CN111343639B - Ultra-dense network attack prediction method combining thermal mode with self-adaptive jump algorithm - Google Patents
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Abstract
The invention discloses a method for predicting ultra-dense network attack by combining a hot mode with a self-adaptive jump algorithm, which comprises the following steps: s1, constructing a thermal mode analysis system model; s2: in the thermal model analysis system model, a mobile user equipment u is adopted i The covered distance is used as a standard for tracking energy patterns for the mobile user equipment u i After comparing all values of the whole track of (a) drawing an energy pattern; s3, tracking target mobile user equipment u i Determining the energy pattern of the mobile user equipment u i The maximum energy or power dissipation point, finding the least safe possible location; s4: the network self-adaptive hopping algorithm is introduced on the basis of the thermal mode analysis, the selection of the network hopping mechanism is known by adopting a network threat sensing and hopping strategy mode, and a possible low-security area can be accurately found in an ultra-dense network environment; s5: the required protection mechanism is enabled in the vicinity of the least secure possible location. The prediction method can more accurately find the possible low-security area in the ultra-dense network environment.
Description
Technical Field
The invention belongs to the field of network security, and particularly relates to an ultra-dense network attack prediction method based on a thermal mode analysis combined with a network self-adaptive hopping algorithm.
Background
With the explosive growth of mobile User Equipment (UE), densification of wireless networks has emerged as an alternative method to meet 1000 times the network capacity and high Quality-of-service (QoS) requirements. Physical layer security (physical layer security, PLS) is of general interest to suppress emerging security challenges through next generation wireless communications [1-5] . This isIn addition, PLS technology has proven to be a complement to traditional encryption technology, which places an irrelevant computational burden on low power devices. Ultra dense networks also ensure that connections with high speed (or mobile) users are supported, with a maximum speed of 500 km/h. All these benefits come at a cost and tend to experience a sudden rise in handoff rate due to the mobile user (low mobility or high mobility) installing a low power and low coverage access point (or Base station, BS). As the close proximity between the user and the small cell BS results in increased handoff rates and overhead, the confidentiality and integrity of the mobile user is compromised. The existing policies protecting mobile users cannot be directly applied to ultra-dense networks, and appropriate polishing and redesign of existing schemes and algorithms should be performed to protect UEs in ultra-dense networks. Therefore, the research on the ultra-dense network attack prediction method has good practical significance and practical value.
Many professionals and scholars at home and abroad have conducted intensive research around the ultra-dense network attack prediction method, and the document Secrecy Rate Analysis in Multi-Tier Heterogeneous Networks Over Generalized Fading Model researches the influence on the capacity of UE (user equipment) along with the increase of the density of eavesdroppers, and concludes that positive confidentiality can still be realized in the ultra-dense network. The security capability of heterogeneous networks is checked in literature, interference coordination method and performance analysis in ultra dense networks, without keeping track of BS (base station) and UE densities. However, none of these works treat energy consumption as a weapon that tracks the location of a user's attack. The literature "mobile target defense technology based on network attack surface adaptive conversion" proposes a closed expression approximating SOP for checking privacy interruption probability (secrecy outage probability, SOP). The document "support vector machine based cyber attack situation prediction technology research" proposes a secure transmission in which a transmitter is loaded with a plurality of antennas for millimeter wave transmission, which have partial information about an eavesdropper. Document A survey on ultra-dense network and emerging technologies: security challenges and possible solutions performs physical layer message authentication by comparing channel estimates with predefined estimates of the signal. However, in the existing method, energy consumption is not used as a weapon for tracking the attack position of the user, and the influence of an eavesdropper on the performance of the mobile user is not checked, and the intermediate node introduced in the existing method can bring unnecessary burden to the network performance of the ultra-dense network.
Disclosure of Invention
Aiming at the technical problems, the invention provides an ultra-dense network attack prediction method based on a thermal mode analysis and a network self-adaptive jump algorithm, which can more accurately find a possible low-security area in an ultra-dense network environment.
The technical scheme adopted by the invention is as follows:
the ultra-dense network attack prediction method combining the thermal mode with the self-adaptive jump algorithm comprises the following steps:
s1, constructing a thermal mode analysis system model:
s2: in the thermal model analysis system model, a mobile user equipment u is adopted i The covered distance is used as a standard for tracking energy patterns for the mobile user equipment u i After comparing all values of the whole track of (a) drawing an energy pattern;
s3, tracking target mobile user equipment u i Determining the energy pattern of the mobile user equipment u i The maximum energy or power dissipation point, finding the least safe possible location;
s4: the network self-adaptive hopping algorithm is introduced on the basis of the thermal mode analysis, the selection of the network hopping mechanism is known by adopting a network threat sensing and hopping strategy mode, and a possible low-security area can be accurately found in an ultra-dense network environment;
s5: the required protection mechanism is enabled in the vicinity of the least secure possible location.
