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Past Talks

Senator Jeanne Shaheen

Jeanne Shaheen
U.S. Senator from New Hampshire
Russian Interference in American Politics and Cyber Threats to Our Democracy
Tuesday, February 20, 2018
Alumni Hall (Hopkins Center)
11:00 AM

Lisa Monaco

Lisa Monaco
Former Homeland Security Advisor to President Obama
In Conversation: Lisa Monaco, Fmr Homeland Security Advisor to President Obama
Tuesday, February 13, 2018
Filene Auditorium (Moore Building)
5:00 PM
Sponsored by The Dickey Center for International Understanding

John Stewart EPRI

John Stewart
Sr. Technical Leader, Cyber Security, EPRI
Securing Grid Control Systems
Friday, January 12, 2018
Sudikoff L045 Trust Lab
12:00 Noon

M. Todd Henderson

M. Todd Henderson
Professor of Law, University of Chicago
Hacking Trust: How the Social Technology of Cooperation Will Revolutionize Government
Thursday, January 11, 2018
5:00pm-6:30pm 
Room 003, Rockefeller Center
Sponsored by: Rockefeller Center

Dr. Liz Bowman

Dr. Elizabeth Bowman
U.S. Army Research Laboratory
Artificial Intelligence, Machine Learning and Information: Army Social Computing Research
Tuesday, December 5th
Haldeman 041 Kreindler Conference Room
4:00 PM

Dr. Fabio Pierazzi

Dr. Fabio Pierazzi
Royal Holloway University of London
Network Security Analytics for Detection of Advanced Cyberattacks
Tuesday, November 28th
Sudikoff Trust Lab (L045)
12:30 PM

V.S. Subrahmanian

V.S. Subrahmanian
Dartmouth Distinguished Professor in Cybersecurity, Technology, and Society
Bots, Socks, and Vandals
Tuesday, November 14th
Carson L01
5:00 PM 

Rand Beers

Rand Beers ('64)
Big Data, the Internet, and Social Media:  The Road to the November 2016 Election
Wednesday, November 8th
Haldeman 41 (Kreindler Conference Hall)
4:30 PM 

Fright Night Imge

Wanna See Something REALLY Scary?
ISTS Looks at the Dark Web on Halloween Night
Tuesday, October 31st
S
udikoff  045 Trust Lab (dungeon)
7:30 PM - RSVP
Space is Limited 

Sal Stolfo

Salvatore J. Stolfo 
Columbia University
A Brief History of Symbiote Defense
Tuesday, October 31st
Rockefeller 003
5:00 PM

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

ISTS Information Pamphlet


2012BrochureCover

 

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

Data Mining for Detection of Network Intrusions

Project Summary

Data Mining for Intrusion Detection

  1. Stepping Stones: We have focused on the application of data-mining techniques to a particular problem: detecting "stepping stones,'' that is, a situation where someone is telnetting through a sequence of machines. The theory is that while some of these occurrences are innocent, hackers quite frequently use a series of steppingstones to get to their target, in order to minimize the chance that the intrusion can be traced to their home machine.
  2. Masqueraders: Using the recently published Bell-Labs benchmark data, where real users' logs of UNIX commands were modified by using real logs from another user in a small number of places, Mr. Yung investigated the problem of detecting "masqueraders," where one user gets control of the account of another. Yung developed a technique that beats all of the proposed techniques that have been developed for the problem represented by this data. In particular, he gets significantly smaller false-positive rates for a fixed false-negative rate. The big idea is constant revision of what "normal" behavior for a user means, as more data is gathered, and the user's behavior evolves slowly.
  • Project Lead: Jeffrey Ullman (Stanford)