CN112615843A - Power Internet of things network security situation assessment method based on multi-channel SAE-AdaBoost - Google Patents
Power Internet of things network security situation assessment method based on multi-channel SAE-AdaBoost Download PDFInfo
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Abstract
The invention relates to the field of evaluation of network security situation of an information system, provides a power internet of things network security situation evaluation method based on multi-channel SAE-AdaBoost, and aims to solve the problems of poor precision and large generalization error of the existing evaluation method. The main scheme comprises the following steps: network security situation index is divided into a plurality of channels T1，T2，Λ，Tn(ii) a Step 2: for each channel, SAE maps each unlabeled training sample k-dimensional vector x' to m-dimensional coding vector x through the hidden layerhTo obtain a low-dimensional representation x of the high-dimensional datah(ii) a And step 3: the AdaBoost algorithm continuously changes the weights of samples from training data, serially learns a series of weak learners, linearly combines the weak learners into a strong learner, and uses the strong learner to evaluate a corresponding channel; and 4, step 4: AHP comprehensive consideration among channelsThe relative importance of the network security situation is obtained by fusing the evaluation results of a plurality of channels.
Description
Technical Field
The invention relates to the field of evaluation of network security situation of an information system, in particular to a method for comprehensively evaluating the network security situation of an electric power internet of things based on a multi-channel architecture combined with a sparse self-encoder and an AdaBoost integrated learning algorithm.
Background
In recent years, with the continuous development of the power internet of things, the types of access devices are increasing, the structure of the power internet of things is becoming more complicated, so that the complexity and uncertainty of an information network in the power industry are increasing, and the information security protection of the power internet of things faces a huge challenge. Therefore, the supervision requirement on the overall network security situation of the system is continuously improved, and the traditional network security situation evaluation means cannot meet the efficient and accurate evaluation requirement of a complex system. In order to improve the network security guarantee capability of the power internet of things, accurately and objectively evaluate the network security situation of the power information and effectively guide the information system to operate safely, efficiently and economically, the method for evaluating the network security situation of the power internet of things is very important, and can integrate various network security indexes of the system and realize automatic evaluation.
In the aspect of analyzing each situation index of an object and carrying out comprehensive network security situation evaluation, the invention patent with the application number of CN201910432976.9 and the name of SAE + BPNN-based network security situation evaluation method discloses a comprehensive evaluation method of network security situation, which belongs to the field of network security situation evaluation, wherein the main thought of the comprehensive evaluation method is as follows: normalizing the extracted index data; inputting the normalized index data into a trained deep self-coding neural network to perform dimension reduction processing on the normalized index data; and inputting the index data subjected to the dimensionality reduction treatment into the trained BP neural network so as to evaluate the network security situation. According to the evaluation method, the sparse self-encoder and the BP neural network are combined, the problem that the evaluation data dimension is too large, so that the model construction complexity is high is solved, and a scheme with high evaluation efficiency is provided for network security situation evaluation.
Although the technical method considers the technical defects of the original BPNN method, the idea of network security situation evaluation by combining SAE (sparse self-encoder) and BPNN (back propagation neural network) is adopted, and the problem that the complexity of model construction is higher due to overlarge model evaluation data dimension is solved. However, in terms of a specific used technical method, the patent still uses the most traditional neural network algorithm, the method does not consider the corresponding relation between the indexes and the safety problem, neglects the difference of the network safety problems represented by different types of indexes, causes the characteristic of mixing different types of indexes, and causes interference to the whole characteristic. For enterprise-level information systems with more safety problem factors, such as the power internet of things, the traditional neural network algorithm has certain limitation in the aspect of overall system network safety situation evaluation; furthermore, the traditional neural network algorithm is easy to be over-fitted, thereby causing the accuracy of the model to be reduced.
Disclosure of Invention
The invention aims to solve the problems that the existing method does not consider the corresponding relation between indexes and security problems, ignores the difference of network security problems represented by different indexes, generates the mixed characteristics of different indexes and interferes the overall characteristics, so that the existing evaluation method has poor precision and large generalization error.
