CN111242272A - Wireless sensor network anomaly detection method - Google Patents
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Abstract
The invention discloses a wireless sensor network anomaly detection method, which is based on a fruit fly optimization algorithm and applies a fuzzy support vector machine to the field of wireless sensor network anomaly detection and comprises the following steps: a training stage: s1: collecting sensor detection data, and preprocessing the data to form a training data set; s2: establishing a wireless sensor network anomaly detection model based on the FSVM technology; s3: establishing an IFOA-FSVM model, and performing anomaly detection training on a data set; a detection stage: s4: collecting sensor detection data, and preprocessing the data to form a sample to be detected; s5: and inputting the sample to be detected into the IFOA-FSVM model for detection, and judging whether the sample to be detected is abnormal or not.
Description
Technical Field
The invention relates to the field of wireless sensor network information security, in particular to a wireless sensor network anomaly detection method.
Background
The wireless sensor network is a special mobile ad hoc network different from the conventional wired network. The wireless sensor network is widely applied to various fields, such as military fields of national defense, anti-terrorism and the like, due to the advantages of simple node networking, capability of self-organizing to form a network, low node cost and the like. When the wireless network sensor is abnormal, abnormal data in the sensor network can be detected efficiently in real time, and the method has very important significance for early warning and prevention of external emergencies and health condition monitoring of the sensor network.
In recent years, many studies have been made at home and abroad on a method for detecting abnormal data of a wireless sensor network. Among these researches, the anomaly detection technology based on the support vector machine has become a research hotspot of the anomaly detection technology due to the simple structure, the fast training speed, the good prediction accuracy and the sufficient theoretical basis. However, most of the existing wireless sensor network anomaly detection models based on the SVM (support vector machine) do not introduce the fuzzy theory, so that the models are weak in generalization capability on data sets containing noise samples compared with the support vector machine model based on the fuzzy theory.
Like the SVM, the size of the hyperparameter of the FSVM (fuzzy support vector machine) has a large influence on the prediction capability of the model, the hyperparameter of the algorithm is difficult to set, a certain strategy needs to be adopted for setting, and in view of the situation, the drosophila optimization algorithm is adopted for setting.
Disclosure of Invention
To the deficiency of the prior art, the technical problem to be solved by the present patent application is: how to provide a wireless sensor network anomaly detection method which can solve the problem of hyper-parameter setting and improve anomaly detection precision.
In order to achieve the purpose, the invention adopts the following technical scheme:
a wireless sensor network anomaly detection method is based on a fruit fly optimization algorithm, and the method applies a fuzzy support vector machine to the field of wireless sensor network anomaly detection and comprises the following steps:
a training stage:
s1: collecting sensor detection data, and preprocessing the data to form a training data set;
s2: establishing a wireless sensor network anomaly detection model based on the FSVM technology;
s3: establishing an IFOA-FSVM model, and performing anomaly detection training on a data set;
a detection stage:
s4: collecting sensor detection data, and preprocessing the data to form a sample to be detected;
s5: and inputting the sample to be detected into the IFOA-FSVM model for detection, and judging whether the sample to be detected is abnormal or not.
Preferably, in step S3, the building of the IFOA-FSVM model includes the following steps:
a1: improving a fruit fly algorithm;
a2: searching for the FSVM to obtain the optimal parameters by combining the training data set in the step S1 and the improved drosophila algorithm in the step A1;
a3 brings the optimal parameters back to the FSVM to complete the training of the anomaly detection model.
Preferably, in step a1, the improvement of the drosophila algorithm comprises the following steps:
b1: the quantity Num of the fruit flies in the initial search algorithm, the total iteration times T of the algorithm, and the current optimal position H of the fruit fly populationopt,jHistorical optimum odor concentration bestSmell of fruit fly population, step length adjustment parameters rho and
extracting zz proportion drosophila individuals from each iteration to search again, wherein j belongs to n, and n is the variable number of the optimization objective function;
b2: calculating a step adjustment factor m (k);
wherein k is the current iteration number, rho is in [2, 8 ]]，
B3: if k < T/2, the fruit fly population is divided into two subgroups group1 and group2, each subgroup executing a random step search strategy with a different range of advance steps. Wherein, group1 carries out search iteration according to the random value of the normal step range, and group2 carries out search iteration according to the random value of the large step range;
Hi，j＝Hopt，j+RandomValue×m(k)
Hi，j＝Hopt，j+3×RandomValue×m(k)
wherein i is the ith fruit fly individual in the population, and j is the jth variable in the objective function;
b4: and if k is less than T/2, modifying the search step range of partial fruit fly subgroups. Wherein, group1 still performs search iteration according to the random value of the normal step range in B3, and group2 performs search iteration according to the random value of the large step range;
Hi，j＝Hopt，j+0.2×RandomValue×m(k)
b5: calculating the odor concentration value smell of each fruit fly according to the fitness function;
smelli＝Function(Hi)
wherein, the Function is a fitness Function, namely a target Function to be optimized;
b6: sorting the odor concentration smell of the fruit flies, finding out fruit fly individuals (smllBest) with the largest smell value, namely current optimal fruit fly individuals, judging whether the smell is larger than the historical optimal odor concentration bestsell or not, and if so, updating the bestsell and the current optimal position Hopt，j(ii) a Otherwise, not updating, and finding out the worst drosophila group with a given proportion;
b7: the worst drosophila melanogaster population is searched again according to the random value of the normal step length range in the B3, the corresponding smell value is calculated, the optimal drosophila melanogaster individual in the search is found, if the smell value is larger than bestsell, bestsell and the current optimal position H are updatedopt，j(ii) a Otherwise, the updating is not carried out.
