Version 2.22.7 streamlines use with Gremlin/TinkerPop (CosmosDB, Neptune, JanusGraph, …), helps you turn Jupyter Notebooks into dashboards, and adds more URL API parameters. Read on to learn more about those
Version 2.22 makes life better for both new and existing users. We’re especially excited about introducing TigerGraph and SQL support, and the continued progress with the 2.0 engine. Read on to learn more about those, and see full release notes at our new release notes page .
It feels likes eye-popping times for those deep into building the future of visual data experiences. With Looker exiting (-> Google for $3B), Tableau exiting (->SalesForce for $16B), and less public, Periscope & ZoomData exiting, the Graphistry team is experiencing good feelings and key reflections. One of them is… the $16B exits are just a prelude to the next $160B in opportunities.
Threat Hunting Masterclass: Three data science notebooks for finding bad actors in your network logs
Welcome to the inaugural Graphistry masterclass! In our first session, we dig into hunting over encrypted network activity with Zeek logs, Graphistry visualization, and Jupyter Notebooks with special guest Chit from Corelight.
Graphistry makes it easy to explore the hidden connections in any CSV or flat file by automatically exposing the underlying graph. This tutorial walks through the CSV Mini-App notebook that comes with Graphistry and applies it to visualizing the recent Implant Files medical device recalls database by the ICIJ.
When everything runs on GPUs, we can fundamentally shift the way we experience data analysis much like video moving to HD or shifting from black-and-white to color. What if you could load your full dataset, ask whole-table questions like what are the patterns, and get the answers… immediately? What if you could do that visually, replacing writing queries with simple infinite zoom and direct manipulations down to the level of individual data points? Core analytics areas like security, fraud, operations, and customer 360 are entering this sci-fi-level world of rapid hypothesis iteration.
Running analytics end-to-end on GPUs, all the way from the data warehouse to what’s on screen in your browser, is not easy. Graphistry first brought that experience to investigating event and graph data. Starting from before the Rapids team was even officially formed, we have been collaborating with them on how to get these techniques into the hands of all analysts. With the official project announcement of Rapids, we thought it would help to share our promising early experiences.
Enter Apache Arrow & GoAi
RAPIDS is one of NVIDIA’s biggest contributions to the GPU Open Analytics Initiative (GoAi), and is poised to become its computational backbone. (We previously overviewed GoAi for the web and visual analytics.) Big data framework developers are shifting to fast data — handling more data at millisecond levels. Similar to how many SQL analytics tasks moved to distributed Hadoop, and then Hadoop moved to in-memory Spark, we are seeing the rise of in-GPU GoAi. Contributors already include most GPU database developers (OmniSci, BlazingDB, FastData, u2026), visual analytics developers (Graphistry), and broader data eco-system OSS companies like Conda.
To make the set of tools work together, GoAi members rallied around Apache Arrow. It is a file format and set of protocols that support in-memory typed dataframes with zero-copy data transfers between tasks and libraries. Clouds let you rent instances with multiple GPUs that have 16GB GPU RAM each, and NVIDIA DGX nodes already store 512GB+ in-GPU RAM. This unlocks running most tasks entirely in the GPU, and as streaming frameworks emerge, nearly everything is fair game.
For a taste of what happens when you switch to streaming of Arrow files between GPUs, the following videos show a before/after of the Graphistry 2.0 engine. The first video shows our original hand-written visual analytics engine: GPUs in the browser, GPUs in the data center, and optimized networking. This year, we rewrote our interop code into Arrow (forming the core of Apache Arrow[JS]): the result is our new visual analytics engine — which runs in any browser — takes much less code, handles about 5X more data, and runs visibly faster:
Graphistry 1.0 Engine
Graphistry 2.0 Engine
NVIDIA RAPIDS
Apache Arrow unlocks and speeds up interoperability between analytics tools, and RAPIDS provides convenient GPU IO and compute layers. This can help all the way across the data pipeline: sending data from CPU Spark to GPU frameworks, converting untyped CSVs to typed Arrow, performing tabular operations like filtering, and under the same family, supporting additional analytics areas like ML and graph. Enterprise-grade GPU analytics tools like Graphistry (visual analytics) and BlazingDB (warehouse interop) are incorporating it as part of a common core that is better than CPU alternatives but not fundamentally differentiating between specific analytic tool categories: Rapids is part of the GoAi rising tide.