In order to solve the technical problems, the invention adopts the following technical scheme:
the invention provides a power Internet of things network security situation evaluation method based on multi-channel SAE-AdaBoost, which comprises the following steps:
step 1: netNetwork security situation index is divided into a plurality of channels T1，T2，Λ，Tn；
Step 2: for each channel, SAE maps each unlabeled training sample k-dimensional vector x' to m-dimensional coding vector x through the hidden layerhTo obtain a low-dimensional representation x of the high-dimensional datah；
And step 3: for channel TiTraining data set
Wherein
Representing the sample points, x, after the dimension reduction of step 2hThe data after dimensionality reduction, x refers to index data after channel division, and yiRepresenting the category corresponding to the sample, taking the value as { -1, 1}, and representing that the security event corresponding to the channel occurs when the value is '1', wherein an AdaBoost algorithm continuously changes the weight of the sample from training data, serially learns a series of weak learners, linearly combines the weak learners into a strong learner, and uses the strong learner to evaluate the channel corresponding to the weak learner;
and 4, step 4: the AHP comprehensively considers the relative importance among the channels, and integrates the evaluation results of the multiple channels to obtain the overall network security situation.
In the above technical solution, step 1 includes the following steps:
the network security problem that affects the network security environment is divided into n channels: t is1，T2，Λ，Tn；
And dividing the overall situation index into the n channels according to the corresponding relation between each index and the safety problem.
In the above technical solution, step 2 includes the following steps:
s2.1: for each channel TiIndex data of
Performing normalization, collectingUsing the minimum-maximum criterion, channel TiThe size range of the index data is narrowed to [0, 1 ]]The method comprises the following steps:
s2.2: to channel TiAnd (3) encoding index data: mapping each unlabeled training sample k-dimensional vector x' into m-dimensional coded vector x through the hidden layerhTo obtain a low-dimensional representation x of the high-dimensional datah。
In the above technical solution, step 3 specifically includes:
step 3.1: dm＝(wm1，wm2，A，wmN) Representing the weight of the mth weak classifier sample, the weight of the initialized sample point is: d1＝(w11，w12，Λ，w1N)，
Step 3.2: for M ═ 1, 2, Λ, M, use with weight DmTraining a weak learner Gm(x)；
Step 3.3: weak learner G for calculationm(x) Classification error of (2):
step 3.4: by forward step-by-step algorithm, weak classifier Gm(x) The coefficients of (a) are:
step 3.5: update the weight distribution of the m +1 th weak learner sample:
Dm+1＝(wm+1，1，wm+1，2，Λ，wm+1，N)
wherein,
wherein,
wherein Z ismIs a normalization factor, and the main function is to convert wmiNormalized to between 0 and 1, such that
Step 3.6: and (3) carrying out linear combination on all weak classifiers by using the coefficient weighting of each weak classifier to obtain a final strong classifier:
in the above technical solution, step 4 specifically includes the following steps:
step 4.1: a risk event influence value evaluation table is formulated based on a universal vulnerability scoring system (CVSS):
| index (I) | Degree of influence | Influence value |
| Confidentiality (C) | None (N)/Low (L)/high (H) | 0/0.22/0.56 |
| Integrity (I) | Does not have (N) to be based onLow (L)/high (H) | 0/0.22/0.56 |
| Availability (A) | None (N)/Low (L)/high (H) | 0/0.22/0.56 |
Step 4.2: determining each channel T by combining the table in S1iC, I, A values of corresponding security events, and calculating a channel TiInfluence value of
Step 4.3: and combining the table in the S1, and constructing a paired comparison matrix of the channel by using a paired comparison method from three dimensions of confidentiality, integrity and availability:
wherein a isijRepresents a channel TiRelative to the channel TjThe degree of importance of;
step 4.4: calculating a characteristic root lambda, and carrying out consistency check: aw ═ λ w
Defining a consistency index:
n represents the number of channels;
defining a consistency ratio:
wherein RI is a random consistency index, when the consistency ratio CR is less than 0.1, the inconsistency degree of A is within an allowable range and has satisfactory consistency, and the normalized characteristic vector is used as a weight vector through consistency test, otherwise, a comparison matrix A is reconstructed;
step 4.5: calculating a weight vector w by using the inspected characteristic root lambda, and weighting and fusing all channels to obtain the overall network security situation:
wherein, I (t) is a general function, when the parameter t is true, the output is 1; when the parameter t is false, the output is 0,
meaning that Ti corresponds to a strong classifier.
