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Anomaly Detection at Multiple Scales (ADAMS)

The Anomaly Detection at Multiple Scales (ADAMS) program seeks to create, adapt and apply technology to anomaly characterization and detection in massive data sets. Anomalies in data cue the collection of additional, actionable information in a wide variety of real world contexts.

Program Manager: Dr. Angelos Keromytis


The content below has been generated by organizations that are partially funded by DARPA; the views and conclusions contained therein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA or the U.S. Government.

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Last updated: November 13, 2015

Palo Alto Research Center Modeling Attrition in Organizations From Email Communication
Palo Alto Research Center A Comparison Study of User Behavior on Facebook and Gmail
Palo Alto Research Center Predicting Group Stability in Online Social Networks
Palo Alto Research Center Multi- Domain Information Fusion for Insider Threat Detection
Palo Alto Research Center A Bayesian Network Model for Predicting Insider Threats
Palo Alto Research Center Characterizing User Behavior and Information Propagation on a Social Multimedia Network
Palo Alto Research Center Understanding Email Writers: Personality Prediction from Email Messages
Palo Alto Research Center Inferring Personality of Online Gamers by Fusing Multiple-View Predictions
Palo Alto Research Center Modeling Destructive Group Dynamics in On-line Gaming Communities
Palo Alto Research Center Proactive Insider Threat Detection through Graph Learning and Psychological Context
CERT Bridging the Gap: A Pragmatic Approach to Generating Insider Threat Data
Leidos (formerly SAIC) OPAvion: Mining and Visualization in Large Graphs
Leidos (formerly SAIC) Opinion Fraud Detection in Online Reviews by Network Effects
Leidos (formerly SAIC) Mining Connection Pathways for Marked Nodes in Large Graphs
Leidos (formerly SAIC) Com2: Fast Automatic Discovery of Temporal (Comet) Communities
Leidos (formerly SAIC) Agglomerative Clustering of Bagged Data using Joint Distributions
Leidos (formerly SAIC) A Scalable Approach to Size-Independent Network Similarity
Leidos (formerly SAIC) NetSimile: A Scalable Approach to Size-Independent Network Similarity
Leidos (formerly SAIC) Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition
Leidos (formerly SAIC) Data Mining Meets HCI: Making Sense of Large Graphs
Leidos (formerly SAIC) Case Study on Fraud Detection using Social Network Analysis
Leidos (formerly SAIC) STINGER: High Performance Data Structure for Streaming Graphs
Leidos (formerly SAIC) Systematic Construction of Anomaly Detection Benchmarks from Real Data
Leidos (formerly SAIC) Do More Views of a Graph Help? Community Detection and Clustering in Multi-Graphs
Leidos (formerly SAIC) Classifier-Adjusted Density Estimation for Anomaly Detection and One-Class Classification
Leidos (formerly SAIC) Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events
Leidos (formerly SAIC) A Fast Algorithm for Streaming Betweenness Centrality
Leidos (formerly SAIC) Load Balanced Clustering Coefficients
Leidos (formerly SAIC) Faster Betweenness Centrality Based on Data Structure Experimentation
Leidos (formerly SAIC) Parallel Approximate Betweenness Centrality for Dynamic Graphs
Leidos (formerly SAIC) Faster Clustering Coefficient Using Vertex Covers
Leidos (formerly SAIC) Robust Multivariate Autoregression for Anomaly Detection in Dynamic Product Ratings
Leidos (formerly SAIC) Net-Ray: Visualizing and Mining Billion-Scale Graphs
Leidos (formerly SAIC) VoG: Summarizing and Understanding Large Graphs
Leidos (formerly SAIC) TENSORSPLAT: Spotting Latent Anomalies in Time
Leidos (formerly SAIC) Patterns amongst Competing Task Frequencies: Super-Linearities, and the Almond-DG Model
Leidos (formerly SAIC) DELTACON: A Principled Massive-Graph Similarity Function
Leidos (formerly SAIC) Fast Outlier Detection Despite the Duplicates
