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  1. Proceedings of the 2013 New Security Paradigms Workshop (NSPW '13)
  2. Detecting hidden enemy lines in IP address space
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Forgive and forget: return to obscurity
Towards the realization of a public health system for shared secure cyber-space
Detecting hidden enemy lines in IP address space
Useful password hashing: how to waste computing cycles with style
Markets for zero-day exploits: ethics and implications
Principles of authentication
Towards narrative authentication: or, against boring authentication
Go with the flow: toward workflow-oriented security assessment
Explicit authentication response considered harmful
Can we sell security like soap?: a new approach to behaviour change
Booby trapping software
Information behaving badly
Designing forensic analysis techniques through anthropology
NSPHD: the polyglot computer

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Detecting hidden enemy lines in IP address space

Content Provider ACM Digital Library
Author Mathur, Suhas Balakrishnan, Suhrid Coskun, Baris
Abstract If an outbound flow is observed at the boundary of a protected network, destined to an IP address within a few addresses of a known malicious IP address, should it be considered a suspicious flow? Conventional blacklisting is not going to cut it in this situation, and the established fact that malicious IP addresses tend to be highly clustered in certain portions of IP address space, should indeed raise suspicions. We present a new approach for perimeter defense that addresses this concern. At the heart of our approach, we attempt to infer internal, hidden boundaries in IP address space, that lie within publicly known boundaries of registered IP netblocks. Our hypothesis is that given a known bad IP address, other IP address in the same internal contiguous block are likely to share similar security properties, and may therefore be vulnerable to being similarly hacked and used by attackers in the future. In this paper, we describe how we infer hidden internal boundaries in IPv4 netblocks, and what effect this has on being able to predict malicious IP addresses.
Starting Page 19
Ending Page 30
Page Count 12
File Format PDF
ISBN 9781450325820
DOI 10.1145/2535813.2535816
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-12-09
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Statistical fingerprinting Blacklists Predictive modeling Clustering
Content Type Text
Resource Type Article
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