Preferably, in step S1, the thermal pattern analysis system model includes different stages of wireless channel gain, transmission, power consumption and energy consumption, and is specifically divided into a wireless channel model, a transmission model, a power consumption model and an energy consumption model, and the area that the mobile user may attack is determined by tracking the footprint of each stage of the thermal pattern,
wireless channel model-wireless channel gain is a combination of small-scale fading and large-scale fading, for a mobile user equipment, small-scale coefficients appear due to the time-varying nature of the channel, and the mobile device experiences doppler shift as it moves, a two-segment path loss model of the actual 3GPP cooperative protocol is employed, which model depends on line-of-sight and line-of-sight probabilities, and then the average path loss between user equipment i and base station k depends on the following functions and is given by:
wherein ,ρL and ρNL Sight and no-path loss, alpha, respectively representing reference distance L and αNL Respectively representing the line-of-sight and non-line-of-sight loss indexes;
representing the distance p between the kth serving base station and the ith user in position; loS Prob represents line-of-sight probability; NLoS Prob represents the probability of no line of sight; pr (Pr) L Representing channel fading coefficients;
The line-of-sight probability is divided into two parts, as follows:
wherein ,γ1 ,γ 2 And d' represents the shaping parameters, ensure
Is a continuous nature of (2); />
Representing the distance p between the kth serving base station and the ith user in position;
transmission model: in the transmission model, the total transmit power P of the kth serving base station tx Distribution ofAmong N user equipments, it is composed of
Indicating that in the presence of an eavesdropper E, the user device u i A signal received from the serving base station k at the p-th position; the following equation gives:
wherein ,
is user equipment u i At p and N i The sum of the interference powers collected there, the displayed white gaussian noise has zero mean and +.>
Variance; />
Indicating the direction from the jth neighboring base station to user equipment u in ultra dense network i The power of the transmission; />
Representing the j-th neighbor base station and user equipment u i Channel gain between; />
Representing the kth serving base station and user equipment u i Channel gain between; n (N) i Representing the interference channel of the i-th user;
thus, the achievable rate
Can be expressed as:
wherein ,λk Representing the mode/state of the base station, i.e. lambda when the base station is in sleep mode i =0, and λ when the base station is in active mode i ＝1；B i Representing the bandwidth allocated to the ith user (i e k);
When the density of the user equipment falls below the standard limit or the base station is out of service for a long period of time, the base station is put into sleep mode, lambda k Depending on the base station sleep (lambda k =0) or an awake state (λ k Operation mode of=1);
secret volume S c Represented as from source to destination u i The number of bits successfully transmitted, which is not intercepted by an eavesdropper (E), gives the following formula:
wherein ,
representing the transmission rate between the kth serving base station and the eavesdropper E; for secure transmissions in ultra-dense networks, S c Is positive (S) c > 0) and greater than the desired threshold (S c ＞S c,th ) In ultra dense networks, the non-zero secret capacity probability is very high, typically greater than 0.95, i.e
Pr(S c ＞0)＞0.95 (9)
Thus, in order to detect an attack on a region, the following conditions need to be satisfied and are given by the following equation:
the total power consumed may be given by the following formula:
wherein ,ηEE Is the number of bits per joule, in ultra dense networks, the higher the density of user equipment with increased power consumption, resulting in reduced performance;
representing the capacity of a kth BS of a user i photographed at a reference distance in bps; />
Representing the power consumed at the reference distance by the capacity of the kth BS of the user i photographed at the reference distance; / >
Represents the j-th neighbor BS and u i A transmission rate therebetween; p (P) i j Indicating the power consumption of the ith user at the jth neighbor BS;
electric power consumption model: for finding mobile user equipment u i The highest point of energy dissipation, which depends on channel parameters, base station transmitted power and user equipment u i The power consumed, the base station for the total power consumption of a particular user equipment, is a combination of the power allocated by the base station and the total static power, and the power consumption of base station k can be expressed as:
wherein ,Pw and Ps Representing the static power consumed by each antenna during active and sleep modes, n A Indicating the number of antenna elements on the base station,
indicating the direction from the jth neighboring base station to user equipment u in ultra dense network i The power of the transmission; lambda (lambda) k Representing the mode/state of the base station, i.e. lambda when the base station is in sleep mode i =0, and λ when the base station is in active mode i ＝1；
The specific activation probability base station k can be expressed as follows:
wherein ,
defined as small cell base station density lambda s And user equipment density lambda u Is a ratio of (2); p (P) a Representing the transmit power of network node a;
energy consumption model: the energy consumption of each device or node is to transmit a bit of information E p Energy E consumed by the RF assembly as a function of the energy required RF In the switching decision and execution stage E HO The overhead incurred during this period, and can be expressed as:
E t ＝E p +E HO +E RF (14)
wherein ,Et Representing the energy required during a network handover from a service to a target base station, E t Divergent according to the service type and the target base station;
representing the energy consumed by the user for two BSs at time t; p (P) fail Representing a probability of handover failure obtained in the process when a user switches his network; />
Representing the energy consumed by two base stations when a handover failure occurs;
in wireless communication, the energy consumption of each device depends on the transmission time and delay, including the transmission delay, propagation delay T P Processing delay and handoffDelay, transmission delay per user equipment k
Can be expressed mathematically as:
wherein ,ns and nb Representing the number of symbols and the data rate from the base station to the user, respectively, t in bps/Hz h Representing a time span for handover information collection and preparation;
wherein the switching delay is due to an additional processing load
The delay caused and the signal exchanged during the process, i.e. +.>
Thus, the total energy consumption per device can be expressed as:
wherein ,
and />
Representing the respective dependent parameters E p and EHO ；T p Representing propagation delay; p (P) k Representing the transmission power of the kth serving BS; />
Representing the kth serving base station and user equipment u i Channel gain between; n is n s and nb Representing the number of symbols and data rate, respectively, from base station to user, in bps/HzUnits of (3).
