SYSTEM AND METHOD FOR COMPUTER NETWORKS ENDPOINT THREAT PREDICTION BASED ON VECTOR EMBEDDING
Systems and methods of detecting communication anomalies in a computer network, including: analyzing sampled traffic within the computer network, to identify at least one entity in the computer network, generating a network graph that corresponds to the computer network, wherein the network graph includes a plurality of nodes based on the identified at least one entity, training a deep learning (DL) algorithm to generate at least one vector characterizing the behavior of each entity in the computer network based on the generated network graph, applying the trained DL algorithm on the sampled traffic, to predict the probability of a communication in the sampled traffic, wherein the prediction is based on the generated at least one vector, and detecting an anomaly when the predicted probability is below an anomaly threshold.
1 . A method of detecting communication anomalies in a computer network, the method comprising:
analyzing, by a processor in communication with the computer network, sampled traffic within the computer network, to identify at least one entity in the computer network;
generating, by the processor, a network graph that corresponds to the computer network, wherein the network graph comprises a plurality of nodes based on the identified at least one entity;
training, by the processor, a deep learning (DL) algorithm to generate at least one vector characterizing the behavior of each entity in the computer network based on the generated network graph;
applying, by the processor, the trained DL algorithm on the sampled traffic, to predict the probability of a communication in the sampled traffic, wherein the prediction is based on the generated at least one vector; and
detecting, by the processor, an anomaly when the predicted probability is below an anomaly threshold.
2 . The method of claim 1 , wherein the generated network graph comprises a graph neural network (GNN) architecture.
3 . The method of claim 1 , further comprising clustering the generated at least one vector to identify groups of related behaving IP addresses in the network.
4 . The method of claim 1 , wherein the DL algorithm comprises a link predictor model.
5 . The method of claim 1 , wherein the DL algorithm is to predict probability of any communication within the computer network.
6 . The method of claim 1 , wherein the generated at least one vector corresponds to at least one of an IP address and a port-protocol-port tuple (PPP).
7 . The method of claim 1 , wherein the network graph is generated based on non-malicious training data of network samples without anomalies.
8 . The method of claim 1 , further comprising blocking communication with a network entity associated with the at least one vector.
9 . A system for detection of communication anomalies in a computer network, the system comprising:
a memory, to store a training dataset; and
a processor, in communication with the computer network, wherein the processor is configured to:
analyze sampled traffic within the computer network, to identify at least one entity in the computer network;
generate a network graph that corresponds to the computer network, wherein the network graph comprises a plurality of nodes based on the identified at least one entity;
train a deep learning (DL) algorithm to generate at least one vector characterizing the behavior of each entity in the computer network based on the generated network graph, and based on the training dataset;
apply the trained DL algorithm on the sampled traffic, to predict the probability of a communication in the sampled traffic, wherein the prediction is based on the generated at least one vector; and
detect an anomaly when the predicted probability is below an anomaly threshold.
10 . The system of claim 9 , wherein the generated network graph comprises a graph neural network (GNN) architecture.
11 . The system of claim 9 , wherein the processor is configured to cluster the generated at least one vector to identify groups of related behaving IP addresses in the network.
12 . The system of claim 9 , wherein the DL algorithm comprises a link predictor model.
13 . The system of claim 9 , wherein the processor is configured to predict probability of any communication within the computer network.
14 . The system of claim 9 , wherein the generated at least one vector corresponds to at least one of an IP address and a port-protocol-port tuple (PPP).
15 . The system of claim 9 , wherein the network graph is generated based on non-malicious training data of network samples without anomalies.
16 . The system of claim 9 , wherein the processor is configured to block communication with a network entity associated with the at least one vector.