B8: judging whether the maximum search times T is met, if so, stopping searching, and returning the searched optimal objective function solution; otherwise, the execution is repeated from B2 until the termination condition is satisfied.
Preferably, the preprocessing of the data in steps S1 and S4 includes missing value padding and feature value normalization; in the process of carrying out characteristic value standardization, min-max standardization is adopted to carry out pretreatment on characteristic data, and the value range of the treated characteristic is between [0, 1 ]. Given the data:
wherein l is the number of samples, and d is the characteristic number of the samples; .
The minimum and maximum values for each column of features in matrix X use vector XtminAnd xtmaxIt is shown that,
xtmin＝{x1min，x2min，...，xdmin}
xtmax＝{x1max，x2max，...，xdmax}
each sample xt∈XAuThe following normalization is performed in order to perform,
wherein x istpIs xtAnd (5) normalizing the feature vector.
Preferably, the specific steps of step S2 are:
c1: given the training set D of the fuzzy support vector machine:
D＝{(x1，y1，s1)，...，(xi，yi，si)，...，(xl，yl，sl)}，
wherein x isi∈RdD is the feature dimension of each sample, and l is the number of samples; y isiFor each sample category, indicating normal or abnormal; siRepresenting the fuzzy membership degree of each sample as the probability that the corresponding sample belongs to a certain class, and classifying the hyperplane by the FSVM as follows;
wherein z isiThe characteristic value of the original characteristic of the sample after being processed by the kernel function corresponding to the FSVM, namely RdThe feature vector in (1) is passed through a mapping function
A conversion to the feature space z is made,
w is a normal vector of the hyperplane, and determines the direction of the hyperplane; b is a displacement term, and determines the distance between the hyperplane and the origin; epsiloniIs a relaxation variable which functions to reduce the influence of outliers on the classification hyperplane; c is a penalty coefficient, has great influence on the final classification capability of the model and is a parameter needing key search;
c2: and establishing an anomaly detection model based on the FSVM.
Wherein, aiIs a Lagrange factor; k (·,. cndot.) is a kernel function, K (x)i,xj)＝zi·zj。
Preferably, the steps a2 and A3 comprise the following sub-steps:
d1: initializing relevant parameters such as iteration times, step length adjustment factors and the like of the IFOA, and selecting an FSVM kernel function according to the data set condition;
d2: searching for the FSVM hyperparameters C and g by improving a drosophila algorithm, inputting the searched hyperparameters into the FSVM algorithm, performing cross validation on a training set, and obtaining the optimal odor concentration smlBest of the group of parameters corresponding to the iterative population of the round by combining a selected error function;
d3: and continuously iterating to find optimal parameters C and g according to an updating strategy of the drosophila algorithm, and further solving w and b of the FSVM.
Preferably, the step S5 uses the following formula when detecting the sample:
advantageous effects
(1) The fuzzy support vector machine is applied to the field of wireless sensor network anomaly detection, and compared with an anomaly detection algorithm based on a support vector, the model has relatively strong robustness.
(2) The fruit fly algorithm is improved, and the advancing step length of the fruit flies can be intelligently adjusted according to the iteration times; dividing the fruit flies into two subgroups, and executing search strategies with different step length ranges in the early stage and the later stage of search; a certain proportion of fruit flies are selected in each iteration process for re-searching, so that the searching diversity of the fruit flies is enriched, and the searching capability of the fruit fly algorithm is improved.