Preferably, in step S2, firstly, a serving base station is selected from all active base stations, a mobile user equipment is found, and then, after comparing all values of the entire trajectory of the mobile user equipment, an energy pattern is drawn, which specifically includes:
the mobile user equipment tends to change its position at each instant of time t, where (X 1 ,Y 1 ) Indicated at t 1 Is located at the position of the mobile user equipment, is set (X p ,Y p) and (Xp-1 ,Y p-1 ) Representing the current and previous positions of the mobile user equipment, respectively, to be mobile user equipment u i At (x=x p ,Y＝Y p ) Energy consumption E at location p And (x=x p-1 ,Y＝Y p-1 ) Energy consumption E at location p-1 Comparing, and so on, to formulate a movement UEu i Is set in the energy mode of (a);
mobile user equipment u i Is defined as the sum of the total power consumption between two successive positions at any time interval T, given by:
where T is the time at which power consumption is observed,
depending on the mobile user equipment u i Is a separation between p and p-1; p (P) s Respectively representing the static power consumed by each antenna during sleep mode; p (P) k Representing the transmission power of the kth serving BS;
representing the transmission power between the kth serving BS and the ith user.
Preferably, in step S4, the network adaptive hopping algorithm mainly includes three parts of network threat sensing, hopping policy generation and hopping implementation deployment, and the network hopping is implemented by changing the system configuration and state of both communication parties in a pseudo-random manner, so as to continuously and dynamically transfer the network attack surface of the protected system, thereby trapping, confusing and confusing the detection of attackers, improving the utilization difficulty of vulnerabilities and backdoors, increasing the difficulty and cost of attack, achieving the purpose of guaranteeing the security of the target system.
Compared with the prior art, the invention has the beneficial effects that: the invention combines the thermal mode analysis and the network self-adaptive jump algorithm, considers the safety problem related to the switching of the mobile user equipment in the ultra-dense network, and tracks the mobile user equipment u i Determining the energy pattern of the mobile user equipment u i The maximum energy or power dissipation point, finding the least secure possible location, enabling the required protection mechanism in the vicinity of the least secure possible location, instead of using the protection strategy in the whole transmission cycle, thus not causing too much interference to the transmission process; the prediction method provided by the invention has the advantages of shorter time consumption, better effect and capability of finding out a possible low-security area in an ultra-dense network environment more accurately.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an ultra-dense network attack prediction method based on a thermal pattern analysis combined with a network adaptive hopping algorithm according to an embodiment of the present invention;
FIG. 2 is a real-time thermal profile of a possible highly sensitive region in an SMMU (virtualized mobile platform);
FIG. 3 is a block diagram of a network adaptive hopping architecture;
fig. 4 shows the energy efficiency of the base station 1 and the base station 2 according to the distance in the linear path mode;
fig. 5 shows the energy efficiency of the base station 1 and the base station 2 as a function of distance in the random path mode;
fig. 6 shows the spectrum efficiency of the base station 1 and the base station 2 as a function of distance in the straight path mode;
fig. 7 shows the spectrum efficiency of the base station 1 and the base station 2 as a function of distance in the random path mode;
FIG. 8 is a graph of capacity as a function of distance with or without disturbances in a straight track scene;
fig. 9 shows the capacity of base station 1 and base station 2 in a random track scenario as a function of distance.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention specifically discloses an ultra-dense network attack prediction method based on thermal mode analysis and a network self-adaptive hopping algorithm, which comprises the following steps:
s1, constructing a thermal mode analysis system model:
s2: in the thermal model analysis system model, a mobile user equipment u is adopted i The covered distance is used as a standard for tracking energy patterns for the mobile user equipment u i After comparing all values of the whole track of (a) drawing an energy pattern;
s3, tracking target mobile user equipment u i Determining the energy pattern of the mobile user equipment u i The maximum energy or power dissipation point, finding the least safe possible location;
s4: the network self-adaptive hopping algorithm is introduced on the basis of the thermal mode analysis, the selection of the network hopping mechanism is known by adopting a network threat sensing and hopping strategy mode, and a possible low-security area can be accurately found in an ultra-dense network environment;
S5: the required protection mechanism is enabled in the vicinity of the least secure possible location.
