Network data processing driver for a cognitive artifical intelligence system
Techniques are disclosed for processing data collected from network components for analysis by a machine learning engine of a Cognitive AI System. A network data processing driver receives a stream of data from a data collector which obtains data from one or more network data sources. The driver normalizes the stream of data to one or more feature values each corresponding to the network data sources and generates a sample vector from the feature values. The sample vector is formatted to be analyzed by the machine learning engine.
1. A computer-implemented method to generate a logical network topology for a network, the method comprising:
receiving a first stream of data from a data collector within a network, the first stream of data obtained from a data source within the network;
identifying a packet header from the first stream of data and extracting address information from the packet header;
separating the extracted address information into multiple components and converting the separated address information components to feature values;
normalizing the feature values, the feature values associated with the data source;
generating, via a neuro-linguistic model, patterns associated with the normalized feature values and related to network traffic attributes;
generating statistics from the patterns;
generating a logical network topology based on the generated statistics;
generating a context-aware description of network activity based at least in part on a mapping of the normalized feature values in the logical network topology to corresponding network components;
detecting an anomaly in the network activity based on the patterns, wherein the anomaly is associated with a network attack;
generating an alert signal in response to detecting the anomaly, the alert signal representing a context-aware description of the anomaly; and
sending the alert signal.
2. The computer-implemented method of claim 1 , wherein the address information includes at least one of:
source Media Access Control (MAC) address;
destination MAC address;
network address; and
data payload.
3. The computer-implemented method of claim 1 , wherein at least one of the feature values includes a value between 0 and 1.
4. The computer-implemented method of claim 1 , further comprising:
receiving a second stream of data from the data collector; and
updating the logical network topology based on the second stream of data.
5. The computer-implemented method of claim 4 , wherein updating the logical network topology includes:
extracting at least one feature value of the feature values from the second stream of data;
updating, via the neuro-linguistic model, patterns associated with the extracted at least one feature value; and
updating the statistics based on the updated patterns associated with the extracted at least one feature value.
6. The computer-implemented method of claim 1 , further comprising:
generating connectivity patterns representing network traffic attributes for the network.
7. The computer-implemented method of claim 1 , further comprising:
converting the alert signal representing the anomaly to human-readable description of the anomaly data.
8. The computer-implemented method of claim 1 , further comprising:
determining whether to analyze the network attack.
9. The computer-implemented method of claim 1 , wherein the network attack is one of: a distributed denial of service (DDoS) attack, an Internet Control Message Protocol (ICMP) flood attack, or network address spoofing.
10. The computer-implemented method of claim 1 , further comprising:
learning, via a machine learning engine, an association between at least one of the patterns and the network attack; and
analyzing the anomaly to determine that the anomaly is associated with the network attack.
11. The computer-implemented method of claim 10 , wherein the network attack is one of: a DDoS attack, an ICMP flood attack, or network address spoofing.