TOPICS OF DISCUSSION:
    ================================================================================================
    
    
    if focus is on cybersecurity in networks:
*        could data minimization be a way to collapse privacy and performance concerns with security concerns!
*Jen's experience: companies prefer to have everything, performance is less of an issue than security
*
*example of where this is interesting is in streaming algorithms
*sketches: working with compact data structures out of interest in efficiency
*
if the project was developed differently: 
*    if what was secured was not the cyber but certain values
*if privacy and freedom of expression were one of them
*what would cybersecurity look like?
*how would we guide cybersecurity research then?
*
distinction between characterization vs design people
*network characterization people
*grounded in reality
*data drive
*
*design people
*design secure protocols
**very different in approach!
*
Drawing parallels between the clean slate approach and game changing technologies
*clean slate: backward compatibility as an excuse against intellectual creativity, an anti-intellectual project
*
*response: but they are so out of touch, the proposals are ungrounded
*
*clean slate needs to start with something: what assumptions are you not willing to relax?
*following a discussion at the WhiteHouse: industry people and some others said, security of internet is so bad, and you have spent so much money and work on cybersecurity, why don't you just give us a new internet?
*the question is what is the trade off for making the internet more secure?
*there are cost/incentive barriers
*deployment issues: incremental: people look to see if others implemented something or not
*the government can play a positive role: set examples or through procurement
*but this is all dirty slate!
*
*we need to start with certain assumptions that we are not willing to relax. you can't have all the goodies and none of the badies.
*what values are we going to bring with us to the clean slate?
*test assumption 1: all devices come with a self-certifying identity registered with a global authority
*if you don't want that, what happens to your solution space: which solutions fall off the table?
*
*
*What kind of attacks do we want, what do we want to do with attacks, what kind of attackers do we want?
*honeypots: how long do you want attackers to be there? 
*
*
Privacy Red team?
*We don't have methodology at all, even if we wanted to do better
*how do you even go about doing a good job?
*have a privacy red team attack our solutions
*
*but then you run into all the problems of collaborations
*FIA-NDN was successful because it had one person with a clear vision
*we are not good at designing by committe
*culturally difficult, collaboration is a difficult thing
*how do we go about it?
*

*
*
PEOPLE TO INVITE:
        ================================================================================================


chat with:
    Christopher Yoo
    

Fred Schneider
*cannot on 12-14
*can on the week of the 16th
*
Jen Rexford:
*    cannot on the 12th
*14, 18, 20 are ok dates
*19th is a faculty meeting which Nick will have to go to, too
*

    
    Measurement Oriented Security:
*    Vern Saxson
    
*    Stefan Savage - UCSD

*Other people doing measurements: (could this be seen as positive/afformative cybersecurity research, is it also 
*Phillipa Gil -> understanding censorship products
*Becker Polverini -> greate firewall of china before and after olympics

*Jedidiah Crandall
*
*Roger Dingledine
*
*
How about the UPenn people: what exactly are they working on (cybersecurity and differential privacy)
*Aaron Roth would be a name there
*
*Lalitha Sankar

Vyas Sekar - CMU - middle boxes

Sharon Goldberg


David Clark

Alex Halderman

Stephanie Forest - UNM, computer security, bioengineered stuff and awareness of internet governance issues

Avi Rubin - Hopkins, firewalls

Rebecca Wright - discrete math, Rutgers

Joan Feigenbaum

Mutu also atRutgers



Notes of Meeting with Jen Rexford (amazing meeting)

Niksun Company -> Walter Willinger (also teaching a course this year on cybersecurity)



Notes from Walter Willinger Slides:
    
    Terminology:
        
        Dwell Time:
            The objective is to reduce dwell time and to identify damage done. It is about being able to account for the damage. 
            
            
        The attacker model:
            
            The life-cycle of the attacker is often described through the "intrusion kill chain":
*            reconnaissance -> weaponization -> delivery -> exploitation -> installation -> comman and control -> actions on objectives
*This contrasts with the attacker models used in encryption: passive/active attacker defined based on information/observations as well as attacks, but typically not discussed in an elaborate life-cycle like the attacker in the "intrusion kill chain". 
            
            
            Sings of APT activities:
*                increase in elevated log-ons
*funding widespread backdoor trojans
*unexpected information flows
*focused spear-fishin campaigns against a company's employees
*
*One of the main problems that proponents say using machine learning for cybersecurity is to decrease what we don't know (!)
*objective: reduce median dwell time
*how: collect all the data necessary to check for the wide range of different signs of APT activities
*bottom line: the more data, the better we can identify and stop APT!
*more arguments: without the complete traffic (after the fact) intrusion reconstruction, network forensics, and/or real time attack deteaction are in general impossible to perform 
            
            
            Do we assume a strategic attacker: the gaming technologies propose using behavioral analysis to identify malicious/abnormal behavior, deception to attract attackers, and obfuscation to make their lives harder. What are the assumptions about the attacker's skill set: could they not employ adversarial algorithms, obfuscation, and deception themselves? 
            




Where do attacks come from?

According to Walter:
*- distill domain expert's understanding of different attack scenarios and try to describe attack specific "behaviors" as "interesting communication pattersn"
*- select queries/DSD algorithms that look for occurrences of such "interesting" patterns
*- Use combination of continuous queries (e.g., monitoring for changing trends/patterns) and one-time or ad-hoc queries
*(e.g., examining particular behavior or patterns)
*
*
The role of visualization:
*    According to Walter:
*Challenge: How to visualize in our target setting and in real-time the effect of a chosen mitigation strategy (rule-of-action) in response to a detected attack (e.g., co-lateral damage).
*
*### seda: this raises questions about how visualization becomes central to understanding/capturing attacks and mitigation methods. What are the limitations of visualization as a method of cybersecurity and population management?
*
*