The thermal pattern analysis system model in step S1 comprises
Radio channel model radio channel gain is a combination of small-scale fading and large-scale fading for mobile user equipment u i The small scale coefficients occur due to the time-varying nature of the channel and mobile device u i As the mobile experiences doppler shift, an actual 3GPP two-segment path loss model is employed that depends on line-of-sight and line-of-sight probabilities, and then the average path loss between user equipment i and base station k depends on the following function and is given by:
wherein ,ρL and ρNL Sight and no-path loss, alpha, respectively representing reference distance L and αNL Respectively representing the line-of-sight and non-line-of-sight loss indexes; pr (Pr) L Representing channel fading coefficients;
the line-of-sight probability is divided into two parts, as follows:
wherein ,γ1 ,γ 2 And d' represents the shaping parameters, wherein it is ensured that
Is a continuous nature of (2);
after 5G and beyond, signal transmission occurs between the base station and the equipment of the 6GHz band, and the channel conditions of the equipment operating in the above frequency range are unexpectedly different due to the small distance between the user and the service base station, and the mm band of outdoor communication is [ (for a cell radius of 200 m) >28 GHz) the attenuation loss of atmospheric absorption is as low as 0.1dB. Small scale fading occurs due to multipath components or movement of users (or serving base stations), however, by taking indoor and outdoor conditions, tests have been conducted to test multipath delay spread by using feedhorns at the transmitter and receiver sides, root mean square delay spread (τ) for both line-of-sight and line-of-sight scenarios s ) Less than 1.5ns and 3.1ns. By using different pairs of antennas s After analysis, it can be concluded that τ s Always less than 6ns. The omnidirectional antenna is more suitable for time dispersion analysis at the receiver side, and the bandwidth B -1 Reciprocal of =5.55 μs greater than τ s I.e. (τ) s ＜＜B -1 )。
Transmission model: in the transmission model, the total transmit power P of the kth serving base station tx Distributed among N user equipments, which are composed of
Indicating that in the presence of an eavesdropper E, the user device u i A signal received from the serving base station k at the p-th position; the following equation gives:
wherein ,
is user equipment u i At p and N i The sum of the interference powers collected there shows that AWGN has zero mean and +.>
Variance; n (N) i Representing the interference channel of the i-th user;
thus, the achievable rate
Can be expressed as:
when the density of the user equipment falls below the standard limit or the base station is out of service for a long period of time, the base station is put into sleep mode, lambda k Depending on the base station sleep (lambda k =0) or an awake state (λ k Operation mode of=1);
secret volume S c Represented as from source to destination u i The number of bits successfully transmitted, which is not intercepted by an eavesdropper (E), gives the following formula:
wherein ,
representing the transmission rate between the kth serving BS and the eavesdropper E; for secure transmissions in ultra-dense networks, S c Is positive (S) c > 0) and greater than the desired threshold (S c ＞S c,th ) In ultra dense networks, the non-zero secret capacity probability is very high, typically greater than 0.95, i.e
Pr(S c ＞0)＞0.95 (9)
Thus, in order to detect an attack on a region, the following conditions need to be satisfied and are given by the following equation:
the total power consumed may be given by the following formula:
wherein ,ηEE Is the number of bits per joule, in ultra dense networks, the higher the density of user equipment with increased power consumption, resulting in reduced performance;
representing the power consumed at the reference distance by the capacity of the kth BS of the user i photographed at the reference distance; />
Represents the j-th neighbor BS and u i A transmission rate therebetween; p (P) i j Indicating the power consumption of the ith user at the jth neighbor BS.
Electric power consumption model: for finding mobile user equipment u i The highest point of energy dissipation, which depends on channel parameters, base station transmitted power and user equipment u i The power consumed, the base station for the total power consumption of a particular user equipment, is a combination of the power allocated by the base station and the total static power, and the power consumption of base station k can be expressed as:
wherein ,Pw and Ps Representing the static power consumed by each antenna during active and sleep modes, n A Representing the number of antenna elements on the base station, i.e. k;
in particular, a particular activation probability base station k may be expressed as follows:
wherein ,
defined as small cell base station density lambda s And user equipment density lambda u Is a ratio of (2).
Energy consumption model: a safety problem thermal model analysis is formulated during the switching, which will accurately reflect the abnormal behaviour of the device in motion, in which thermal model analysis the aim is to define the energy consumption of the user appropriately in motion or stationary, so that the energy requirements of any given user for a specific area can be tracked without disturbing the ongoing transmission, the energy consumption of any device or node being that of transmitting a piece of information E p Energy E consumed by the RF assembly as a function of the energy required RF In the switching decision and execution stage E HO The overhead incurred during this period, and can be expressed as:
E t ＝E p +E HO +E RF (14)
wherein ,Et Representing the energy required during a network handover from a service to a target base station, E t Divergent according to the service type and the target base station; p (P) a Representing the transmit power of network node a;
indicating the energy consumed by the user for both BSs at time t.
The power consumption of any device depends on the transmission time and delay, including transmission delay, propagation delay T P Processing delay and handover delay, transmission per user equipment kDelay of transmission
Can be expressed mathematically as:
wherein ns and nb Representing the number of symbols and the data rate from the base station to the user, respectively, t in bps/Hz h Representing a time span for handover information collection and preparation;
wherein the switching delay is due to an additional processing load
The delay caused and the signal exchanged during the process, i.e. +.>
Thus, the total energy consumption per device can be expressed as:
wherein 
and />
Representing the respective dependent parameters E p and EHO 。
A description of other symbols used in the system model is given in table 1 below;
table 1 symbol illustrates
Considering downlink transmission consisting of k picocell base stations in an ultra-dense network, where maximum transmission power P tx R meters are served and in the case of E eavesdroppers there are N user devices, all distributed according to a homopoisson point process. Of the N user devices, some are stationary and some are moving at a velocity v, the eavesdropper may be active or passive in nature, with the sole purpose of eavesdropping on the user device's information; assuming that the channel state information of the eavesdropper is unknown at the base station, further assuming that the density of the base station is expected to be higher than or equal to the density of the user equipment and that the eavesdropper is much lower (E < N) than the density of the user equipment, in order to reduce inter-cell interference, orthogonal resource blocks are employed to separate the k individual picocell base stations in such a way that each user equipment can be served by multiple base stations through their mutual cooperation and the method is called multiple association which will reduce the overhead due to frequent handovers in ultra dense network architectures and also improve the high quality of service of high speed user equipment.
