Estimation device, estimation method, and estimation program
An estimation device includes processing circuitry configured to generate a feature quantity by performing invertible transformation on a payload of a packet character by character with respect to each packet determined to be abnormal or normal by an abnormality detector, and give a determination result as to whether a packet is abnormal or normal to the generated feature quantity, learn a model that classifies whether the packet is abnormal or normal by machine learning using the feature quantity of the payload of the packet and the determination result as to whether the packet is abnormal or normal as teacher data, extract a number of dimensions of the feature quantity in which a contribution degree to classification is equal to or greater than a predetermined value in the learned model, and estimate a cause part of abnormality in the payload of the packet determined to be abnormal.
1 . An estimation device comprising:
processing circuitry configured to:
generate a feature quantity by performing invertible transformation on a payload of a packet character by character with respect to each packet determined to be abnormal or normal by an abnormality detector, and give a determination result as to whether a packet is abnormal or normal to the generated feature quantity;
learn a model that classifies whether the packet is abnormal or normal by machine learning using the feature quantity of the payload of the packet and the determination result as to whether the packet is abnormal or normal as teacher data;
extract a number of dimensions of the feature quantity in which a contribution degree to classification is equal to or greater than a predetermined value in the learned model; and
estimate a cause part of abnormality in the payload of the packet determined to be abnormal using the extracted number of dimensions of the feature quantity, and output a result of estimation.
2 . The estimation device according to claim 1 , wherein
the model is a model using a decision tree, and
the processing circuitry is further configured to:
extract the number of dimensions of the feature quantity written in a branch condition from a node in which the branch condition in the decision tree obtained by the machine learning is written as the number of dimensions of the feature quantity in which the contribution degree is equal to or greater than a predetermined value.
3 . The estimation device according to claim 1 , wherein
the model is a model using linear regression or logistic regression.
4 . The estimation device according to claim 1 , wherein
the processing circuitry is further configured to:
specify a part estimated as a cause part of abnormality in the payload of the packet determined to be abnormal based on the extracted number of dimensions of the feature quantity, and outputs information obtained by visualizing the specified part as a result of the estimation.
5 . The estimation device according to claim 1 , wherein
the invertible transformation transforms a character string into a numeric string according to ASCII code table.
6 . The estimation device according to claim 1 , wherein the model is a model using a decision tree.
7 . The estimation device according to claim 1 , wherein the model is a model using linear regression.
8 . The estimation device according to claim 1 , wherein the model is a model using logistic regression.
9 . The estimation device according to claim 1 , wherein
the processing circuitry is further configured to:
specify a part estimated as a cause part of abnormality in the payload of the packet determined to be abnormal based on the extracted number of dimensions of the feature quantity.
10 . An estimation method comprising:
generating a feature quantity by performing invertible transformation on a payload of a packet character by character with respect to each packet determined to be abnormal or normal by an abnormality detector, and giving a determination result as to whether a packet is abnormal or normal to the generated feature quantity;
learning a model that classifies whether the packet is abnormal or normal by machine learning using the feature quantity of the payload of the packet and the determination result as to whether the packet is abnormal or normal as teacher data;
extracting a number of dimensions of the feature quantity in which a contribution degree to classification is equal to or greater than a predetermined value in the learned model; and
estimating a cause part of abnormality in the payload of the packet determined to be abnormal using the extracted number of dimensions of the feature quantity, and outputting a result of estimation.
11 . The estimation method according to claim 10 , wherein the model is a model using a decision tree.
12 . The estimation method according to claim 10 , wherein the model is a model using linear regression.
13 . The estimation method according to claim 10 , wherein the model is a model using logistic regression.
14 . A non-transitory computer-readable recording medium storing therein an estimation program that causes a computer to execute a process comprising:
generating a feature quantity by performing invertible transformation on a payload of a packet character by character with respect to each packet determined to be abnormal or normal by an abnormality detector, and giving a determination result as to whether a packet is abnormal or normal to the generated feature quantity;
learning a model that classifies whether the packet is abnormal or normal by machine learning using the feature quantity of the payload of the packet and the determination result as to whether the packet is abnormal or normal as teacher data;
extracting a number of dimensions of the feature quantity in which a contribution degree to classification is equal to or greater than a predetermined value in the learned model; and
estimating a cause part of abnormality in the payload of the packet determined to be abnormal using the extracted number of dimensions of the feature quantity, and outputting a result of estimation.