Because the invention adopts the technical scheme, the invention has the following beneficial effects:
1. the invention divides the whole index into a plurality of channels, not only fully considers the different network safety problems represented by different types of indexes, but also ensures the independence and the purity of the characteristics in the same channel. A plurality of channels are divided according to the safety problem, and when the network safety situation of the power internet of things is rapidly reduced, the positioning, tracking and tracing of the safety problem are facilitated.
2. Aiming at the problem that the accuracy of the model is reduced due to overfitting of the traditional neural network algorithm, an AdaBoost integrated learning algorithm is introduced, a plurality of weak learners are linearly weighted, the generalization error is reduced, and the accuracy of the model is improved.
3. Based on the CVSS risk event influence value evaluation table, the AHP is used for fusing all the channels to obtain the overall network security situation, and the relative importance among all the channels is comprehensively considered, so that the evaluation result is more dependent and persuasive.
Drawings
Fig. 1 is a schematic diagram of channel division.
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 detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. The components of 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.
The network security situation indexes are divided into a plurality of channels:
considering that the network security environment is usually affected by a plurality of aspects, the network security problem that can affect the network security environment is divided into n channels: t is1，T2，Λ，Tn；
And dividing the overall situation index into the n channels according to the corresponding relation between each index and the safety problem.
The specific steps of the SAE-AdaBoost for respectively evaluating the multiple channels are as follows:
s1: for each channel TiIndex data of
Normalizing by using a minimum-maximum standard method to obtain a channel TiThe size range of the index data is narrowed to [0, 1 ]]The method comprises the following steps:
s2: to channel TiThe process of coding the index data is to map each unlabeled training sample k-dimensional vector x' into an m-dimensional coding vector x through the hidden layerhTo obtain a low-dimensional representation x of the high-dimensional datah. The method comprises the following specific steps:
(1) normalizing data x '═ x'1，x′2，Λ，x′kMapping to a hidden layer through a linear function and a sigmoid activation function to obtain a coding result
Namely:
mapping the encoding result y to a reconstruction layer through a linear function and a sigmoid activation function to obtain a decoding result x ″ { x ″)1，x″2，Λ，x″kThe dimension of x "is consistent with the dimension of the original data x', i.e.:
wherein x ishFor the encoded data, x 'is the characteristic expression of the original data, x' is the decoded data, w1、w2、b1、b2Weights and biases for input layer to hidden layer, hidden layer to reconstructed layer, respectively.
(2) When the output value of a certain neuron of the hidden layer is close to 1, the unit is in an active state, and conversely, the unit is in an inactive state. For the purpose of "sparseness", a by suppressing most neurons of the hidden layer and leaving them in an "inactive" statej(k) Representing the activation of the jth cell, then the average activation of the ith neuron in the hidden layer is:
(3) using KL divergence as PN expression to penalize
Deviating by a constant p close to 0, so that most neurons are suppressed for sparseness:
where Sh represents the number of neurons in the cryptic layer,
is composed of
And p, relative entropy, which measures the difference between the two distributions. Since the relative entropy is a convex function, when
When KL reaches a minimum value. To achieve sparsity limitation, a loss function is defined as:
wherein n is the number of samples, and n is the regularization coefficient, x ″iAnd beta is the coefficient of the sparsity limiting penalty term for the output value of the ith group of samples. The above formula consists of three parts, which are a mean square error term, a regularization term and a penalty term.
