Abstract. Although extensive studies have been conducted on online social networks (OSNs), it is not clear how to characterize information propagation and social influence, two types of important but not well defined social behavior. This paper presents a measurement study of 58M messages collected from 700K users on Twitter.com, a popular social medium. We analyze the propagation patterns of general messages and show how breaking news (Michael Jackson's death) spread through Twitter. Furthermore, we evaluate different social influences by examining their stabilities, assessments, and correlations. This paper addresses the complications as well as challenges we encounter when measuring message propagation and social influence on OSNs. We believe that our results here provide valuable insights for future OSN research.
This document presents a framework to assist the writers of certificate policies or certification practice statements for certification authorities and public key infrastructures. In particular, the framework provides a comprehensive list of topics that potentially (at the writer's discretion) need to be covered in a certificate policy definition or a certification practice statement.
Vulnerabilities that allow worms to hijack the control flow of each host that they spread to are typically discovered months before the worm outbreak, but are also typically discovered by third party researchers. A determined attacker could discover vulnerabilities as easily and create zero-day worms for vulnerabilities unknown to network defenses. It is important for an analysis tool to be able to generalize from a new exploit observed and derive protection for the vulnerability.Many researchers have observed that certain predicates of the exploit vector must be present for the exploit to work and that therefore these predicates place a limit on the amount of polymorphism and metamorphism available to the attacker. We formalize this idea and subject it to quantitative analysis with a symbolic execution tool called DACODA. Using DACODA we provide an empirical analysis of 14 exploits (seven of them actual worms or attacks from the Internet, caught by Minos with no prior knowledge of the vulnerabilities and no false positives observed over a period of six months) for four operating systems.Evaluation of our results in the light of these two models leads us to conclude that 1) single contiguous byte string signatures are not effective for content filtering, and tokenbased byte string signatures composed of smaller substrings are only semantically rich enough to be effective for content filtering if the vulnerability lies in a part of a protocol that is not commonly used, and that 2) practical exploit analysis must account for multiple processes, multithreading, and kernel processing of network data necessitating a focus on primitives instead of vulnerabilities.
IONSince late 1999, DDoS (Distributed Denial of Service) [1,2,3] attack has drawn many attentions from both research and industry communities. Many potential solutions (e.g., ingress filtering [6,7], packet marking [5,8,9,10,11] or tracing [4], and aggregate-based congestion control or rate limiting) have been proposed to handle this network bandwidth consumption attack. Among them, "ICMP traceback (iTrace)" is currently being considered as an industry standard by IETF (Internet Engineering Task Force). While the idea of iTrace is very clever, efficient, reasonably secure and practical, it suffers a serious statistic problem such that the chance for "useful" and "valuable" iTrace messages can be extremely small against various types of DDoS attacks. This implies that most of the network resources spent on generating and utilizing iTrace messages will be wasted. Therefore, we propose a simple enhancement called "Intention-Driven" iTrace, which conceptually introduces an extra bit in the routing and forwarding process. With the new "intention-bit", it is shown that, through our simulation study, the performance of iTrace improves dramatically. This work has been proposed to IETF's ICMP Trace-Back working group.
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