sending a detection light pulse signal with a certain time interval to a sensing optical fiber to be monitored, acquiring a backward Rayleigh scattering curve of the whole optical fiber at different moments and sending the backward Rayleigh scattering curve to the watermark verification unit for storage; demodulating the backward Rayleigh scattering curve of the whole optical fiber at different times to obtain external sound wave vibration signals at each position of the optical fiber, encrypting by adopting a public key A in an asymmetric key A to obtain a first encrypted signal, and sending the first encrypted signal to the terminal module;
the terminal module is used for decrypting the first encrypted signal by using a private key A in an asymmetric key A to obtain a first decrypted signal, further encrypting the first decrypted signal by using a public key B in an asymmetric key B to obtain a second encrypted signal, and sending the second encrypted signal to the watermark verification unit;
the watermark verification unit is used for decrypting the second encrypted signal by adopting a private key B in the asymmetric key B to obtain a second decrypted signal; demodulating the backward Rayleigh scattering curve of the whole optical fiber at different times to obtain demodulated external sound wave vibration signals at each position of the optical fiber, comparing the demodulated external sound wave vibration signals at each position of the optical fiber with the second decryption signal, and judging whether the two signals are consistent; if the network transmission layer intrusion is consistent with the network transmission layer intrusion, triggering the terminal module to analyze the intrusion activity; otherwise, judging that the network transmission layer intrusion exists, and alarming the network transmission layer intrusion;
the terminal module is further used for extracting the characteristics of the second decryption signal when the second decryption signal is triggered, inputting the characteristics into a pre-trained intrusion behavior judgment model to judge whether a physical layer intrusion behavior exists or not, and if the intrusion behavior exists, giving an alarm on physical layer intrusion;
wherein the second decrypted signal is characterized by: time domain features, frequency domain features, time-frequency features, and time-space features; the intrusion behavior judgment model is a machine learning model; the asymmetric key B is different from the asymmetric key a.
2. The intelligent online monitoring system for intrusion of the internet of things according to claim 1, wherein an arctangent phase demodulation algorithm is adopted to demodulate a backward Rayleigh scattering curve of the whole optical fiber at different times to obtain external sound wave vibration signals at each position of the optical fiber.
3. The intelligent online monitoring system for intrusion in the internet of things according to claim 1, wherein before the external sound wave vibration signal is encrypted, k-bit effective numbers after decimal point of the signal data at each moment are respectively reserved according to a rounding method, and the signal data at each moment of the external sound wave vibration signal are shaped; wherein, the signal data d of t moment in the external sound wave vibration signaltAfter the shaping treatment, it is represented as Dt＝10k+1×at+10k×|dt|；atDenotes the sign bit, if dtIs a negative number, then atThe value is 1; if d istIs a positive number, then atThe value is 2.
4. The intelligent online monitoring system for intrusion of the internet of things according to claim 3, wherein after the encrypted external sound wave vibration signal is decrypted, the decrypted data of the external sound wave vibration signal at each moment are respectively restored; wherein, the decryption data D at the time t in the external sound wave vibration signaltThe restored signal data obtained after restoration is:
5. the intelligent online intrusion monitoring system according to claim 1, wherein the training method for the intrusion behavior judgment model comprises the following steps: collecting external sound wave vibration signals at each position of a plurality of optical fibers when physical layer intrusion behaviors do not exist and different physical layer intrusion behaviors exist, and respectively extracting the characteristics of the external sound wave vibration signals to obtain a training set; inputting the training set into a machine learning model for training to obtain a pre-trained intrusion behavior judgment model; wherein, the physical layer intrusion behavior comprises fence cutting, optical cable treading, fence climbing and illegal border crossing.
6. The system according to any one of claims 1 to 5, wherein the watermark verification unit performs a data retransmission operation after determining that there is a network transport layer intrusion and performing a network transport layer intrusion alarm, specifically: the time T at which the comparison results are inconsistentnAnd its corresponding optical fiber position lnSending the data to the sound wave detection unit and triggering the sound wave detection unit to resend the optical fiber position lnT in external sound wave vibration signalnSignal data of a time; the sound wave detection unit adopts a public key A to the optical fiber position lnT in external sound wave vibration signalnAfter signal data at a moment are encrypted, first encrypted signal data are obtained and sent to the terminal module; the terminal module decrypts the first encrypted signal data by using a private key A to obtain first decrypted signal data, further encrypts by using a public key B to obtain second encrypted signal data, and sends the second encrypted signal data to the watermark verification unit; the watermark verification unit decrypts the second encrypted signal data by using a private key B to obtain second decrypted signal data; for TnDemodulating the backward Rayleigh scattering curve of the whole optical fiber at the moment to obtain the demodulated optical fiber position lnT in external sound wave vibration signalnSignal data of time, the optical fiber position l after demodulationnT in external sound wave vibration signalnComparing the signal data at the moment with the second decrypted signal data, and judging whether the signal data and the second decrypted signal data are consistent; if the first decryption signal is consistent with the second decryption signal, judging that the network transmission layer intrusion does not exist at the moment, and triggering the terminal module to enable the optical fiber position l in the second decryption signalnT in external sound wave vibration signalnReplacing the signal data at the moment with the first decrypted signal data, and then analyzing the intrusion activity, and ending the operation; otherwise, judging that the network transmission layer intrusion exists at present, carrying out network transmission layer intrusion alarm, and continuously executing data retransmission operation.
7. An intelligent online monitoring method for intrusion of the internet of things based on the intelligent online monitoring system for intrusion of the internet of things as claimed in any one of claims 1 to 6, comprising the following steps:
s1, sending the detection light pulse signals with a certain time interval to the sensing optical fiber to be monitored by adopting the sound wave detection unit, acquiring backward Rayleigh scattering curves of the whole optical fiber at different moments, and sending the backward Rayleigh scattering curves to the watermark verification unit for storage; demodulating the backward Rayleigh scattering curve of the whole optical fiber at different moments to obtain external sound wave vibration signals at each position of the optical fiber, encrypting the external sound wave vibration signals by using a public key A in an asymmetric key A to obtain first encryption signals, and sending the first encryption signals to a terminal module;
s2, the control terminal module decrypts the first encrypted signal by using a private key A in the asymmetric key A to obtain a first decrypted signal, then further encrypts by using a public key B in the asymmetric key B to obtain a second encrypted signal, and sends the second encrypted signal to the watermark verification unit;
s3, controlling the watermark verification unit to decrypt the second encrypted signal by using a private key B in the asymmetric key B to obtain a second decrypted signal; demodulating the backward Rayleigh scattering curve of the whole optical fiber at different times to obtain demodulated external sound wave vibration signals at each position of the optical fiber, comparing the demodulated external sound wave vibration signals at each position of the optical fiber with the second decryption signal, and judging whether the two signals are consistent; if the network transmission layer intrusion does not exist, judging that the network transmission layer intrusion does not exist, triggering the terminal module to analyze the intrusion activity, and turning to the step S4; otherwise, judging that the network transmission layer intrusion exists, alarming the network transmission layer intrusion, and ending the operation;
s4, after the terminal module is triggered, extracting the characteristics of external sound wave vibration signals at each position of the optical fiber, inputting the characteristics into a pre-trained intrusion behavior judgment model to judge whether a physical layer intrusion behavior exists or not, and if so, performing physical layer intrusion alarm;
wherein the second decrypted signal is characterized by: time domain features, frequency domain features, time-frequency features, and space-time features; the intrusion behavior judgment model is a machine learning model; the asymmetric key B is different from the asymmetric key a.
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