6,299 research outputs found
Fingerprinting Internet DNS Amplification DDoS Activities
This work proposes a novel approach to infer and characterize Internet-scale
DNS amplification DDoS attacks by leveraging the darknet space. Complementary
to the pioneer work on inferring Distributed Denial of Service (DDoS)
activities using darknet, this work shows that we can extract DDoS activities
without relying on backscattered analysis. The aim of this work is to extract
cyber security intelligence related to DNS Amplification DDoS activities such
as detection period, attack duration, intensity, packet size, rate and
geo-location in addition to various network-layer and flow-based insights. To
achieve this task, the proposed approach exploits certain DDoS parameters to
detect the attacks. We empirically evaluate the proposed approach using 720 GB
of real darknet data collected from a /13 address space during a recent three
months period. Our analysis reveals that the approach was successful in
inferring significant DNS amplification DDoS activities including the recent
prominent attack that targeted one of the largest anti-spam organizations.
Moreover, the analysis disclosed the mechanism of such DNS amplification DDoS
attacks. Further, the results uncover high-speed and stealthy attempts that
were never previously documented. The case study of the largest DDoS attack in
history lead to a better understanding of the nature and scale of this threat
and can generate inferences that could contribute in detecting, preventing,
assessing, mitigating and even attributing of DNS amplification DDoS
activities.Comment: 5 pages, 2 figure
A Characterization of Cybersecurity Posture from Network Telescope Data
Data-driven understanding of cybersecurity posture is an important problem
that has not been adequately explored. In this paper, we analyze some real data
collected by CAIDA's network telescope during the month of March 2013. We
propose to formalize the concept of cybersecurity posture from the perspectives
of three kinds of time series: the number of victims (i.e., telescope IP
addresses that are attacked), the number of attackers that are observed by the
telescope, and the number of attacks that are observed by the telescope.
Characterizing cybersecurity posture therefore becomes investigating the
phenomena and statistical properties exhibited by these time series, and
explaining their cybersecurity meanings. For example, we propose the concept of
{\em sweep-time}, and show that sweep-time should be modeled by stochastic
process, rather than random variable. We report that the number of attackers
(and attacks) from a certain country dominates the total number of attackers
(and attacks) that are observed by the telescope. We also show that
substantially smaller network telescopes might not be as useful as a large
telescope
Digital Platforms and Antitrust Law
This Article is about “big data” and antitrust law. Big data, for my purposes, refers to digital platforms that enable the discovery and sharing of information by consumers, and the harvesting and analysis of consumer data by the platform. The obvious example of such a platform is Google. The big platforms owe their market dominance not to anticompetitive conduct but to economies of scale. This Article discusses three types of anticompetitive conduct associated with digital platforms: kill zone expropriation, acquisition of nascent rivals, and denial of access to data. There is nothing so unusual about digital platforms that would require a reform of the antitrust laws. Some are described as two-sided markets, but this designation, even after Ohio v. American Express Co., should not present an obstacle to the application of antitrust law.
I. Introduction
II. Platforms
III. Competition Issues ... A. Kill Zone Expropriation ... B. Acquisition of Nascent Rivals ... C. Denial of Access to Data
IV. Antitrust Law
V. Conclusio
Flooding attacks to internet threat monitors (ITM): Modeling and counter measures using botnet and honeypots
The Internet Threat Monitoring (ITM),is a globally scoped Internet monitoring
system whose goal is to measure, detect, characterize, and track threats such
as distribute denial of service(DDoS) attacks and worms. To block the
monitoring system in the internet the attackers are targeted the ITM system. In
this paper we address flooding attack against ITM system in which the attacker
attempt to exhaust the network and ITM's resources, such as network bandwidth,
computing power, or operating system data structures by sending the malicious
traffic. We propose an information-theoretic frame work that models the
flooding attacks using Botnet on ITM. Based on this model we generalize the
flooding attacks and propose an effective attack detection using Honeypots
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