Category: graph

Lacework Intrusion Detection System – Cloud IDS

Lacework Polygraph is a Host based IDS for cloud workloads. It provides a graphical view of who did what on which system, reducing the time for root cause analysis for anomalies in system behaviors. It can analyze workloads on AWS, Azure and GCP.

It installs a lightweight agent on each target system which aggregates information from processes running on the system into a centralized customer specific (MT) data warehouse (Snowflake on AWS)  and then analyzes the information using machine learning to generate behavioral profiles and then looks for anomalies from the baseline profile. The design allows automating analysis of common attack scenarios using ssh, privilege changes, unauthorized access to files.

The host based model gives detailed process information such as which process talked to which other and over what api. This info is not available to a network IDS. The behavior profiles reduce the false positive rates. The graphical view is useful to drill down into incidents.

It does not have an IPS functionality – as false positives with an IPS could negatively affect the system.

Cloud based network isolation tools like Aviatrix might make IPS scenarios feasible by limiting the effect of an IPS.

Nvidia nvGraph and Tesla P100 GPU

“The latest version of NVIDIA’s parallel computing platform gives developers direct access to powerful new Pascal features, including unified memory and NVLink. Also included in this release is a new graph analytics library — nvGRAPH — which can be used for robotic path planning, cyber security and logistics analysis, expanding the application of GPU acceleration into the realm of big data analytics.”

nvGRAPH supports three widely-used algorithms:

Page Rank is most famously used in search engines, and also used in social network analysis, recommendation systems, and for novel uses in natural science when studying the relationship between proteins and in ecological networks.

Single Source Shortest Path is used to identify the fastest path from A to B through a road network, and can also be used for a optimizing a wide range of other logistics problems.

Single Source Widest Path is used in domains like IP traffic routing and traffic-sensitive path planning.

In addition, the nvGRAPH semiring Sparse Matrix Vector Multiplication (SPMV) operations can be used to build a wide range of innovative graph traversal algorithms.

A paper on how to represent cyber attacks as graphs – http://csis.gmu.edu/noel/pubs/2015_IEEE_HST.pdf references the CAPEC, which is a collection of vulnerabilities for such a graph study.

A graphdb represents data using nodes, edges and properties.

graphdatabase_propertygraph

It allows querying with a graph traversal language such as Gremlin. Blazegraph offers a GPU accelerated graphdb. Here’s a graph of machine learning papers from Research Front Maps.

rfmap1

MapD, a GPU accelerated db recently got funding from Nvidia and others.