In step S2, first, a serving base station is selected from all active base stations to find a mobile user equipment u, as shown in fig. 2, in real-time mode for a plurality of pico-cell users i Then to mobile user equipment u i After all the value comparisons of the whole track of (a) are made, the energy pattern is plotted, which comprises the following specific procedures:
mobile user equipment u i Tends to change its position at each instant of time t, where (X 1 ,Y 1 ) Indicated at t 1 Mobile user equipment u i Is provided with (X) p ,Y p) and (Xp-1 ,Y p-1 ) Respectively representing mobile user equipment u i Current and previous location, mobile user equipment u i At (x=x p ,Y＝Y p ) Energy consumption E at location p And (x=x p-1 ,Y＝Y p-1 ) Energy consumption E at location p-1 Comparing, and so on, to formulate a movement UEu i Is set in the energy mode of (a);
mobile user equipment u i Is defined as the sum of the total power consumption between two successive positions at any time interval T, given by:
where T is the time at which power consumption is observed,
depending on the mobile user equipment u i Is a separation between p and p-1; p (P) k Representing the transmission power of the kth serving BS; />
Representing the transmission power between the kth serving BS and the ith user.
The imaging of ultra dense networks can be determined by the coverage distance of small cells, and in general, the close proximity of active access points will help to increase data rates and move to mobile user equipment. Using mobile user equipment u i The covered distance is used as a criterion for tracking the energy pattern and then determining the eavesdropper for a given user device u i Is also disclosed.
It is actually assumed that the serving base station BS1 will serve only a stack of moving serving devices up to a certain distance d H ，d H<R, wherein dH : refers to and BS 2 In contrast, BS 1 Channel condition u of (2) i Is a distance deterioration result of (a). Since an eavesdropper will attempt to eavesdrop on the information at any time, but the most attributable location will be around the switching area, at d H The proximity requires an appropriate protection policy without the need to use the protection policy throughout the transmission cycle.
In step S3, the method for finding the least secure possible location specifically includes at least one search procedure:
first, for a given mobile user equipment u i If the rate R is actually achievable k Minimum achievable rate R not meeting p for serving base station k t Then introduce picocell base station k * As u i Instead of k, k * E beta, if the actual achievable rate R k > minimum achievable rate R t ，S c > 0 and S c ＜S c,th Then
There will be attack probabilities in, where k= { BS 1 },k * ＝{BS 2} and i＝{ui The node or user is treated as a damaged node, < + >>
Representing a distance p between a kth serving base station and an ith user equipment location, BS representing a base station, UE representing a user equipment;
Next, if k is equal to
If any damaged node exists in the network, the report is immediately transferred to a centralized monitoring system, the maximum energy or power dissipation point is determined by tracking the energy mode of the attack node, if at k to +.>
There is no damaged node in it, then the areas are combined under the influence of the attack and the HO procedure will be stopped for the kth base station, possibly by d p To invoke appropriate precautions. A step of
As shown in fig. 3, in step S4, the network adaptive hopping algorithm mainly includes three parts of network threat sensing, hopping policy generation and hopping implementation deployment, where network hopping is implemented by changing system configuration and state of both communication parties in a pseudo-random manner, so as to continuously and dynamically transfer network attack surfaces of a protected system, thereby trapping, confusing and confusing detection of attackers, improving utilization difficulty of vulnerabilities and backdoors, increasing difficulty and cost of attack, achieving the purpose of guaranteeing security of a target system.
In order to evaluate the performance of the proposed thermal pattern analysis to ensure communication of mobile user equipments in dense pico-cell scenarios in ultra dense networks, assuming a cell radius of 200 meters, the centralized purpose of both pico-cells is to retrieve information about all user states. The simulation parameters are listed in table 2.
Table 2 experimental parameter settings
As small cells are introduced in ultra dense networks, the channel vanishes very slowly because the 5G has advanced towards the line of sight transmission direction and it is desirable to transmit information at a very high frequency (28 GHz).
Due to the high channel gain corresponding to line-of-sight transmission, it is dominant for user equipment in dense environments. Consider the path loss model of the user equipment, shadowing losses and doppler effects, and therefore assume that the effects of multipath propagation are negligible.
Let f c =2ghz, it is necessary to analyze whether the network channel experiences slow or fast fading, it is necessary to find the coherence time (T c ) And coherence bandwidth period (B c) and 
and BBc =2.232 KHz, symbol period, T s =0.5 ns; wherein the user movement is 60kmph (high speed) and undergoes slow fading to T c . In the case of ultra-dense network architecture, the channel does not vary much due to small distance variations and high transmission frequencies and high-speed users that are assumed to be constant; table 3 shows the variation of Doppler shift at different speeds, deltat as a function of v.