(4) Optimizing a cost loss function and a parameter w, b through a gradient descent algorithm:
wi，bithe weight and the offset of the ith data are respectively expressed, and alpha represents the learning rate. When a certain number of iterations is reached, hidden layer neurons obtain the learning characteristics of high-dimensional data, meanwhile, the sparse self-coding deep neural network has trained corresponding weight vectors w and bias vectors b, and the data x after dimensionality reduction is obtained by substituting w and b into the formula (1)h。
S3: is the data x after the dimension reductionhAdding tag data y to obtain channel TiTraining data set
Wherein
Representing the sample points, y, after S2 dimensionality reductioniAnd representing the category corresponding to the sample, taking the value of { -1, 1}, and representing that the security event corresponding to the channel occurs when the value is '1'. The AdaBoost algorithm continuously changes the weights of samples from training data, serially learns a series of weak learners, and linearly combines the weak learners into a strong learner. The method comprises the following specific steps:
(1)Dm＝(wm1，wm2，Λ，wmN) Representing the weight of the mth weak classifier sample, the weight of the initialized sample point is:
(2) for M ═ 1, 2, a, M, use is made of a weight DmTraining a weak learner Gm(x) The weak learners here can be neural networks, decision trees, etcThe principle and training process are not repeated.
(3) Weak learner G for calculationm(x) Classification error of (2):
i (t) is a general function, and when the parameter t is true, the output is 1; when the parameter t is false, the output is 0.
(4) By forward step-by-step algorithm, weak classifier Gm(x) The coefficients of (a) are:
(5) updating the weight distribution D of the m +1 th weak learner samplem+1＝(wm+1，1，wmn+1，2，Λ，wm+1，N)
Wherein Z ismIs a normalization factor, and the main function is to convert wmiNormalized to between 0 and 1, such that
(6) And (3) carrying out linear combination on all weak classifiers by using the coefficient weighting of each weak classifier to obtain a final strong classifier:
the specific steps of the AHP fusing the evaluation results of the plurality of channels to obtain the overall network security situation are as follows:
s1: a risk event influence value evaluation table is formulated based on a universal vulnerability scoring system (CVSS):
| index (I) | Degree of influence | Influence value |
| Confidentiality (C) | None (N)/Low (L)/high (H) | 0/0.22/0.56 |
| Integrity (I) | None (N)/Low (L)/high (H) | 0/0.22/0.56 |
| Availability (A) | None (N)/Low (L)/high (H) | 0/0.22/0.56 |
S2: determining each channel T by combining the table in S1iC, I, A values of corresponding security events, and calculating a channel TiInfluence value of (2):
s3: and combining the table in the S1, and constructing a paired comparison matrix of the channel by using a paired comparison method from three dimensions of confidentiality, integrity and availability:
wherein a isijRepresents a channel TiRelative to the channel TjThe degree of importance of.
S4: calculating a characteristic root lambda, and carrying out consistency check: aw ═ λ w
Defining a consistency index:
defining a consistency ratio:
wherein RI is a random consistency index, and when the consistency ratio CR is generally considered to be less than 0.1, the inconsistency degree of A is considered to be within an allowable range, and the A has satisfactory consistency and passes consistency check. Its normalized eigenvector can be used as the weight vector, otherwise it is reconstructed into the comparison matrix a.
S5: calculating a weight vector w by using the inspected characteristic root lambda, and weighting and fusing all channels to obtain the overall network security situation:
Claims (5)
1. a multi-channel SAE-AdaBoost-based power Internet of things network security situation assessment method is characterized by comprising the following steps:
step 1: network security situation index is divided into a plurality of channels T1，T2，Λ，Tn；
Step 2: for each channel, SAE maps each unlabeled training sample k-dimensional vector x' to m-dimensional coding vector x through the hidden layerhTo obtain high dimensional dataIs expressed as xh；
And step 3: for channel TiTraining data set
Wherein
Representing the sample points, x, after the dimension reduction of step 2hThe data after dimensionality reduction, x refers to index data after channel division, and yiRepresenting the category corresponding to the sample, taking the value as { -1, 1}, and representing that the security event corresponding to the channel occurs when the value is '1', wherein an AdaBoost algorithm continuously changes the weight of the sample from training data, serially learns a series of weak learners, linearly combines the weak learners into a strong learner, and uses the strong learner to evaluate the channel corresponding to the weak learner;
and 4, step 4: the AHP comprehensively considers the relative importance among the channels, and integrates the evaluation results of the multiple channels to obtain the overall network security situation.
