PPT-Detecting attacks Based on material by Prof. Vern

Paxson UC Berkeley Detecting Attacks Given a choice wed like our systems to be airtightsecure But often we dont have that choice 1 reason why not cost in different dimensions A messy alternative detect misuse rather than build a system that cant be misused. What were their experiences in 2013 The results suggest a more unstable and complex landscape DDoS Attacks More Unpredictable than Ever Over the last year DDoS attacks evolved in strategy and tactics We saw increased media reports of smokescreening We base our approach on observing statistically signi64257cant changes in a parameter that sum marizes aggregate activity bracketing a distributed attack in time and then determining which sources present during that interval appear to have coordina of Electrical Engineering Computer Science Syracuse University Syracuse New York USA xzhang35wedusyredu Abstract In this paper we perform a thorough study on the risks im posed by the globally accessible Android Clipboard Based on the risk assessme Undetectable . Bluepill. Virtualization and its Attacks. What is . Virtualization?. What makes it possible?. How does . it . affect security?. Blue Pill Attacks. Conclusion. Questions. What’s Virtualization?. & Defense!. Who am I?. Michael LaSalvia . H. as . been in the information security industry for over 10 years and has worked for several fortune 500 companies, large managed services providers as well as a SANS mentor. Yossi. Oren and . Avishai. Wool. , . http://eprint.iacr.org/2009/422. snipurl.com/e-voting. IEEE RFID’2010, Orlando FL. Agenda. What’s the Israeli e-Voting Scheme?. How can we break it cheaply and completely?. Computer Security 2014. Background. An algorithm or software can be designed to be . provably secure. .. E.g. cryptosystems, small OS kernels, TPM modules, .... Involves proving that certain situations cannot arise. GraphG prof 1 prof 2 prof 3 prof 4 phd 5 stud 6 stud 7 adv adv adv adv adv sup sup GraphI 1 prof 2,3 prof 4 prof 5 phd 6,7 stud adv adv adv sup 1Fortheformaldevelopmentinthispaper,itwillbeconve-nientt GraphG prof 1 prof 2 prof 3 prof 4 phd 5 stud 6 stud 7 adv adv adv adv adv sup sup GraphI 1 prof 2,3 prof 4 prof 5 phd 6,7 stud adv adv adv sup 1Fortheformaldevelopmentinthispaper,itwillbeconve-nientt The Stakes Have Changed. . Have You?. November 17, 2016. Today’s Speakers. Sean Pike. Program Vice President, Security Products, IDC. Tom Bienkowski. Director, Product Marketing, Arbor Networks. Kevin Whalen. How the Attacks Start. Popularity of these sites with millions of users makes them perfect places for cyber attacks or cybercriminal activities. Typically happen when user log in to their social networking sites like Facebook or Twitter. Based on material by Prof. Vern . Paxson. , UC Berkeley. Detecting Attacks. Given a choice, we’d like our systems to be airtight-secure. But often we don’t have that choice. #1 reason why not: cost (in different dimensions). Characterizing collaborative/coordinated attacks. Types of collaborative attacks. Identifying Malicious activity. Identifying Collaborative Attack. . . 3. Collaborative Attacks. Informal definition:. Mengjia Yan, Yasser . Shalabi. , . Josep. . Torrellas. University of Illinois at Urbana-Champaign. http://. iacoma.cs.uiuc.edu. MICRO October 2016. Motivation. Cache-based covert channel attacks. Communicate through cache conflicts.