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Securing the e-Campus 2017 - Exact time and dates TBD

Recent Talks

Dan Wallach

STAR-Vote: A Secure, Transparent, Auditable and Reliable Voting System

Professor Dan Wallach
Rice University
Thursday April 27, 2017
Carson L01, 5:00 PM

Ben Miller Dragos

Pandora's Power Grid - What Can State Attacks Do and What Would be the Impact?

Ben Miller
Chief Threat Officer, Dragos, Inc.
Tuesday May 2, 2017
Kemeny 007, 4:30 PM
Brendan Nyhan

 

 

 

Factual Echo Chambers? Fact-checking and Fake News in Election 2016.

Professor Brendan Nyhan
Dartmouth College
Thursday May 4, 2017
Rocky 001, 5:00 PM

Dickie George

 

Espionage and Intelligence

Professor Dickie George
Johns Hopkins University
Thursday May 11, 2017
Rocky 001, 5:00 PM

Dan Wallach

A Nation Under Attack: Advanced Cyber-Attacks in Ukraine

Ukrainian Cybersecurity Researchers
Thursday April 6, 2017
Oopik Auditorium 5:30 PM

RIOTS logo 

Professor Sean Smith, Director of the ISTS and Bill Nisen, Associate Director, spoke at the

School House residential cluster on the Internet of Risky Things  - February 21, 2017, 5:30 PM

Craig Smith

 

 

 

You Don't Own Your Car
Craig Smith
OpenGarages
Tuesday May 10, 2016 
Carson L02 @4:15

David Safford

 

Hardware Based Security for GE's Industrial Control Systems
David Safford
GE Global Research
Tuesday May 17, 2016
Carson L02 @4:15

 

DanTentler

"It's Fine," They Said. "Just Ship It," They Said.
Dan Tentler
The Phobos Group
Tuesday April 12, 2016 
Carson L02 @4:15

Harold Thimbleby

 

 

 

The Best Way to Improve Healthcare is to Improve Computers
Harold Thimbleby
Swansea University
April 23, 2015

Craig Shue

 

 

 

Managing User-Level Compromises in Enterprise Network
Craig Shue
Worcester Polytechnic Institute
March 31, 2015

 

Newsletter 

Oct news 2015

 

ISTS Information Pamphlet


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Institute for Security, Technology, and Society
Dartmouth College
6211 Sudikoff Laboratory
Hanover, NH 03755 USA
info.ists@dartmouth.edu
HomeEvents >

Exploiting Feature Distributions in Anomaly Diagnosis

Abstract

Both operators and users of the Internet are increasingly concerned with the problem of network anomalies --- attacks, infections, misconfiguations, and other unusual events. The increasing practicality of large-scale flow capture makes it possible to conceive of traffic analysis methods that detect and identify a large and diverse set of anomalies. However the challenge of effectively analyzing this massive data source for anomaly diagnosis is as yet unmet. In this talk I will argue that the distributions of packet features (IP addresses and ports) observed in flow traces reveals both the presence and the structure of a wide range of anomalies. Using entropy as a summarization tool, I will show that the analysis of feature distributions leads to significant advances on two fronts: (1) it enables highly sensitive detection of a wide range of anomalies, augmenting detections by volume-based methods, and (2) it enables automatic classification of anomalies via unsupervised learning. Using data from two backbone networks (Abilene and Geant), I will show that using feature distributions, anomalies naturally fall into distinct and meaningful clusters. These clusters can be used to automatically classify anomalies and to uncover new anomaly types.