Forecasting and classifying cyber-attacks using analytical data based neural embeddings

• US 9,948,666 B2
• Filed: 02/09/2016
• Issued: 04/17/2018
• Est. Priority Date: 02/09/2016
• Status: Active Grant

• Current Assignee: Kyndryl Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Ahmed, Mohamed N., Baughman, Aaron K., Behnken, John F., Marzorati, Mauro
• Primary Examiner(s): Lipman, Jacob
• Application Number:
  US15/019,270
• Publication Number:
  US 20170230400A1
• Time in Patent Office: 798 Days
• Field of Search: 726 25
• CPC Class Codes: G06N 3/042 Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G06N 3/086 using evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: H04L 63/1408 by monitoring network traffic monitoring network traffic per se H04L43/00
• CPC Class Codes: H04L 63/1433 Vulnerability analysis

Abstract

A first collection including an analytical feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over at least one of vectors of the second collection with a corresponding vector of a fourth collection, migrating at least one of the vectors of the second collection with a corresponding vector of a fifth collection. Using a forecasting configuration, an analytical feature vector of the third collection is aged to generate a changed analytical feature vector containing analytical feature values expected at a future time. The changed analytical feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.

Claims

1. A method comprising:

constructing a first collection, the first collection comprising an analytical feature vector and a Q&

A feature vector;

constructing a second collection from the first collection by inserting noise data in at least one of the analytical feature vector and the Q&

A feature vector;

further constructing a third collection by using at least one of (i) combining, to crossover, at least one of an analytical feature vector and a Q&

A feature vector of the second collection with a corresponding at least one of an analytical feature vector and a Q&

A feature vector of a fourth collection, wherein the second and the fourth collections have a property similar to one another, and (ii) combining, to migrate, at least one of an analytical feature vector and a Q&

A feature vector of the second collection with a corresponding at least one of an analytical feature vector and a Q&

A feature vector of a fifth collection, wherein the second and the fifth collections have a property distinct from one another;

aging, using a forecasting configuration, an analytical feature vector of the third collection to generate a changed analytical feature vector, the changed analytical feature vector containing analytical feature values expected at a future time; and

predicting, by inputting the changed analytical feature vector in a trained neural network, a probability of a cyber-attack occurring at the future time.

2. The method of claim 1, the combining to crossover further comprising:

partitioning the analytical feature vector of the second collection into a first partition of a first size and second partition of a second size;

partitioning the analytical feature vector of the fourth collection into a first partition of the first size and second partition of the second size; and

constructing the analytical feature vector of the third collection by substituting the first partition in the analytical feature vector of the second collection with the first partition of the analytical feature vector of the fourth collection.

3. The method of claim 1, the combining to migrate further comprising:

partitioning the analytical feature vector of the second collection into a first partition of a first size and second partition of a second size;

partitioning the analytical feature vector of the fifth collection into a first partition of the first size and second partition of the second size; and

constructing the analytical feature vector of the third collection by substituting the first partition in the analytical feature vector of the second collection with the first partition of the analytical feature vector of the fifth collection.

4. The method of claim 1, further comprising:

further predicting, using the trained neural network, a classification of the cyber-attack occurring at the future time.

5. The method of claim 1, further comprising:

aging using a second forecasting configuration, a Q&

A feature vector of the third collection to generate a changed Q&

A feature vector, the changed Q&

A feature vector containing Q&

A feature values expected at the future time, wherein the predicting also inputs the changed Q&

A feature vector in the trained neural network.

6. The method of claim 1, further comprising:

evaluating the property of the second collection, wherein the third collection has an increased value of the property.

7. The method of claim 1, wherein the noise is inserted by adding a random value to the analytical feature vector.

8. The method of claim 1, wherein the noise is inserted by changing an existing value in the analytical feature vector by a random amount.

9. The method of claim 1, wherein the noise is inserted by deleting an existing value from the analytical feature vector.

10. The method of claim 1, further comprising:

extracting a set of Q&

A features from the answer, a Q&

A feature in the set of Q&

A features being data with the characteristic;

creating a set of expanded Q&

A features from the set of Q&

A features; and

adding, to form the Q&

A feature vector, the set of Q&

A features, the set of expanded Q&

A features, and a timestamp corresponding to a time of collection of the raw data.