TABLE 3 Doppler shift changes at different speeds
When connected to base station BS 1 Or base station BS 2 At the time, study user equipment u i Distance covered
The impact on energy efficiency is shown in fig. 4 and 5 for the straight line and random path modes, respectively. First, when user equipment u i Away from serving base station BS 1 When EE is plotted, it shows an exponential decay behavior. In a similar manner, when user equipment u i Starting from base station BS 2 Upon receiving the signal, the mode of EE shows an exponentially rising curve. This is because with the user equipment u i And serving base station BS 1 The distance between the user equipments u gradually increases i Is degraded and the effects of interference and channel loss become more dominant. User equipment u i More energy is required to maintain a sustainable data rate level. Even if ED exists, R k And also further reduced, which enhances the energy requirements of the user equipment; thus, even user equipment u i Under the influence of ED, thermal model analysis also follows a similar trend. Furthermore, the thermal model shows a similar trend for each mobile user in any of the light rays; the weakest possible point must occur at 120m < d H Between < 200m, these boundaries will be modified according to network deployment conditions.
It was also concluded from the above analysis that,user equipment u i Is correlated with the EE and energy consumption due to the fact that with the base station BS 2 EE increase with serving base station BS 1 In contrast, for base station BS 2 User equipment u i Is reduced. Thus, user equipment u i A handover preparation will be started. At user equipment u i In the case of random movements, the nature of the user will be somewhat unpredictable, in order to analyze the user equipment u i The behavior of random movements draws curves for different positions.
Further study of the performance of the thermal mode analysis, for the straight path mode, the slave base station BS 1 To base station BS 2 The spectrum efficiency variation of the distance function is shown in FIG. 6, from BS for random path mode 1 To BS 2 The spectral efficiency of the distance function varies as shown in fig. 7.
As can be seen from fig. 6 and 7, when user equipment u i And base station BS 1 As the separation between increases, the spectral efficiency shows a similar trend as EE. As previously described, user equipment u i And base station BS 1 An increase in the distance between them will decrease the channel conditions. Due to user equipment u i At the connection to the base station BS 1 When, an exponential decay curve of SE is displayed, and at d H = -170 m and base station BS 2 SE overlap of (c). It follows that the SE of the user for random movements shows a sharp and abrupt increase in value compared to the straight trajectory.
Through the study, calculate
For base station BS 1 And base station BS 2 And draws the result as the capacity of the user equipment u i Capacity variation of straight lines and random trajectories of (c) as shown in fig. 7.
As can be seen from FIG. 7, when d H At=190 m, the curves coincide. In the presence of interfering signals, when connected to the base station BS 1 When it is seen that user equipment u i Is reduced. Observations confirm that d H Is the most needed to implement protectionThe results depend on high quality services since the results are correlated with previous performance metrics.
Base station BS in random track scene 1 And base station BS 2 The capacity of (a) varies with distance as shown in fig. 9. As can be seen from FIG. 9, at a distance of about 190m, the two curves coincide, as
Is further improved by the increase of (a). This trend is consistent with expectations and is further reduced when considering interference power. It follows that in ultra-dense network scenarios, interference power plays an important role in reducing network performance.
The ultra-dense network attack prediction method combining the thermal mode analysis with the network self-adaptive jump algorithm is compared with the methods of the document [7] and the document [11], and the comparison result is shown in the table 3. It can be seen from table 3 that both the proposed method and the method of document [7] can effectively predict the low-safety region, and the method of document [11] fails to predict the low-safety region, and the proposed method is shorter in time for prediction and better in effect because the proposed method fuses the search speed of the adaptive hopping algorithm search capability and the global search capability of the thermal mode analysis.
Among them, document [7] is Secrecy Rate Analysis in Multi-Tier Heterogeneous Networks Over Generalized Fading Model (privacy rate analysis of multi-layer heterogeneous network under generalized fading model), which mainly studies the influence on UE (user equipment) capacity with increasing eavesdropper density, and concludes that positive privacy capability can still be achieved in ultra-dense networks; document [11] A survey on ultra-dense network and emerging technologies: security challenges and possible solutions (investigation of ultra dense networks and emerging technologies: security challenges and possible solutions) mainly describes the execution of physical layer message authentication by comparing channel estimates with predefined estimates of the signal. However, they do not examine the effect of an eavesdropper on the performance of the mobile user. Therefore, there is still room for improvement in the above method.
Table 3 comparison of the effects of the three methods in the case of an ultra-dense network
The invention combines the thermal mode analysis and the network self-adaptive jump algorithm, considers the safety problem related to the switching of the mobile user equipment in the ultra-dense network, and tracks the mobile user equipment u i Determining the energy pattern of the mobile user equipment u i The maximum energy or power dissipation point, finding the least secure possible location, enabling the required protection mechanism in the vicinity of the least secure possible location, instead of using the protection strategy in the whole transmission cycle, thus not causing too much interference to the transmission process; the prediction method provided by the invention has the advantages of shorter time consumption, better effect and capability of finding out a possible low-security area in an ultra-dense network environment more accurately.
