PPT-TaintScope: A Checksum-Aware Directed Fuzzing Tool for Auto

Tielei Wang 1 Tao Wei 1 Guofei Gu 2 Wei Zou 1 1 Peking University China 2 Texas AampM University US 31st IEEE Symposium on Security amp Privacy Outline Introduction Background . The actual checksum math is easy and from a perfor mance standpoint so cheap that it can be con sidered free In the process of improving the use of hardware checksum of64258oading engines recalculating the IP checksum has been found to be essentialy of Career Development. Why are children’s aspirations so restricted? How can we expand them?. Linda . S. . Gottfredson. , PhD. School of Education. University of Delaware. Newark, DE 19716 USA. Cows. The “No Bull” Talk on Fuzzing. Security B-Sides Ottawa. November 13, 2010. Mike Sues (Rigel Kent). Karim Nathoo (Inverse Labs). Objectives. We can’t cover fuzzing in-depth in 50 minutes. Raise awareness of fuzzing as an option in higher assurance/product evaluations/more focused assessments. Tielei. Wang. 1. , Tao Wei. 1. , . Guofei. Gu. 2. , Wei Zou. 1. 1. Peking University, China. 2. Texas A&M University, US. 31st IEEE Symposium on Security & Privacy. Outline. Introduction. Background . A Checksum-Aware Directed fuzzing Tool for Automatic Software Vulnerability Detection. Tielei. Wang. 1. , Tao Wei. 1. , . Guofei. Gu. 2. , Wei Zou. 1. 1. Peking University, China. 2. Texas A&M University, US. EECS 700 – Special Topics: Internet of Things. Dain . Vermaak. IOT Needs Data. Animated by Auto Animator. Imagine a Typical Database. Animated by Auto Animator. Imagine a Typical Database. Animated by Auto Animator. By. . Nikolaj . Tolka. čio. v. Agenda. What is web application fuzz testing. Introduction to “Fuzzing Machine”. What results it produces. Youtube. setup in “Fuzzing Machine” . How it can be used in other projects. Development . and . Testing Time on Embedded Space . Programs With Auto- Generated Code. Software Engineer. Northrop Grumman Electronic Systems. Matthew Conte. Abstract. Embedded software for space systems is one of the most expensive types of software to produce, mainly because of the rigorous testing involved. However, developers on the Northrop Grumman Space Systems Software team have observed that infrastructure code such as class definitions for interface messages, configuration files, and telemetry points is often repetitive, with each class differing only in parameters and implementation of common functions. It became clear that rewriting this repetitive code for each program and, sometimes, within a single program was taking developers’ focus away from the true engineering problems. . Dawn Song, . Kostya. . Serebryany. ,. Peter . Collingbourne. . Techniques for bug finding. Automatic test case generation. Lower coverage . Lower false positives . Higher false negatives. Fuzzing . Dawn Song, . Kostya. . Serebryany. ,. Peter . Collingbourne. . Techniques for bug finding. Automatic test case generation. Lower coverage . Lower false positives . Higher false negatives. Fuzzing . John . Heasman. Stanford University, April 2009. Agenda. Introductions. What is . fuzzing. ?. What data can be fuzzed?. What does fuzzed data look like?. When (not) to fuzz?. Two approaches and a basic methodology. Software Vulnerability Detection. . Tielei . Wang. 1,2. , Tao Wei. 1,2. , Guofei Gu. 3. , Wei . Zou. 1,2. 1. Key Laboratory of Network and Software Security Assurance . (. Peking University), . Ministry . Vulnerability Detection. Tielei . Wang1. ;. 2, Tao Wei1. ;. 2, Guofei Gu3, Wei Zou1. ;. 2. 1Key Laboratory of Network and Software Security Assurance (. Peking University. ),. Ministry of Education, Beijing 100871, China. Walla Walla CommunityCollege does not endorse any one brand or manufacturer of toolsSocket Set -1/4 Drive3/16 1509/16 Standard Depth 6 Point 3/16 9/16 Deep 6 Point Socket Set -3/8 Drive5/16 1507/8 Sta