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Record W207420180

Using Kerberos to provide secure authentication for DB2

2011· article· en· W207420180 on OpenAlexaff
Greg Stager

Bibliographic record

VenueConference of the Centre for Advanced Studies on Collaborative Research · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicBig Data Technologies and Applications
Canadian institutionsIBM (Canada)
Fundersnot available
KeywordsKerberosComputer securityComputer scienceAuthentication (law)Authentication protocolSingle sign-onPassword
DOInot available

Abstract

fetched live from OpenAlex

During your average day, how many times do you need to type in a username and password? It's common for this to be at least a dozen times, and quite often many more. Wouldn't you prefer to do it only once, when you log into your operating system? This goal is known as single sign-on, a method where a person must only provide authenticating credentials once, and be able to gain access to multiple independent systems in a highly secure manner. Kerberos is the premier technology used to provide single sign-on. The Kerberos protocol, based around a trusted third party and ticket service, is highly robust and efficient. Its use of strong encryption provides a secure method of authentication over an insecure network. As a kerberized application, DB2 uses Kerberos to provide secure authentication. Developed by the Massachusetts Institute of Technology (MIT), Kerberos is a reference to the three-headed dog named Cerberus that guarded that gates of Hades in ancient Greek mythology. Cerberus, a vicious monster by all accounts, was responsible for ensuring only the souls of people who had died could enter Hades, and that no one could leave. Certainly very vivid imagery for an authentication system. The modern day Kerberos authentication protocol provides a similar level of security, ensuring that clients can authenticate themselves securely to a server or an insecure network, as well as providing options for data integrity and data privacy of any messages exchanged. So not only is Cerberus guarding the gate, it's making sure no one eavesdrops on conversations. A trusted third party, named the Key Distribution Center (KDC) forms the basis of the Kerberos protocol. The KDC shares the encryption keys for all principals making use of Kerberos. One non-traditional aspect of Kerberos is the principal, which is the identity that is either requesting or accepting connections to/from others. Thus servers also have identities and are referenced by their principal name. When a client logs into Kerberos, it contacts the KDC requesting a special ticket called a ticket-granting ticket (TGT). Clients authenticate themselves to servers by requesting a service ticket from the KDC, utilizing the TGT previously obtained. The client then passes this ticket to the server as proof of its identity. The server receives the ticket, examines the contents, and based on its trust of the KDC, knows who the client is. All tickets are encrypted with the keys for the appropriate principal so that they can not be tampered with. Tickets have limited lifetimes and Kerberos contains measures to prevent tickets from being re-used. In addition, Kerberos supports mutual authentication whereby a server proves its identity to a client by returning its own ticket to the client. The client is then assured the other end of the communication pipe is who it thinks it is talking to and not someone masquerading as the server. The use of encrypted tickets provides one of the fundamental strengths of the Kerberos protocol, that passwords are never sent over the network or placed in a ticket during day-to-day use. The KDC knows the users password, as does the user, but Kerberos does not require the password to be sent on the network, and there is never a chance for it to fall into the wrong hands or be exposed. The use of encrypted tickets also provides a highly efficient workflow. The KDC does not need to remember state information about which clients are connecting to which servers, or which clients have logged in. It merely examines the contents of the encrypted tickets to make a decision as to whether or not the client has proven its identity or that it should be able to connect to a server. As the KDC is a single-point of failure for all authentication, this simplified protocol allows for a very robust and fast service, critical for any enterprise environment. Kerberos has been the authentication mechanism used for Microsoft Windows since Windows 2000. Every Windows Active Directory is also a fully functioning Kerberos KDC. Every major UNIX/Linux provider supports Kerberos, often based on the MIT reference code. There is no doubt that Kerberos is an enterprise level authentication solution. Kerberos is governed by several standards, including RFC-1510 describing the protocol, and RFC-1964 describing the GSS-API (Generic Security Services API) implementation. The MIT Kerberos Consortium is a guiding body consisting of a mix of academia and large corporations that guide the current development of Kerberos. With on-going code updates, conferences and RFC updates, Kerberos is a modern and effective authentication solution. DB2 is an enterprise relational database management system. As a client-server product, DB2 is kerberized, meaning that it supports Kerberos as an authentication mechanism. DB2 integrates with the implementation of Kerberos provided by the operating system. DB2 supports cross-platform, cross-realm authentication using Kerberos to provide highly secure single sign-on capability. Kerberos support in DB2 is implemented through client and server security plugins that are accessed through the GSS-API interface. This plugin infrastructure, along with a sample Kerberos plugin, has allowed users to customize the plugin to suite their own Kerberos infrastructure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.005
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.017

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.791
GPT teacher head0.557
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2011
Admission routes1
Has abstractyes

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