2. The method for evaluating the network security situation of the power internet of things based on the multi-channel SAE-AdaBoost as claimed in claim 1, wherein the step 1 comprises the following steps:
the network security problem that affects the network security environment is divided into n channels: t is1，T2，Λ，Tn；
And dividing the overall situation index into the n channels according to the corresponding relation between each index and the safety problem.
3. The method for evaluating the network security situation of the power internet of things based on the multi-channel SAE-AdaBoost as claimed in claim 1, wherein the step 2 comprises the following steps:
s2.1: for each channel TiIndex data of
Normalizing by using a minimum-maximum standard method to obtain a channel TiThe size range of the index data is narrowed to [0, 1 ]]The method comprises the following steps:
s2.2: to channel TiAnd (3) encoding index data: mapping each unlabeled training sample k-dimensional vector x' into m-dimensional coded vector x through the hidden layerhTo obtain a low-dimensional representation x of the high-dimensional datah。
4. The method for evaluating the network security situation of the power internet of things based on the multi-channel SAE-AdaBoost as claimed in claim 1, wherein the step 3 specifically comprises:
step 3.1: dm＝(wm1，wm2，Λ，wmN) Representing the weight of the mth weak classifier sample, the weight of the initialized sample point is: d1＝(w11，w12，Λ，w1N)，
Step 3.2: for M ═ 1, 2, Λ, M, use with weight DmTraining a weak learner Gm(x)；
Step 3.3: weak learner G for calculationm(x) Classification error of (2):
step 3.4: by forward step-by-step algorithm, weak classifier Gm(x) The coefficients of (a) are:
step 3.5: update the weight distribution of the m +1 th weak learner sample:
Dm+1＝(wm+1，1，wm+1，2，Λ，wm+1，N)
wherein,
wherein,
wherein Z ismIs a normalization factor, and the main function is to convert wmiNormalized to between 0 and 1, such that
Step 3.6: and (3) carrying out linear combination on all weak classifiers by using the coefficient weighting of each weak classifier to obtain a final strong classifier:
5. the method for evaluating the network security situation of the power internet of things based on the multi-channel SAE-AdaBoost as claimed in claim 1, wherein the step 4 specifically comprises the following steps:
step 4.1: a risk event influence value evaluation table is formulated based on a universal vulnerability scoring system (CVSS):
Step 4.2: determining each channel T by combining the table in S1iC, I, A values of corresponding security events, and calculating a channel TiInfluence value of
Step 4.3: and combining the table in the S1, and constructing a paired comparison matrix of the channel by using a paired comparison method from three dimensions of confidentiality, integrity and availability:
wherein a isijRepresents a channel TiRelative to the channel TjThe degree of importance of;
step 4.4: calculating a characteristic root lambda, and carrying out consistency check: aw ═ λ w
Defining a consistency index:
n represents the number of channels;
defining a consistency ratio:
wherein RI is a random consistency index, when the consistency ratio CR is less than 0.1, the inconsistency degree of A is within an allowable range and has satisfactory consistency, and the normalized characteristic vector is used as a weight vector through consistency test, otherwise, a comparison matrix A is reconstructed;
step 4.5: calculating a weight vector w by using the inspected characteristic root lambda, and weighting and fusing all channels to obtain the overall network security situation:
wherein, I (t) is a general function, when the parameter t is true, the output is 1; when the parameter t is false, the output is 0,
meaning that Ti corresponds to a strong classifier.
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