Claims (5)
1. The ultra-dense network attack prediction method combining the thermal mode with the self-adaptive jump algorithm is characterized by comprising the following steps of:
s1, constructing a thermal mode analysis system model;
s2: in the thermal mode analysis system model, the distance covered by the mobile user equipment is used as a standard for tracking the energy mode, and the energy mode is drawn after all values of the whole track of the mobile user equipment are compared;
s3, tracking an energy mode of the target mobile user equipment, determining a maximum energy or power dissipation point of the mobile user equipment, and finding out a possible low-security area;
s4: the network self-adaptive hopping algorithm is introduced on the basis of the thermal mode analysis, the network threat sensing and hopping strategy mode is adopted to guide the selection of the network hopping mechanism, and a possible low-security area can be accurately found in the ultra-dense network environment;
S5: the required protection mechanism is enabled in the vicinity of the possibly low security area.
2. The method for predicting ultra-dense network attack by combining a thermal model with an adaptive jump algorithm according to claim 1, wherein the thermal model analysis system model in step S1 includes different stages of wireless channel gain, transmission, power consumption and energy consumption, and is specifically divided into a wireless channel model, a transmission model, a power consumption model and an energy consumption model, and the area where a mobile user may attack is determined by tracking the footprints of the stages of the thermal model,
wireless channel model-wireless channel gain is a combination of small-scale fading and large-scale fading, for a mobile user equipment, small-scale coefficients appear due to the time-varying nature of the channel, and the mobile device experiences doppler shift as it moves, a two-segment path loss model of the actual 3GPP cooperative protocol is employed, which model depends on line-of-sight and line-of-sight probabilities, and then the average path loss between user equipment i and base station k depends on the following functions and is given by:
wherein ,ρL and ρNL Sight and no-path loss, alpha, respectively representing reference distance L and αNL Respectively representing the line-of-sight and non-line-of-sight loss indexes; 
Representing the distance p between the kth serving base station and the ith user in position; loS Prob represents line-of-sight probability; NLoS Prob represents the probability of no line of sight; pr (Pr) L Representing channel fading coefficients;
the line-of-sight probability is divided into two parts, as follows:
wherein ,γ1 ,γ 2 And d' represents the shaping parameters, ensure
Is a continuous nature of (2); />
Representing the distance p between the kth serving base station and the ith user in position;
transmission model: in the transmission model, the total transmit power P of the kth serving base station tx Distributed among N user equipments, which are composed of
Indicating that in the presence of an eavesdropper E, the user device u i A signal received from the serving base station k at the p-th position; the following equation gives:
wherein ,
is user equipment u i At p and N i The sum of the interference powers collected there, the displayed white gaussian noise has zero mean and +.>
Variance; />
Indicating the direction from the jth neighboring base station to user equipment u in ultra dense network i The power of the transmission; />
Representing the j-th neighbor base station and user equipment u i Channel gain between; />
Representing the kth serving base station and user equipment u i Channel gain between; n (N) i Representing the interference channel of the i-th user;
thus, the achievable rate
Can be expressed as:
wherein ,λk Representing the mode/state of the base station, i.e. lambda when the base station is in sleep mode k =0, and λ when the base station is in active mode k ＝1；B i Representing the bandwidth allocated to the ith user (i e k);
when the density of the user equipment falls below the standard limit or the base station is out of service for a long period of time, the base station is put into sleep mode, lambda k Depending on the base station sleep (lambda k =0) or an awake state (λ k Operation mode of=1);
secret volume S c Represented as from source to destination u i The number of bits successfully transmitted, which is not intercepted by an eavesdropper (E), gives the following formula:
wherein ,
representing the transmission rate between the kth serving base station and the eavesdropper (E)The method comprises the steps of carrying out a first treatment on the surface of the For secure transmissions in ultra-dense networks, S c Is positive (S) c > 0) and greater than the desired threshold (S c ＞S c,th ) In ultra dense networks, the non-zero secret capacity probability is very high, typically greater than 0.95, i.e
Pr(S c ＞0)＞0.95 (9)
Thus, in order to detect an attack on a region, the following conditions need to be satisfied and are given by the following equation:
the total power consumed may be given by the following formula:
wherein ,ηEE Is the number of bits per joule, in ultra dense networks, the higher the density of user equipment with increased power consumption, resulting in reduced performance; 
Representing the capacity of a kth BS of a user i photographed at a reference distance in bps; />
Representing the power consumed at the reference distance by the capacity of the kth BS of the user i photographed at the reference distance; />
Represents the j-th neighbor BS and u i A transmission rate therebetween; p (P) i j Indicating the power consumption of the ith user at the jth neighbor BS;
electric power consumption model: for finding mobile user equipment u i The highest point of energy dissipation, which depends on channel parameters, base station transmitted power and user equipment u i Work consumedThe rate, the total power consumption of a base station for a particular user equipment, is a combination of the power allocated by the base station and the total static power, and the power consumption of base station k can be expressed as:
wherein ,Pw and Ps Representing the static power consumed by each antenna during active and sleep modes, n A Indicating the number of antenna elements on the base station,
indicating the direction from the jth neighboring base station to user equipment u in ultra dense network i The power of the transmission;