(3) The invention adopts the improved drosophila algorithm to search the hyperparameters of the fuzzy support vector machine, and improves the abnormal detection accuracy of the fuzzy support vector machine.
Description of the drawings:
FIG. 1 is a diagram of a model of the anomaly detection method of the present invention;
FIG. 2 is a flow chart of the improved fruit fly algorithm of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1-2, a method for detecting an abnormality of a wireless sensor network, based on a drosophila optimization algorithm, for applying a fuzzy support vector machine to the field of detecting an abnormality of a wireless sensor network, includes the following steps:
a training stage:
s1: collecting sensor detection data, and preprocessing the data to form a training data set;
s2: establishing a wireless sensor network anomaly detection model based on the FSVM technology;
s3: establishing an IFOA-FSVM model, and performing anomaly detection training on a data set;
a detection stage:
s4: collecting sensor detection data, and preprocessing the data to form a sample to be detected;
s5: and inputting the sample to be detected into the IFOA-FSVM model for detection, and judging whether the sample to be detected is abnormal or not.
In this embodiment, in step S3, the establishing of the IFOA-FSVM model includes the following steps:
a1: improving a fruit fly algorithm;
a2: searching for the FSVM to obtain the optimal parameters by combining the training data set in the step S1 and the improved drosophila algorithm in the step A1;
a3 brings the optimal parameters back to the FSVM to complete the training of the anomaly detection model.
In this embodiment, in the step a1, the improvement of the drosophila algorithm includes the following steps:
b1: the quantity Num of the fruit flies in the initial search algorithm, the total iteration times T of the algorithm, and the current optimal position H of the fruit fly populationopt,jHistorical optimum odor concentration bestSmell of fruit fly population, step length adjustment parameters rho and
extracting zz proportion drosophila individuals from each iteration to search again, wherein j belongs to n, and n is the variable number of the optimization objective function;
b2: calculating a step adjustment factor m (k);
wherein k is the current iteration number, and P is the [2, 8 ]]，
B3: if k < T/2, the fruit fly population is divided into two subgroups group1 and group2, each subgroup executing a random step search strategy with a different range of advance steps. Wherein, group1 carries out search iteration according to the random value of the normal step range, and group2 carries out search iteration according to the random value of the large step range;
Hi，j＝Hopt，j+RandomValue×m(k)
Hi，j＝Hopt，j+3×RandomValue×m(k)
wherein i is the ith fruit fly individual in the population, and j is the jth variable in the objective function;
b4: and if k is less than T/2, modifying the search step range of partial fruit fly subgroups. Wherein, group1 still performs search iteration according to the random value of the normal step range in B3, and group2 performs search iteration according to the random value of the large step range;
Hi，j＝Hopt，j+0.2×RandomValue×m(k)
b5: calculating the odor concentration value smell of each fruit fly according to the fitness function;
smelli＝Function(Hi)
wherein, the Function is a fitness Function, namely a target Function to be optimized;
b6: sorting the odor concentration smell of the fruit flies, finding out fruit fly individuals (smllBest) with the largest smell value, namely current optimal fruit fly individuals, judging whether the smell is larger than the historical optimal odor concentration bestsell or not, and if so, updating the bestsell and the current optimal position Hopt，j(ii) a Otherwise, not updating, and finding out the worst drosophila group with a given proportion;
b7: the worst drosophila melanogaster population is searched again according to the random value of the normal step length range in the B3, the corresponding smell value is calculated, the optimal drosophila melanogaster individual in the search is found, if the smell value is larger than bestsell, bestsell and the current optimal position H are updatedopt，j(ii) a Otherwise, the updating is not carried out.
B8: judging whether the maximum search times T is met, if so, stopping searching, and returning the searched optimal objective function solution; otherwise, the execution is repeated from B2 until the termination condition is satisfied.
In this embodiment, the preprocessing of the data in steps S1 and S4 includes missing value padding and feature value normalization; in the process of carrying out characteristic value standardization, min-max standardization is adopted to carry out pretreatment on characteristic data, and the value range of the treated characteristic is between [0, 1 ]. Given the data:
wherein l is the number of samples, and d is the characteristic number of the samples; .
The minimum and maximum values for each column of features in matrix X use vector XtminAnd xtmaxIt is shown that,
xtmin＝{x1min，x2min，...，xdmin}
xtmax＝{x1max，x2max，...，xdmax}
each sample xt∈XAllThe following normalization is performed in order to perform,
wherein x istpIs xtAnd (5) normalizing the feature vector.
