2019 IEEE High Performance
Extreme Computing Conference
(HPEC ‘19)
Twenty-third Annual HPEC Conference
24 - 26 September 2019
Westin Hotel, Waltham, MA USA
Thursday, September 26, 2019
BRAIDS: Boosting Resilience through Artificial Intelligence and Decision Support 1
1:00-2:40 in Eden Vale A1/A2
Chair: Alexia Schulz / MIT-LL, Pierre Trepagnier / MIT-LL, Igor Linkov / ACE, Matthew Bates / ACE
Combining Tensor Decompositions and Graph Analytics to Provide Cyber Situational Awareness at HPC Scale
James Ezick, Tom Henretty, Muthu Baskaran, Richard Lethin (Reservoir Labs), John Feo (PNNL), Tai-Ching Tuan, Christopher Coley (Univ.
Maryland), Leslie Leonard, Rajeev Agrawal, William Glodek, Ben Parsons (U.S. Army ERDC)
This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that
demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-
performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions
isolate coherent patterns of network behavior in ways that common clustering methods based on distance metrics cannot. Parallelized graph
analysis then uses directed queries on a representation that combines the elements of identified patterns with other available information (such
as additional log fields, domain knowledge, network topology, whitelists and blacklists, prior feedback, and published alerts) to confirm or reject
a threat hypothesis, collect context, and raise alerts. MADHAT was developed using the collaborative HPC Architecture for Cyber Situational
Awareness (HACSAW) research environment and evaluated on structured network sensor logs collected from Defense Research and
Engineering Network (DREN) sites using HPC resources at the U.S. Army Engineer Research and Development Center DoD Supercomputing
Resource Center (ERDC DSRC). To date, MADHAT has analyzed logs with over 650 million entries.
Proactive Cyber Situation Awareness via High Performance Computing
Allan Wollaber, Jaime Peña, Benjamin Blease, Leslie Shing, Kenneth Alperin, Serge Vilvovsky, Pierre Trepagnier (MIT-LL), Neal Wagner
(STR), Leslie Leonard (U.S. Army ERDC)
Cyber situation awareness technologies have largely been focused on present-state conditions, with limited abilities to forward-project nominal
conditions in a contested environment. We demonstrate an approach that uses data-driven, high performance computing (HPC) simulations of
attacker/defender activities in a logically connected network environment that enables this capability for interactive, operational decision
making in real time. Our contributions are three-fold: (1) we link live cyber data to inform the parameters of a cybersecurity model, (2) we
perform HPC simulations and optimizations with a genetic algorithm to evaluate and recommend risk remediation strategies that inhibit
attacker lateral movement, and (3) we provide a prototype platform to allow cyber defenders to assess the value of their own alternative risk
reduction strategies on a relevant timeline. We present an overview of the data and software architectures, and results are presented that
demonstrate operational utility alongside HPC-enabled runtimes.
[Best Student Paper Finalist] A Survey of Attacks and Defenses of Edge-Deployed Neural Networks
Mihailo Isakov (Boston Univ.), Vijay Gadepally (MIT-LL), Karen M. Gettings (MIT-LL), Michel A. Kinsy (Boston Univ.)
Deep Neural Network (DNN) workloads are quickly moving from datacenters onto edge devices, for latency, privacy, or energy reasons. While
datacenter networks can be protected using conventional cybersecurity measures, edge neural networks bring a host of new security
challenges. Unlike classic IoT applications, edge neural networks are typically very compute and memory intensive, their execution is data-
independent, and they are robust to noise and faults. Neural network models may be very expensive to develop, and can potentially reveal
information about the private data they were trained on, requiring special care in distribution. The hidden states and outputs of the network can
also be used in reconstructing user inputs, potentially violating users' privacy. Furthermore, neural networks are vulnerable to adversarial
attacks, which may cause misclassifications and violate the integrity of the output. These properties add challenges when securing edge-
deployed DNNs, requiring new considerations, threat models, priorities, and approaches in securely and privately deploying DNNs to the edge.
In this work, we cover the landscape of attacks on, and defenses, of neural networks deployed in edge devices and provide a taxonomy of
attacks and defenses targeting edge DNNs.
Hypersparse Neural Network Analysis of Large-Scale Internet Traffic
Jeremy Kepner (MIT LLSC), Kenjiro Cho (Internet Initiative Japan), KC Claffy (UCSD), Vijay Gadepally (MIT LLSC), Peter Michaleas (MIT
LLSC), Lauren Milechin (MIT EAPS)
The Internet is transforming our society, necessitating a quantitative understanding of Internet traffic. Our team collects and curates the largest
publicly available Internet traffic data containing 50 billion packets. Utilizing a novel hypersparse neural network analysis of “video” streams of
this traffic using 10,000 processors in the MIT SuperCloud reveals a new phenomena: the importance of otherwise unseen leaf nodes and
isolated links in Internet traffic. Our neural network approach further shows that a two-parameter modified Zipf-Mandelbrot distribution
accurately describes a wide variety of source/destination statistics on moving sample windows ranging from 100,000 to 100,000,000 packets
over collections that span years and continents. The inferred model parameters distinguish different network streams and the model leaf
parameter strongly correlates with the fraction of the traffic in different underlying network topologies. The hypersparse neural network pipeline
is highly adaptable and different network statistics and training models can be incorporated with simple changes to the image filter functions.