PPT-Sensor-based Mobile Web Fingerprinting and Cross-site Input Inference Attacks

Attacks Chuan Yue chuanyueminesedu IEEE Workshop on Mobile Security Technologies MoST 2016 1 5262016 Outline Introduction and Background Attacks Potential Defense Mechanisms. SINKS. Acknowledgement. Xiujuan. Yi (xyi@uci.edu). . Malini. . Karunagaran. Rutuja Raghoji. Ramya. . Tridandapani. Introduction. Typical . sensor network applications generate . large amounts . of data and send that data to the base station . Touching from a . Distance. In a nutshell …. Web . page fingerprinting attack . Dodges . defences. such . as. HTTPOS. Randomized . pipelining over Tor . . A. d . hoc . defenses unsuccessful. RECOGNIZING WEB PAGES. IT443 – Network Security Administration. Instructor: Bo Sheng. 1. Malware. Computer systems still have many vulnerabilities. When exposed to the Internet leads to exploitation. Major issue as computer systems become more ubiquitous. Warm up. Share your picture with the people at your table group.. Make sure you have your Science notebook, agenda and a sharpened pencil. use tape to put it in front of your table of contents. Describe the difference between observations and inferences. Warm up. Share your picture with the people at your table group.. Make sure you have your Science notebook, agenda and a sharpened pencil. use tape to put it in front of your table of contents. Describe the difference between observations and inferences. Mobile Web Fingerprinting and Cross-site Input Inference . Attacks. Chuan. Yue, chuanyue@mines.edu. IEEE . Workshop on Mobile Security Technologies (. MoST. ), . 2016. 1. 5/26/2016. Outline. Introduction and Background. :. Mobile Phone . Localization. via Ambience Fingerprinting. Martin . Azizyan. Duke University. Ionut. . Constandache. Duke University. Romit. Roy Choudhury Duke University. Abstract. Mobile computing . DNA Fingerprinting. Also known as . DNA profiling. Analyzes individuals based on the occurrence of repetitive sequences of DNA. The DNA of each individual has a distinctive pattern.. It is the unique pattern of these bands that makes it possible to distinguish individuals.. BATs and BLBs. Noa Bar-Yosef. Security Research Engineer. Imperva. noa@imperva.com. 12/02/2009. Agenda. The challenge of business logic bots. Business logic attacks. Business process automation:. The friendly side of web automation. BY. SASIKIRAN V.L. REDDY. STUDENT NO.6603240. OUTLINE. 1.Introduction. 2.Threats and Attacks. 3.Protocols for Detection of Clone Attacks. . 3.1 Randomized Multicast(RM). . 3.2 Line-Selected Multicast(LSM). Mariantonietta La Polla. Fabio Martinelli. Daniele Sgandurra. Outline. Introduction. Mobile Technologies. Mobile Malware. Attacks on Mobile Devices. Security Solutions For Mobile Devices. Conclusions. Dr. . Sudha. . Kumari. Assistant Professor. Department of Veterinary Microbiology. Bihar Animal Sciences University, Patna. INTRODUCTION . The . Thang Hoang. †. , Deokjai Choi. †. , . Thuc. Nguyen. ‡. † . Faculty of Information Technology, . Saigon Technology University, Vietnam. thang.hoangminh@stu.edu.vn. † . Dept. of Electronics & Computer Engineering, Chonnam National University, South Korea. Authors. Bo Sun, Fei Yu, Kui Wu, Yang Xiao, and Victor C. M. Leung.. . Presented by . Aniruddha Barapatre. Introduction. Importance of Cellular phones.. Due to the open radio transmission environment and the physical vulnerability of mobile devices , .