RAPIDS is still early, but the numbers already look great. As a few examples of core data tasks, on a Titan V single GPU with 12GB GPU RAM and 32GB CPU RAM, similar to a cloud device, we see significant speedups on loading data and a simple cross-filtering task (filtering followed by histogramming). The result is 100M-1B row datasets become interactive!
Test setup:
- Titan V single GPU, 12GB GPU RAM, 32GB CPU RAM machine
- Representative of a $1.0/hr AWS P3.2 preemptible
- IO: Load 100M rows (x 6 floats) or 1.5B rows (x 1 float) of data, as CSV and Arrow
- Compute: Cross-filter (filter + histogram)
- Compare CPU (Pandas) with GPU (PyGDF for filtering and Numba for histograms)
The early results are spectacular — 20-30s computations become subsecond, 100M-1B row datasets become easy… and that is when bursting on just one GPU.
Graphistry + NVIDIA RAPIDS
Think of Graphistry as a UI for accessing RAPIDS tech without coding. Graphistry is the only full-stack GPU visual analytics platform, meaning we use GPUs all the way from your browser to the data center. The platform has been architecting to use Arrow end-to-end in the pipeline over the last year and helping bring similar Arrow-based workflows to the web, and RAPIDS has been a big motivator for that. As new RAPIDS functionality become available, they become drop-in replacements along our pipeline. The result is visual analytics users get to leverage RAPIDS — and broader GoAi frameworks — without writing code.
Our results around GoAi have been raising eyebrows all the way from operational analyst to bank executives. NVIDIA RAPIDS has been a key investment for us, and not discussed here, especially in terms of marching to a multi-node multi-GPU future. Hard tech startups have to be targeted in the bets they make, and Graphistry is excited to welcome RAPIDS into the GoAi community!
Incident responders and threat hunters are often facing a bit of an analytical catch-22. They typically have access to more and higher fidelity data sources than ever before, yet the volume and complexity of the data can often make it hard to see the point that matters.
Analyzing Bro logs is a good case in point. Bro can bring a ton of context and potential paths to pivot through an investigation, but this same wealth of data can quickly get impractical to use in a real investigation. Being able to see through this complexity and pivot to bring in the right context is something that graphs excel in general and Graphistry specializes in the context of an investigation. The video and walkthrough below shows how Graphistry can quickly accelerate a common investigation.
Getting Started
Let’s look at common investigation. Below we are looking at some Bro logs in Splunk, where we see some suspicious downloads that appear to GIF files but are actually executables. From here we can jump right into the investigation in Graphistry using a deeplink from within Splunk. This drops us into a pre-built Graphistry investigation template that can automatically query additional context and data sources.
Viewing Basic Connections
Once we are are in the Graphistry template, have pre-built pivots that brings in additional context. We can just Run All Pivots and then use the UI to filter data from the pivots that we want to see.
By looking at the first pivots, we can quickly see all the IP addresses and domains that are associated with our suspect files. In the diagram each ring shows a type of data (e.g. File hash, IP address, domain, etc), and the key in the bottom right shows what each ring represents.
From this view we can quickly see the IP addresses that are associated with our suspicious files.
Enrich and Expand
Next we can start to enrich our info from Bro. In the next few pivots we can pull in data from Virus Total to see if there are any hits on the suspect files and IP addresses. Below we can see we are getting a non-trivial amount of hits on our files as well as the IP addresses associated with those files. This gives a quick and easy way to verify that we are looking at a real incident.
Expand and Hunt
Now that we know that we are looking at a real incident, we might get curious to see if other devices have been communicating with these bad IP addresses. We can enable our final two pivots and focus just on the results of those two data sources to see if we picked up any new hits.
And from here we can quickly see a new IP address with Virus Total hits that we hadn’t seen before. So now we can continue to pull this thread to find other hosts in the network that may be affected by this same threat and see the full scope of the incident.