In this embodiment, the specific steps of step S2 are as follows:
c1: given the training set D of the fuzzy support vector machine:
D＝{(x1，y1，s1)，...，(xi，yi，si)，...，(xl，yl，sl)}，
wherein x isi∈RdD is the feature dimension of each sample, and l is the number of samples; y isiFor each sample category, indicating normal or abnormal; siRepresenting the fuzzy membership degree of each sample as the probability that the corresponding sample belongs to a certain class, and classifying the hyperplane by the FSVM as follows;
wherein z isiThe characteristic value of the original characteristic of the sample after being processed by the kernel function corresponding to the FSVM, namely RdThe feature vector in (1) is passed through a mapping function
A conversion to the feature space z is made,
w is a normal vector of the hyperplane, and determines the direction of the hyperplane; b is a displacement term, and determines the distance between the hyperplane and the origin; epsiloniIs a relaxation variable which functions to reduce the influence of outliers on the classification hyperplane; c is a penalty coefficient, has great influence on the final classification capability of the model and is a parameter needing key search;
c2: and establishing an anomaly detection model based on the FSVM.
Wherein, aiIs a Lagrange factor; k (·,. cndot.) is a kernel function, K (x)i,xj)＝zi·zj。
In this embodiment, the steps a2 and A3 include the following substeps:
d1: initializing relevant parameters such as iteration times and step length adjustment factors of the IFOA, and selecting an FSVM kernel function (selecting a Gaussian kernel for a nonlinear problem) according to the data set condition;
d2: searching for the FSVM hyperparameters C and g (the kernel function is a Gaussian kernel) by improving a drosophila algorithm, inputting the searched hyperparameters into the FSVM algorithm, performing cross validation on a training set, and combining a selected error function (fitness function) to obtain the optimal odor concentration smlBest of the group of parameters corresponding to the iterative population of the round;
d3: and continuously iterating to find optimal parameters C and g according to an updating strategy of the drosophila algorithm, and further solving w and b of the FSVM.
In this embodiment, in the step S5, a formula is adopted when the sample is detected:
advantageous effects
(1) The fuzzy support vector machine is applied to the field of wireless sensor network anomaly detection, and compared with an anomaly detection algorithm based on a support vector, the model has relatively strong robustness.
(2) The fruit fly algorithm is improved, and the advancing step length of the fruit flies can be intelligently adjusted according to the iteration times; dividing the fruit flies into two subgroups, and executing search strategies with different step length ranges in the early stage and the later stage of search; a certain proportion of fruit flies are selected in each iteration process for re-searching, so that the searching diversity of the fruit flies is enriched, and the searching capability of the fruit fly algorithm is improved.
(3) The invention adopts the improved drosophila algorithm to search the hyperparameters of the fuzzy support vector machine, and improves the abnormal detection accuracy of the fuzzy support vector machine.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (7)
1. A wireless sensor network anomaly detection method is characterized in that: based on a fruit fly optimization algorithm, the application of the fuzzy support vector machine to the field of wireless sensor network anomaly detection comprises the following steps:
a training stage:
s1: collecting sensor detection data, and preprocessing the data to form a training data set;
s2: establishing a wireless sensor network anomaly detection model based on the FSVM technology;
s3: establishing an IFOA-FSVM model, and performing anomaly detection training on a data set;
a detection stage:
s4: collecting sensor detection data, and preprocessing the data to form a sample to be detected;
s5: and inputting the sample to be detected into the IFOA-FSVM model for detection, and judging whether the sample to be detected is abnormal or not.
2. The method for detecting the abnormality in the wireless sensor network according to claim 1, wherein the step S3 of establishing the IFOA-FSVM model includes the following steps:
a1: improving a fruit fly algorithm;
a2: searching for the FSVM to obtain the optimal parameters by combining the training data set in the step S1 and the improved drosophila algorithm in the step A1;
a3 brings the optimal parameters back to the FSVM to complete the training of the anomaly detection model.
