	US 12,107,749 B2
	Adaptive network probing using machine learning
Armin Sarabi, Ann Arbor, MI (US); Mingyan Liu, Ann Arbor, MI (US); Kun Jin, Ann Arbor, MI (US); and Tongxin Yin, Ann Arbor, MI (US)
Assigned to The Regents of The University of Michigan, Ann Arbor, MI (US)
Appl. No. 18/033,834
Filed by THE REGENTS OF THE UNIVERSITY OF MICHIGAN, Ann Arbor, MI (US)
PCT Filed Oct. 25, 2021, PCT No. PCT/US2021/056465 § 371(c)(1), (2) Date Apr. 26, 2023, PCT Pub. No. WO2022/093697, PCT Pub. Date May 5, 2022.
Claims priority of provisional application 63/105,492, filed on Oct. 26, 2020.
Prior Publication US 2023/0403225 A1, Dec. 14, 2023
Int. Cl. H04L 43/12 (2022.01); G06N 20/20 (2019.01); H04L 41/147 (2022.01); H04L 41/16 (2022.01)

CPC H04L 43/12 (2013.01) [G06N 20/20 (2019.01); H04L 41/147 (2013.01); H04L 41/16 (2013.01)]

17 Claims

1. A computer-implemented method for scanning a computer network, comprising:

a) sending, by a computer processor, a particular network probe to a particular port of a network address in a computer network;

b) receiving, by the computer processor, a response to the particular network probe from the network address;

c) appending, the computer processor, the response for the particular port to a set of features forming a feature vector associated with the network address, where the feature vector includes at least two features and each feature of the at least two features indicates status of a different port at the network address;

d) determining, by the computer processor, a next port to probe at the network address, where the next port is selected dynamically according to the set of features from the feature vector; and

e) predicting, by the computer processor, the response of the next port using the feature vector and a model, where the model is trained using a machine learning method and outputs a probability that the next port will respond to a given network probe in a desired manner and the next port differs from the particular port.