Of course, we can continue to pull this thread to expand our search. However, hopefully this provides a feel for how we can take a relatively dense set of data and visually expand to see the relationships that we care about and progressively expand to follow the natural flow of an investigation.
Graph visualization has proven to be powerful for investigating almost any type of data, and most recently the team at Graphistry was able to help in uncovering a massive Ethereum heist on two of the world’s most popular DApps (distributed applications).
AnChain.ai and Graphistry recently partnered to investigate the world’s first publicly identified BAPT (Blockchain Advanced Persistent Threat). The investigation identified the BAPT-F3D hacker group, which was responsible for stealing 12,948 ETH (~ $4 million) between July and August 2018 from various vulnerable smart contract DApps. As of today, BAPT-F3D is still actively attacking.
Fomo3D and the Airdrop Vulnerability
AnChain.ai, which specializes in security for the blockchain ecosystem, analyzed the wildly popular game u201cFomo3Du201d ( the #1 DApp in July 2018) and its copycat u201cLast Winneru201d (the #5 DApp in August 2018). These games are DApps based on Ethereum Solidity smart contract and operate quite openly as Ponzi schemes or exit scams. At high level the game works as a lottery with players buying keys that reset the timer for a round. Keys continue to get more expensive over time, and eventually when the time runs out, the player who bought the last key wins the entire pot.
Additionally the game included another side-betting opportunity when a player buys their keys. When a player buys their keys they have a percentage chance to win an u201cairdropu201d to instantly win ETH from a growing sidepot. The more a player gambles on their chance, the more they stand to win. And this airdrop function is where things got interesting.The airdrop function contained a vulnerability, which allowed coordinated attackers to steal the equivalent of more than $4 million USD across both games in just a few days.
Finding the Industry’s First Blockchain APT
Combining Graphistry’s industry-leading GPU-powered investigation platform with AnChain.ai Situational Awareness Platform (SAP), AnChain.ai gained a holistic view of all millions of events and over 30,000 addresses related to the games. As a result, the AnChain team was able to identify the first known Blockchain Advanced Persistent Threat (BAPT), dubbed BAPT-F3D. This was the first known BAPT in blockchain history. Further bytecode artifacts similarity analysis by SECBIT Labs confirmed this BAPT group of 5+ addresses are strongly correlated, as likewise seen in the visualization.
Figure: Center white node – main contract; intermediate money sinks seen on path to APT accounts identified by anomalous high-volume behavior. Paths with many edges (transactions) are either killchain or benign use that are visually separated by their operational behavior.
The AnChain.ai SAP was able to identify the following traits related to BAPT-F3D:
- Advanced: Leverages massive scale of sophisticated attack contracts to exploit a vulnerability in the u201cairdropu201d feature; Anti-Forensics capability that self-destructs the blockchain artifacts. Coordinated crime.
- Persistent: Well planned, and operating continuously for weeks; Constantly upgrading attack contracts from V1 to V3. Moving from target to target
- Threat: Financially motivated threat targeting specific smart contract DApps with similar vulnerabilities, stealing $4 millions worth of ETH and counting.
Impacts and Conclusions
Using knowledge graphs, AnChain.ai was able to document a new type of threat facing DApp owners, exchanges, and the growing blockchain ecosystem. For Graphistry, the analysis proved to be very similar to our work in anti-fraud and money-laundering investigations, although with very new and interesting twist. But most importantly, it shows the power of knowledge graphs and GPU-powered graph investigations to quickly expose the important connections and relationships across millions of pieces of data.
We think of this as the user interface for a world increasingly dependent on data, machine-learning, and AI. Analysts have similar needs whether investigating malware or phishing incidents, tracking the flow of illicit funds, fraud within a healthcare system, or hundreds of other data driven projects. Humans need to be able to see and understand what is in their data. They need AI and ML models to not be impenetrable black boxes. By bringing an interactive and investigative front end to these technologies, we hope to make them more accessible, usable, and ultimately deliver far more impactful analysis and applications.