An adversary engages in activity to detect the version or type of OS software in a an environment by passively monitoring communication between devices, nodes, or applications. Passive techniques for operating system detection send no actual probes to a target, but monitor network or client-server communication between nodes in order to identify operating systems based on observed behavior as compared to a database of known signatures or values. While passive OS fingerprinting is not usually as reliable as active methods, it is generally better able to evade detection.
Likelihood Of Attack
The table below shows the other attack patterns and high level categories that are related to this attack pattern. These relationships are defined as ChildOf and ParentOf, and give insight to similar items that may exist at higher and lower levels of abstraction. In addition, relationships such as CanFollow, PeerOf, and CanAlsoBe are defined to show similar attack patterns that the user may want to explore.
Meta Attack Pattern - A meta level attack pattern in CAPEC is a decidedly abstract characterization of a specific methodology or technique used in an attack. A meta attack pattern is often void of a specific technology or implementation and is meant to provide an understanding of a high level approach. A meta level attack pattern is a generalization of related group of standard level attack patterns. Meta level attack patterns are particularly useful for architecture and design level threat modeling exercises.
The ability to monitor network communications.Access to at least one host, and the privileges to interface with the network interface card.
Any tool capable of monitoring network communications, like a packet sniffer (e.g., Wireshark)
The table below specifies different individual consequences associated with the attack pattern. The Scope identifies the security property that is violated, while the Impact describes the negative technical impact that arises if an adversary succeeds in their attack. The Likelihood provides information about how likely the specific consequence is expected to be seen relative to the other consequences in the list. For example, there may be high likelihood that a pattern will be used to achieve a certain impact, but a low likelihood that it will be exploited to achieve a different impact.
A Related Weakness relationship associates a weakness with this attack pattern. Each association implies a weakness that must exist for a given attack to be successful. If multiple weaknesses are associated with the attack pattern, then any of the weaknesses (but not necessarily all) may be present for the attack to be successful. Each related weakness is identified by a CWE identifier.
[REF-33] Stuart McClure, Joel Scambray
and George Kurtz. "Hacking Exposed: Network Security Secrets & Solutions". Chapter 2: Scanning, pg. 56. 6th Edition. McGraw Hill. 2009.
[REF-128] Defense Advanced Research Projects Agency Information Processing Techniques Office and
Information Sciences Institute University of Southern California. "RFC793 - Transmission Control Protocol". Defense Advanced Research Projects Agency (DARPA). 1981-09.
[REF-212] Gordon "Fyodor" Lyon. "Nmap Network Scanning: The Official Nmap Project Guide to Network Discovery and Security Scanning". Chapter 8. Remote OS Detection. 3rd "Zero Day" Edition,. Insecure.com LLC. 2008.
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September 30, 2019