3. The method for detecting the abnormality of the wireless sensor network according to claim 2, wherein in the step A1, the improvement of the drosophila algorithm comprises the following steps:
b1: the quantity Num of the fruit flies in the initial search algorithm, the total iteration times T of the algorithm, and the current optimal position H of the fruit fly populationopt,jHistorical optimum odor concentration bestSmell of fruit fly population, step length adjustment parameters rho and
extracting zz proportion drosophila individuals from each iteration to search again, wherein j belongs to n, and n is the variable number of the optimization objective function;
b2: calculating a step adjustment factor m (k);
wherein k is the current iteration number, rho is in [2, 8 ]]，
B3: if k < T/2, the fruit fly population is divided into two subgroups group1 and group2, each subgroup executing a random step search strategy with a different range of advance steps. Wherein, group1 carries out search iteration according to the random value of the normal step range, and group2 carries out search iteration according to the random value of the large step range;
Hi，j＝Hopt，j+RandomValue×m(k)
Hi，j＝Hopt，j+3×RandomValue×m(k)
wherein i is the ith fruit fly individual in the population, and j is the jth variable in the objective function;
b4: and if k is less than T/2, modifying the search step range of partial fruit fly subgroups. Wherein, group1 still performs search iteration according to the random value of the normal step range in B3, and group2 performs search iteration according to the random value of the large step range;
Hi，j＝Hopt，j+0.2×RandomValue×m(k)
b5: calculating the odor concentration value smell of each fruit fly according to the fitness function;
smelli＝Function(Hi)
wherein, the Function is a fitness Function, namely a target Function to be optimized;
b6: sorting the odor concentration smell of the fruit flies, finding out fruit fly individuals (smllBest) with the largest smell value, namely current optimal fruit fly individuals, judging whether the smell is larger than the historical optimal odor concentration bestsell or not, and if so, updating the bestsell and the current optimal position Hopt，j(ii) a Otherwise, not updating, and finding out the worst drosophila group with a given proportion;
b7: the worst drosophila melanogaster population is searched again according to the random value of the normal step length range in the B3, the corresponding smell value is calculated, the optimal drosophila melanogaster individual in the search is found, if the smell value is larger than bestsell, bestsell and the current optimal position H are updatedopt，j(ii) a Otherwise, the updating is not carried out.
B8: judging whether the maximum search times T is met, if so, stopping searching, and returning the searched optimal objective function solution; otherwise, the execution is repeated from B2 until the termination condition is satisfied.
4. The method of claim 3, wherein the preprocessing of the data in steps S1 and S4 includes missing value padding and feature value normalization; in the process of carrying out characteristic value standardization, min-max standardization is adopted to carry out pretreatment on characteristic data, and the value range of the treated characteristic is between [0, 1 ]. Given the data:
wherein l is the number of samples, and d is the characteristic number of the samples; .
The minimum and maximum values for each column of features in matrix X use vector XtminAnd xtmaxIt is shown that,
xtmin＝{x1min，x2min，…，xdmin}
xtmax＝{x1max，x2max，…，xdmax}
each sample xt∈XAllThe following normalization is performed in order to perform,
wherein x istpIs xtAnd (5) normalizing the feature vector.
5. The method for detecting the abnormality of the wireless sensor network according to claim 4, wherein the step S2 includes the following steps:
c1: given the training set D of the fuzzy support vector machine:
D＝{(x1，y1，s1)，...，(xi，yi，si)，...，(xl，yl，sl)}，
wherein x isi∈RdD is the feature dimension of each sample, and l is the number of samples; y isiFor each sample category, indicating normal or abnormal; siRepresenting the fuzzy membership degree of each sample as the probability that the corresponding sample belongs to a certain class, and classifying the hyperplane by the FSVM as follows;
wherein z isiThe characteristic value of the original characteristic of the sample after being processed by the kernel function corresponding to the FSVM, namely RdThe feature vector in (1) is passed through a mapping function
A conversion to the feature space z is made,
w is a normal vector of the hyperplane, and determines the direction of the hyperplane; b is a displacement term, and determines the distance between the hyperplane and the origin; epsiloniIs a relaxation variable which functions to reduce the influence of outliers on the classification hyperplane; c is a penalty coefficient, has great influence on the final classification capability of the model and is a parameter needing key search;
c2: and establishing an anomaly detection model based on the FSVM.
Wherein, aiIs a Lagrange factor; k (·,. cndot.) is a kernel function, K (x)i,xj)＝zi·zj。
6. The method for detecting the abnormality of the wireless sensor network according to claim 5, wherein the steps A2 and A3 comprise the following sub-steps:
d1: initializing relevant parameters such as iteration times, step length adjustment factors and the like of the IFOA, and selecting an FSVM kernel function according to the data set condition;
d2: searching for the FSVM hyperparameters C and g by improving a drosophila algorithm, inputting the searched hyperparameters into the FSVM algorithm, performing cross validation on a training set, and obtaining the optimal odor concentration smlBest of the group of parameters corresponding to the iterative population of the round by combining a selected error function;
d3: and continuously iterating to find optimal parameters C and g according to an updating strategy of the drosophila algorithm, and further solving w and b of the FSVM.
7. The method for detecting the abnormality in the wireless sensor network according to claim 6, wherein the step S5 adopts the following formula when detecting the sample:
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