Knowledge (XXG)

Open-source software security

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bugs found in software. The level of quality and security is measured in rungs. Rungs do not have a definitive meaning, and can change as Coverity releases new tools. Rungs are based on the progress of fixing issues found by the Coverity Analysis results and the degree of collaboration with Coverity. They start with Rung 0 and currently go up to Rung 2.
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It is argued that a system is most vulnerable after a potential vulnerability is discovered, but before a patch is created. By measuring the number of days between the vulnerability and when the vulnerability is fixed, a basis can be determined on the security of the system. There are a few caveats
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in collaboration with Stanford University has established a new baseline for open-source quality and security. The development is being completed through a contract with the Department of Homeland Security. They are utilizing innovations in automated defect detection to identify critical types of
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to such an approach: not every vulnerability is equally bad, and fixing a lot of bugs quickly might not be better than only finding a few and taking a little bit longer to fix them, taking into account the operating system, or the effectiveness of the fix.
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Hansen, M., Köhntopp, K., & Pfitzmann, A. (2002). The Open Source approach – opportunities and limitations with respect to security and privacy. Computers & Security, 21 (5), 461–471. Retrieved 5 May 2008, from Computer
76:, an expert on security system design and implementation, released his first public firewall toolkit. At one time, there were over 2,000 sites using his toolkit, but only 10 people gave him any feedback or patches. 51:
to create faulty executables that are unwittingly produced by a well-intentioned developer. With access to the source code for the compiler, the developer has at least the ability to discover if there is any
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At rung 1, there is collaboration between Coverity and the development team. The software is analyzed with a subset of the scanning features to prevent the development team from being overwhelmed.
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Having a large amount of eyes reviewing code can "lull a user into a false sense of security". Having many users look at source code does not guarantee that security flaws will be found and fixed.
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is based on the idea that an enemy can steal a secure military system and not be able to compromise the information. His ideas were the basis for many modern security practices, and followed that
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rates mutual funds. With a large enough data set, statistics could be used to measure the overall effectiveness of one group over the other. An example of such as system is as follows:
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can be used to measure the rates at which different people find security flaws between open and closed source software. The process can be broken down by the number of volunteers N
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There are 11 projects that have been analyzed and upgraded to the status of Rung 2 by reaching zero defects in the first year of the scan. These projects include: AMANDA,
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Hoepman, J.-H., & Jacobs, B. (2007). Increased Security Through Open Source. Communications of the ACM, 50 (1), 79–83. Retrieved 5 May 2008, from ACM Digital Library.
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Cowan, C. (January 2003). Software Security for Open-Source Systems. IEEE Security & Privacy, 38–45. Retrieved 5 May 2008, from IEEE Computer Society Digital Library.
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There are a variety of models and metrics to measure the security of a system. These are a few methods that can be used to measure the security of software systems.
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forces the user to accept the level of security that the software vendor is willing to deliver and to accept the rate that patches and updates are released.
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Lawton, G. (March 2002). Open Source Security: Opportunity or Oxymoron? Computer, 18–21. Retrieved 5 May 2008, from IEEE Computer Society Digital Library.
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The project has been analyzed by Coverity's Scan infrastructure, but no representatives from the open-source software have come forward for the results.
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By comparing a large variety of open source and closed source projects a star system could be used to analyze the security of the project similar to how
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Witten, B., Landwehr, C., & Caloyannides, M. (2001, September/October). Does Open Source Improve System Security?
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It is assumed that any compiler that is used creates code that can be trusted, but it has been demonstrated by
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Simply making source code available does not guarantee review. An example of this occurring is when
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is the measure of assurance or guarantee in the freedom from danger and risk inherent to an
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and the rate that paid reviewers find a flaw is measured by λ
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Comparison of open-source and closed-source software
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Retrieved 18 May 2008, from Raindrop. 269: 157:1 Star: Many security vulnerabilities. 92:Number of days between vulnerabilities 7: 1020:Microsoft Open Specification Promise 487:Alternative terms for free software 827:Python Software Foundation License 14: 890:Definition of Free Cultural Works 507:Free software project directories 1077: 1076: 1066: 527:Open-source software development 346:Accelerating Open Source Quality 885:Debian Free Software Guidelines 717:Free Software Movement of India 258:Open Source Security Foundation 1: 875:Contributor License Agreement 689:Open-source-software movement 473:Free and open-source software 160:2 Stars: Reliability issues. 17:Open-source software security 1046:The Cathedral and the Bazaar 900:The Free Software Definition 950:Mozilla software rebranding 915:Permissive software license 331:Peterson, G. 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(n.d.). 84:Metrics and models 62:is a bad practice. 1103:Computer security 1090: 1089: 990:Trusted Computing 980:Software security 928: 927: 609:Operating systems 517:Long-term support 151:Morningstar, Inc. 145:Morningstar model 49:compiler backdoor 1115: 1080: 1079: 1070: 975:Software patents 858: 770:Creative Commons 629:Web applications 466: 459: 452: 443: 437:Linux Foundation 383: 369: 356: 342: 336: 329: 323: 319: 313: 310: 304: 301: 295: 288: 277: 274: 1123: 1122: 1118: 1117: 1116: 1114: 1113: 1112: 1093: 1092: 1091: 1086: 1058: 1025:Open-core model 1000: 994: 924: 862: 856: 726: 703: 665: 548: 541: 475: 470: 392: 387: 386: 380:Wayback Machine 373:Scan Ladder FAQ 370: 359: 353:Wayback Machine 343: 339: 330: 326: 320: 316: 311: 307: 302: 298: 289: 280: 275: 271: 266: 254: 176: 147: 140: 136: 132: 128: 124: 120: 116: 112: 107:Poisson process 103: 101:Poisson process 94: 86: 69: 34: 29: 12: 11: 5: 1121: 1119: 1111: 1110: 1105: 1095: 1094: 1088: 1087: 1085: 1084: 1074: 1063: 1060: 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745:Apache 694:Events 594:Health 567:Codecs 244:, and 238:Python 207:Rung 2 197:Rung 1 187:Rung 0 847:WTFPL 557:Audio 242:Samba 852:zlib 775:CDDL 750:APSL 226:Perl 105:The 817:MPL 812:MIT 807:ISC 780:EPL 765:BSD 740:AFL 435:by 423:at 246:tcl 230:PHP 214:ntp 141:). 1099:: 413:. 402:, 398:: 360:^ 281:^ 248:. 240:, 236:, 232:, 228:, 220:, 216:, 465:e 458:t 451:v 429:) 139:p 137:λ 135:p 131:v 129:λ 127:v 123:p 119:v 115:p 111:v

Index

open-source software
Proprietary software
Ken Thompson
compiler backdoor
Kerckhoffs' principle
security through obscurity
Marcus Ranum
Poisson process
Morningstar, Inc.
Coverity
ntp
OpenPAM
OpenVPN
Perl
PHP
Postfix
Python
Samba
tcl
Open Source Security Foundation



Stalking the right software security metric
Accelerating Open Source Quality
Archived
Wayback Machine


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