11. The method of claim 1, wherein the method is embodied in a computer program product comprising one or more computer-readable storage devices and computer-readable program instructions which are stored on the one or more computer-readable tangible storage devices and executed by one or more processors.

12. The method of claim 1, wherein the method is embodied in a computer system comprising one or more processors, one or more computer-readable memories, one or more computer-readable storage devices and program instructions which are stored on the one or more computer-readable storage devices for execution by the one or more processors via the one or more memories and executed by the one or more processors.

13. The method of claim 1, further comprising:

constructing, from the first portion, NL corpora; and

submitting the NL question against the NL corpora using a Q&

A system, wherein the Q&

A system produces the answer corresponding to the NL question based on the NL corpora.

14. The method of claim 13, wherein the answer comprises a ranked list of sub-portions in the first portion, wherein a higher ranking sub-portion is more relevant in answering the NL question than a lower ranking sub-portion.

15. The method of claim 1, further comprising:

creating the analytical feature vector from raw analytical data present in a data processing environment;

identifying in the analytical feature vector, a first portion, wherein the first portion is suitable for natural language processing (NLP);

constructing, from the first portion, a natural language (NL) question, the NL question being related to a future cyber-attack on the data processing environment;

constructing the Q&

A feature vector based on a set of features present in an answer to the NL question.

16. The method of claim 15, further comprising:

extracting a set of raw analytical features from the raw analytical data, a raw analytical feature in the set of raw analytical features being data with a characteristic, the characteristic being usable in detection of the cyber-attack; and

creating a set of expanded analytical features from the set of raw analytical features; and

adding, to form the analytical feature vector, the set of raw analytical features, the set of expanded analytical features, and a timestamp corresponding to a time of collection of the raw analytical data.

17. The method of claim 16, further comprising:

normalizing, as a part of creating the set of expanded analytical features, a raw analytical feature in the set of raw analytical features to form an extended analytical feature in the set of expanded analytical features.

18. The method of claim 16, further comprising:

deriving, as a part of creating the set of expanded analytical features, an expanded analytical feature in the set of expanded analytical features from a raw analytical feature in the set of raw analytical features.

19. A computer program product comprising one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices, the stored program instructions comprising:

program instructions to construct a first collection, the first collection comprising an analytical feature vector and a Q&

A feature vector;

program instructions to construct a second collection from the first collection by inserting noise data in at least one of the analytical feature vector and the Q&

A feature vector;

program instructions to further construct a third collection by using at least one of (i) combining, to crossover, at least one of an analytical feature vector and a Q&

A feature vector of the second collection with a corresponding at least one of an analytical feature vector and a Q&

A feature vector of a fourth collection, wherein the second and the fourth collections have a property similar to one another, and (ii) combining, to migrate, at least one of an analytical feature vector and a Q&

A feature vector of the second collection with a corresponding at least one of an analytical feature vector and a Q&

A feature vector of a fifth collection, wherein the second and the fifth collections have a property distinct from one another;

program instructions to age, using a forecasting configuration, an analytical feature vector of the third collection to generate a changed analytical feature vector, the changed analytical feature vector containing analytical feature values expected at a future time; and

program instructions to predict, by inputting the changed analytical feature vector in a trained neural network, a probability of a cyber-attack occurring at the future time.

20. A computer system comprising one or more processors, one or more computer-readable memories, and one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions comprising:

program instructions to construct a first collection, the first collection comprising an analytical feature vector and a Q&

A feature vector;

program instructions to construct a second collection from the first collection by inserting noise data in at least one of the analytical feature vector and the Q&

A feature vector;

program instructions to further construct a third collection by using at least one of (i) combining, to crossover, at least one of an analytical feature vector and a Q&

A feature vector of the second collection with a corresponding at least one of an analytical feature vector and a Q&

A feature vector of a fourth collection, wherein the second and the fourth collections have a property similar to one another, and (ii) combining, to migrate, at least one of an analytical feature vector and a Q&

A feature vector of the second collection with a corresponding at least one of an analytical feature vector and a Q&

A feature vector of a fifth collection, wherein the second and the fifth collections have a property distinct from one another;

program instructions to age, using a forecasting configuration, an analytical feature vector of the third collection to generate a changed analytical feature vector, the changed analytical feature vector containing analytical feature values expected at a future time; and

program instructions to predict, by inputting the changed analytical feature vector in a trained neural network, a probability of a cyber-attack occurring at the future time.