Leidos (formerly SAIC) Influence Propagation: Patterns, Model and a Case Study
Leidos (formerly SAIC) Demonstrating Interactive Multi-resolution Large Graph Exploration
Leidos (formerly SAIC) Interactive Multi-resolution Exploration of Million Node Graphs
Leidos (formerly SAIC) Leveraging Memory Mapping for Fast and Scalable Graph Computation on a PC
Leidos (formerly SAIC) Scalable, Minimalist Graph Computation on a PC via Memory Mapping
Leidos (formerly SAIC) Using Blocking to Learn Causal Relational Models in the Presence of Latent Variables
Leidos (formerly SAIC) A Sound and Complete Algorithm for Learning Causal Models from Relational Data
Leidos (formerly SAIC) Reasoning about Independence in Probabilistic Models of Relational Data
Leidos (formerly SAIC) Layered Behavioral Trace Modeling for Threat Detection
Leidos (formerly SAIC) A New Parallel Algorithm for Connected Components in Dynamic Graphs
Leidos (formerly SAIC) Context-Aware Insider Threat Detection
Leidos (formerly SAIC) Detecting Insider Threats in a Real Corporate Database of Computer Usage Activity
Leidos (formerly SAIC) Fast and Accurate k-means for Large Datasets
Leidos (formerly SAIC) GLOs: Graph-Level Operations for Exploratory Network Visualization
Leidos (formerly SAIC) Inside Insider Trading: Patterns & Discoveries from a Large Scale Exploratory Analysis
Leidos (formerly SAIC) Exploring Large Scale Insider Trading Data: Network Patterns & Discoveries
Leidos (formerly SAIC) Guiding Scientific Discovery with Explanations Using DEMUD
Leidos (formerly SAIC) Use of Domain Knowledge to Detect Insider Threats in Computer Activities
Leidos (formerly SAIC) Detecting Unknown Insider Threat Scenarios
IBM Measuring the Sensitivity of Graph Metrics to Missing Data
IBM Non-negative Residual Matrix Factorization: Problem Definition, Fast Solutions, and Applications
IBM PICS: Parameter-free Identification of Cohesive Subgroups in Large Attributed Graphs
IBM Fast and Reliable Anomaly Detection in Categorical Data
IBM One-Class Conditional Random Fields for Sequential Anomaly Detection
IBM Adaptive Multi-task Sparse Learning with an Application to fMRI Study
IBM Generating Balanced Classifier-independent Training Samples from Unlabeled Data
Columbia University Bait and Snitch: Defending Computer Systems with Decoys
Columbia University Fog Computing: Mitigating Insider Data Theft Attacks in the Cloud
Columbia University Lost in Translation: Improving Decoy Documents via Automated Translation
Columbia University Software Decoys for Insider Threat
Columbia University Modeling User Search Behavior for Masquerade Detection
Columbia University On the Design and Execution of Cyber-Security User Studies: Methodology, Challenges, and Lessons Learned
Columbia University Decoy Document Deployment for Effective Masquerade Attack Detection
Columbia University Insider Threat Defense
Columbia University Monitoring Technologies for Mitigating Insider Threats
Columbia University BotSwindler: Tamper Resistant Injection of Believable Decoys in VM-Based Hosts for Crimeware Detection
Columbia University Automating the Injection of Believable Decoys to Detect Snooping
Columbia University Addressing the Insider Threat
Columbia University Designing Host and Network Sensors to Mitigate the Insider Threat
Columbia University Baiting Inside Attackers Using Decoy Documents
University of Maryland Behavioral Analysis of Insider Threat: A Survey and Bootstrapped Prediction in Imbalanced Data
University of Maryland PASS: A Parallel Activity Search System
University of Maryland Discovering the Top-k Unexplained Sequences in Time-Stamped Observation Data
University of Maryland Pattern-based Centrality in Semantics Graphs
University of Maryland PADUA: Parallel Architecture to Detect Unexplained Activities
University of Maryland Exploring Linked Data with Contextual Tag Clouds
University of Maryland Infrastructure for Efficient Exploration of Large Scale Linked Data via Contextual Tag Clouds