the specific activation probability base station k can be expressed as follows:
wherein ,
defined as small cell base station density lambda s And user equipment density lambda u Is a ratio of (2); p (P) a Representing the transmit power of network node a;
energy consumption model: the energy consumption of each device or node is to transmit a bit of information E p Energy E consumed by the RF assembly as a function of the energy required RF In the switching decision and execution stage E HO The overhead incurred during this period, and can be expressed as:
E t ＝E p +E HO +E RF (14)
wherein ,Et Representing the energy required during a network handover from a service to a target base station, E t Divergent according to the service type and the target base station;
representing the energy consumed by the user for two BSs at time t; p (P) fail Representing a probability of handover failure obtained in the process when a user switches his network; />
Representing the energy consumed by two base stations when a handover failure occurs;
in wireless communication, the energy consumption of each device depends on the transmission time and delay, including the transmission delay, propagation delay T P Processing delay and handover delay, transmission delay of each user equipment k
Can be expressed mathematically as:
wherein ,ns and nb Representing the number of symbols and the data rate from the base station to the user, respectively, t in bps/Hz h Representing a time span for handover information collection and preparation;
wherein the switching delay is due to an additional processing load
The delay caused and the signal exchanged during the process, i.e. +.>
Thus, the total energy consumption per device can be expressed as:
wherein ,
and />
Representing the respective dependent parameters E p and EHO ；T p Representing propagation delay; p (P) k Representing the transmission power of the kth serving BS; />
Representing the kth serving base station and user equipment u i Channel gain between; n is n s and nb Respectively representing the number of symbols from the base station to the user and the data rate in bps/Hz.
3. The method for predicting ultra-dense network attack by combining a thermal model with an adaptive hopping algorithm according to claim 1, wherein in step S2, firstly, a serving base station is selected from all active base stations, a mobile user equipment is found, and then, after comparing all values of the entire trajectory of the mobile user equipment, an energy model is drawn, which comprises the following specific steps:
the mobile user equipment tends to change its position at each instant of time t, where (X 1 ,Y 1 ) Indicated at t 1 Is located at the position of the mobile user equipment, is set (X p ,Y p) and (Xp-1 ,Y p-1 ) Representing the current and previous positions of the mobile user equipment, respectively, to be mobile user equipment u i At (x=x p ,Y＝Y p ) Energy consumption E at location p And (x=x p-1 ,Y＝Y p-1 ) Energy consumption E at location p-1 Comparing, and so on, to formulate a movement UEu i Is set in the energy mode of (a);
mobile user equipment u i Is defined as the sum of the total power consumption between two successive positions at any time interval T, given by:
Where T is the time at which power consumption is observed,
depending on the mobile user equipment u i V represents the speed as well as the separation between p and p-1; p (P) w and Ps Representing the static power consumed by each antenna during active and sleep modes, respectively; p (P) k Representing the transmission power of the kth serving BS; />
Representing the transmission power between the kth serving BS and the ith user.
4. The method for predicting ultra-dense network attacks by combining a thermal model with an adaptive hopping algorithm according to claim 1, wherein the method for finding the least secure possible location in step S3 specifically comprises at least one search procedure:
first, for a given mobile user equipment, if the rate R is actually achievable k Does not satisfy the minimum achievable rate R for serving base station k t Then introduce picocell base station k * Serving base station as mobile user equipment replaces k, k * E beta, beta represents the BS set {1, …, k }, if the actual achievable rate R k > minimum achievable rate R t ，S c > 0 and S c ＜S c,th Then
There will be attack probabilities in, where k= { BS 1 },k * ＝{BS 2} and i＝{ui Will be regarded asImpaired node or user, < >>
Representing a distance p between a kth serving base station and an ith user equipment location, BS representing a base station, UE representing a user equipment;
Next, if k is equal to
If any damaged node is present, the report is immediately transferred to a centralized monitoring system, the maximum energy or power dissipation point is determined by tracking the energy pattern of the attacked node, if at k to +.>
There is no damaged node in it, then the areas are merged under the influence of the attack and the wireless network communication procedure will be stopped for the kth base station, possibly by d p To invoke appropriate precautions.
5. The method is characterized in that in step S4, the network adaptive hopping algorithm mainly comprises three parts of network threat sensing, hopping strategy generation and hopping implementation deployment, the network hopping realizes continuous and dynamic transfer of the network attack surface of a protected system by changing the system configuration and the state of both communication parties in a pseudo-random manner so as to trap, confuse and confuse the detection of an attacker, thereby improving the utilization difficulty of a vulnerability and a backdoor, increasing the difficulty and the cost of the attack and achieving the aim of ensuring the safety of a target system, the process comprises the steps of acquiring the attacker information through detection scanning, establishing a target network view, forming a hopping network view based on threat sensing, triggering a hopping defense function, generating a network hopping strategy, deploying the network hopping configuration information through hopping implementation, and then completing continuous and dynamic transfer of the network attack surface of the protected system so as to trap, confuse and confuse the detection of the attacker; the algorithm maximizes defense benefits on the premise of guaranteeing network service quality, triggers the hopping strategy based on threat awareness, and improves the pertinence of network hopping strategy selection.
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