Detecting unmanaged and unauthorized assets in an information technology network with a recurrent neural network that identifies anomalously-named assets
The present disclosure describes a system, method, and computer program for detecting unmanaged and unauthorized assets on an IT network by identifying anomalously-named assets. A recurrent neural network (RNN) is trained to identify patterns in asset names in a network. The RNN learns the character distribution patterns of the names of all observed assets in the training data, effectively capturing the hidden naming structures followed by a majority of assets on the network. The RNN is then used to identify assets with names that deviate from the hidden naming structures. Specifically, the RNN is used to measure the reconstruction errors of input asset name strings. Asset names with high reconstruction errors are anomalous since they cannot be explained by learned naming structures. After filtering for attributes or circumstances that mitigate risk, such assets are associated with a higher cybersecurity risk.
1. A method, performed by a computer system, for anomalous asset name detection in an IT computer network, the method comprising:
creating a set of input vectors representative of an asset name in the IT network, each vector in the set corresponding to a character in the asset name;
applying the set of input vectors to a recurrent neural network comprising an encoder and a decoder, wherein the recurrent neural network is trained to identify patterns in asset names in the IT network;
using the encoder to compress the set of input vectors to a single latent vector that is representative of the asset name and that is generated based on patterns in asset names in the IT network learned by the recurrent neural network during training;
applying the latent vector to the decoder as an initial state of the decoder to reconstruct the asset name one character at a time;
receiving the reconstructed asset name;
determining a degree of reconstruction error between the reconstructed asset name and the asset name;
determining whether the degree of reconstruction error is above a threshold;
in response to determining that the degree of reconstruction error is above a threshold, flagging the asset name as being anomalous;
applying one or more filter criteria to assets flagged as having an anomalous name, wherein applying the one or more filter criteria comprises:
identifying a peer asset group associated with an asset having a flagged asset name, wherein the peer group is identified based on IP addresses of assets,
determining whether a prefix or suffix in the flagged asset name is common to the peer group, and
in response to determining that the prefix or suffix of the flagged asset name is common to the peer group, concluding that the asset satisfies the filter criteria;
filtering out assets that satisfy the filter criteria; and
providing an indication of an elevated cybersecurity risk for at least a subset of remaining flagged assets that pass filtering.
2. The method of claim 1 , wherein applying the filter criteria comprises:
identifying a number of users using an asset having a flagged asset name; and
in response to the number being above a threshold, concluding that the asset satisfies the filter criteria.
3. The method of claim 1 , wherein providing an indication of an elevated cybersecurity risk comprises displaying flagged asset names that pass filtering in a user interface for administrative review.
4. The method of claim 1 , wherein providing an indication of an elevated cybersecurity risk comprises increasing a risk score of a user session using a flagged asset that passed filtering.
5. The method of claim 1 , wherein the set of input vectors is created using one-hot encoding.
6. The method of claim 1 , wherein the recurrent neural network is a bidirectional recurrent neural network.
7. The method of claim 1 , wherein the recurrent neural network is a seq2seq LSTM.
8. The method of claim 7 , wherein the asset name is reconstructed one character at a time with teacher forcing method in which the set of input vectors, offset by one time step, is applied to the decoder.
9. A non-transitory computer-readable medium comprising a computer program, that, when executed by a computer system, enables the computer system to perform the following method for anomalous asset name detection in an IT computer network, the method comprising:
creating a set of input vectors representative of an asset name in the IT network, each vector in the set corresponding to a character in the asset name;
applying the set of input vectors to a recurrent neural network comprising an encoder and a decoder, wherein the recurrent neural network is trained to identify patterns in asset names in the IT network;
using the encoder to compress the set of input vectors to a single latent vector that is representative of the asset name and that is generated based on patterns in asset names in the IT network learned by the recurrent neural network during training;
applying the latent vector to the decoder as an initial state of the decoder to reconstruct the asset name one character at a time;
receiving the reconstructed asset name;
determining a degree of reconstruction error between the reconstructed asset name and the asset name;
determining whether the degree of reconstruction error is above a threshold;
in response to determining that the degree of reconstruction error is above a threshold, flagging the asset name as being anomalous;
applying one or more filter criteria to assets flagged as having an anomalous name, wherein applying the one or more filter criteria comprises:
identifying a peer asset group associated with an asset having a flagged asset name, wherein the peer group is identified based on IP addresses of assets,
determining whether a prefix or suffix in the flagged asset name is common to the peer group, and
in response to determining that the prefix or suffix of the flagged asset name is common to the peer group, concluding that the asset satisfies the filter criteria;
filtering out assets that satisfy the filter criteria; and
providing an indication of an elevated cybersecurity risk for at least a subset of remaining flagged assets that pass filtering.
10. The non-transitory computer-readable medium of claim 9 , wherein applying the filter criteria comprises:
identifying a number of users using an asset having a flagged asset name; and
in response to the number being above a threshold, concluding that the asset satisfies the filter criteria.
11. The non-transitory computer-readable medium of claim 9 , wherein providing an indication of an elevated cybersecurity risk comprises displaying flagged asset names that pass filtering in a user interface for administrative review.
12. The non-transitory computer-readable medium of claim 9 , wherein providing an indication of an elevated cybersecurity risk comprises increasing a risk score of a user session using a flagged asset that passed filtering.
13. The non-transitory computer-readable medium of claim 9 , wherein the set of input vectors is created using one-hot encoding.
14. The non-transitory computer-readable medium of claim 9 , wherein the recurrent neural network is a bidirectional recurrent neural network.
15. The non-transitory computer-readable medium of claim 9 , wherein the recurrent neural network is a seq2seq LSTM.
16. The non-transitory computer-readable medium of claim 15 , wherein the asset name is reconstructed one character at a time with teacher forcing method in which the set of input vectors, offset by one time step, is applied to the decoder.
17. A computer system for anomalous asset name detection in an IT computer network, the system comprising:
one or more processors;
one or more memory units coupled to the one or more processors, wherein the one or more memory units store instructions that, when executed by the one or more processors, cause the system to perform the operations of:
creating a set of input vectors representative of an asset name in the IT network, each vector in the set corresponding to a character in the asset name;
applying the set of input vectors to a recurrent neural network comprising an encoder and a decoder, wherein the recurrent neural network is trained to identify patterns in asset names in the IT network;
using the encoder to compress the set of input vectors to a single latent vector that is representative of the asset name and that is generated based on patterns in asset names in the IT network learned by the recurrent neural network during training;
applying the latent vector to the decoder as an initial state of the decoder to reconstruct the asset name one character at a time;
receiving the reconstructed asset name;
determining a degree of reconstruction error between the reconstructed asset name and the asset name;
determining whether the degree of reconstruction error is above a threshold;
in response to determining that the degree of reconstruction error is above a threshold, flagging the asset name as being anomalous;
applying one or more filter criteria to assets flagged as having an anomalous name, wherein applying the one or more filter criteria comprises:
identifying a peer asset group associated with an asset having a flagged asset name, wherein the peer group is identified based on IP addresses of assets,
determining whether a prefix or suffix in the flagged asset name is common to the peer group, and
in response to determining that the prefix or suffix of the flagged asset name is common to the peer group, concluding that the asset satisfies the filter criteria;
filtering out assets that satisfy the filter criteria; and
providing an indication of an elevated cybersecurity risk for at least a subset of remaining flagged assets that pass filtering.