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

Software Vulnerability Detection Tielei Wang 12 Tao Wei 12 Guofei Gu 3 Wei Zou 12 1 Key Laboratory of Network and Software Security Assurance Peking University Ministry . Maxino Member IEEE and Philip J Koopman Senior Member IEEE Abstract Embedded control networks commonly use checksums to detect data transmission errors However design decisions about which checksum to use are difficult because of a lack of informat Fixity can be generated on the server side and there are also various types of programs available to assist with this task This paper provides basic information about checksums and describes the initial experience of five different easily accessible We call the tail of the head of and uv the ends of If there is an edge with tail and head then we let uv denote such an edge and we say that this edge is directed from to Loops Parallel Edges and Simple Digraphs An edge uv in a digraph is a 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 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. Middleboxes. Interference with . Tracebox. Gregory Detal. *, Benjamin . Hesmans. *, Olivier Bonaventure*, Yves . Vanaubel. ° and Benoit . Donnet. °.. *Université catholique de Louvain. °Université de Liège.  . 0368-3500. Nurit. . Dor. Shir. Landau-. Feibish. Noam Rinetzky. Preliminaries. Students will group in teams of 2-3 students. . Each group will do one of the projects presented.. Administration.  . 0368-3500. Nurit. . Dor. Shir. Landau-. Feibish. Noam Rinetzky. Preliminaries. Students will group in teams of 2-3 students. . Each group will do one of the projects presented.. Administration. Dawn Song, . Kostya. . Serebryany. ,. Peter . Collingbourne. . Techniques for bug finding. Automatic test case generation. Lower coverage . Lower false positives . Higher false negatives. Fuzzing . Richard Johnson | Offensive Summit 2015. Introduction . Whoami. Richard Johnson / @richinseattle. Research Manager, Vulnerability Development. Cisco, Talos Security Intelligence and Research Group. Agenda. 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. [SIGCOMM’16]. Muhammad Shahbaz. Nick . Feamster. Jennifer Rexford. Sean Choi. Nick McKeown. Ben Pfaff. Changhoon. Kim. Importance of Software Switches. Hypervisor. OVS. VM. VM. VM. VM. Hypervisor. Systems. -. From . ZFS to Z. 2. FS. Yupu Zhang. yupu@cs.wisc.edu. 10/4/2013. 1. Data Integrity In Reality. Preserving data integrity is a challenge . Imperfect. components. D. isk, firmware, controllers . Zettabyte Reliability with Flexible End-to-end Data Integrity Yupu Zhang , Daniel Myers, Andrea Arpaci-Dusseau , Remzi Arpaci-Dusseau University of Wisconsin - Madison 5/9/2013 1 Data Corruption