Systems and methods for intelligent machine learning-based malware detection
The methods described herein include receiving a plurality of packets associated with a file, each of the plurality of packets comprising content, and a source domain; extracting one or more features from content of a first packet of the plurality of packets; applying a trained machine learning model to the extracted one or more features to determine a probability of maliciousness associated with the first packet; responsive to determining that the probability maliciousness of the first packet is between a first threshold value and a second threshold value, labeling the first packet as having an uncertain maliciousness; extracting one or more features from content of a second packet of the plurality of packets; and applying the trained machine learning model to the extracted one or more features of the first packet and the second packet to determine a probability of maliciousness associated with the second packet.
1. A non-transitory computer-readable storage medium having computer-readable code stored thereon for programming at least one processor to perform steps of:
receiving a plurality of packets associated with a file, each of the plurality of packets comprising content, and a source domain;
extracting one or more features from content of a first packet of the plurality of packets;
applying a trained machine learning model to the extracted one or more features to determine a probability of maliciousness associated with the first packet;
responsive to determining that the probability maliciousness of the first packet is between a first threshold value and a second threshold value, labeling the first packet as having an uncertain maliciousness;
extracting one or more features from content of a second packet of the plurality of packets; and
applying the trained machine learning model to the extracted one or more features of the first packet and the second packet to determine a probability of maliciousness associated with the second packet.
2. The non-transitory computer-readable storage medium of claim 1 , wherein the steps further comprise:
responsive to labeling the first packet as having an uncertain maliciousness, storing the first packet and its one or more features.
3. The non-transitory computer-readable storage medium of claim 1 , wherein the steps further comprise:
converting the content of the first packet and second packet of the plurality of packets into a digital representation.
4. The non-transitory computer-readable storage medium of claim 3 , wherein the digital representation is any of a decimal representation, a binary representation, a hexadecimal representation, a tokenized script, and a tokenized domain.
5. The non-transitory computer-readable storage medium of claim 1 , wherein the steps further comprise:
labeling the file based on the probability of maliciousness.
6. The non-transitory computer-readable storage medium of claim 1 , wherein the file type is one of a portable executable (PE) file, a portable document format (PDF) file, a Dynamic Loaded Library (DLL), a JavaScript (JS) file, a Hypertext Markup Language (HTML) file, and a Microsoft Office File.
7. The non-transitory computer-readable storage medium of claim 1 , wherein the trained machine learning model comprises one or more decision trees.
8. The non-transitory computer-readable storage medium of claim 1 , wherein the one or more features include n-gram features.
9. The non-transitory computer-readable storage medium of claim 1 , wherein the one or more features include an entropy feature.
10. The non-transitory computer-readable storage medium of claim 1 , wherein the one or more features include a domain feature.
11. A method comprising steps of:
receiving a plurality of packets associated with a file, each of the plurality of packets comprising content, and a source domain;
extracting one or more features from content of a first packet of the plurality of packets;
applying a trained machine learning model to the extracted one or more features to determine a probability of maliciousness associated with the first packet;
responsive to determining that the probability maliciousness of the first packet is between a first threshold value and a second threshold value, labeling the first packet as having an uncertain maliciousness;
extracting one or more features from content of a second packet of the plurality of packets; and
applying the trained machine learning model to the extracted one or more features of the first packet and the second packet to determine a probability of maliciousness associated with the second packet.
12. The method of claim 11 , wherein the steps further comprise:
responsive to labeling the first packet as having an uncertain maliciousness, storing the first packet and its one or more features.
13. The method of claim 11 , wherein the steps further comprise:
converting the content of the first packet and second packet of the plurality of packets into a digital representation.
14. The method of claim 13 , wherein the digital representation is any of a decimal representation, a binary representation, a hexadecimal representation, a tokenized script, and a tokenized domain.
15. The method of claim 11 , wherein the steps further comprise:
labeling the file based on the probability of maliciousness.
16. The method of claim 11 , wherein the file type is one of a portable executable (PE) file, a portable document format (PDF) file, a Dynamic Loaded Library (DLL), a JavaScript (JS) file, a Hypertext Markup Language (HTML) file, and a Microsoft Office File.
17. The method of claim 11 , wherein the trained machine learning model comprises one or more decision trees.
18. The method of claim 11 , wherein the one or more features include n-gram features.
19. The method of claim 11 , wherein the one or more features include an entropy feature.
20. The method of claim 11 , wherein the one or more features include a domain feature.