Approximate matching is a term used in computer forensics to mean that two objects have similar contents but are not identically the same. It replaced the previously used terms similarity and fuzzy hashing.
The following two paragraphs are clearly similar but not identical:
We the People of the United States, in Order to form a more perfect Union, establish Justice, insure domestic Tranquility, provide for the common defence, promote the general Welfare, and secure the Blessings of Liberty to ourselves and our Posterity, do ordain and establish this Constitution for the United States of America.
We the People of the United States, in Order to form a more perfect Union, establish Justice, insure domestic Tranquility, provide for the common defense, promote the general Welfare, and secure the Blessings of Liberty to ourselves and our Posterity, do ordain and establish this Constitution for the United States of America.
Kinds of Similarity
In forensics there are several kinds of similarity that are of interest:
- Binary Similarity
- Textual Similarity
- Visual Similarity
- Audible Similarity
- Algorithmic (code) Similarity
Binary Similarity between a master object and a target objectcan be rigorously defined as the fraction of substrings that the two documents have in common divided by the total number of substrings in the master document. Notice that this implies that the similarity function does not have the commutative property. That is, BS(a,b) may not equal BS(b,a).
There are several applications for a binary similarity function:
- Determining that a master object is embedded in the target object.
- Determining if the target object is derived from the target object.
The leading similarity systems in use are are:
- sdhash, developed by Vassil Roussev.
- ssdeep, the first widely used binary similarity algorithm. Developed by Jesse Kornblum, this system uses a piecewise hash comparison algorithm originally developed for anti-spam systems.
The leading text similarity system is:
- sdtext, developed by Clay Sheilds.
Jesse Kornblum, “Identifying almost identical files using context triggered piecewise hashing,” Jesse Kornblum, DFRWS 2006, Digital Investigation 3S, S91-S97
Jiang, Z.L., Hui, L.C.K., Chow, K.P., Yiu, S.M., Lai, P.K.Y. Improving disk sector integrity using 3-dimension hashing scheme, Proceedings of Future Generation Communication and Networking, FGCN 2007 2, art. no. 4426219, pp. 141-145, 2007
Rönnau, S., Pauli, C., Borghoff, U.M. Merging changes in XML documents using reliable context fingerprints (2008) DocEng'08 - Proceedings of the 8th ACM Symposium on Document Engineering, pp. 52-61.
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Maartmann-Moe, C., Thorkildsen, S.E., André Årnes The persistence of memory: Forensic identification and extraction of cryptographic keys (2009) Digital Investigation, 6 (SUPPL.), pp. S132-S140.
Seo, K., Lim, K., Choi, J., Chang, K., Lee, S. Detecting similar files based on hash and statistical analysis for digital forensic investigation (2009) Proceedings of the 2009 2nd International Conference on Computer Science and Its Applications, CSA 2009, art. no. 5404198.
Garcia, J., Holleboom, T. Retention of micro-fragments in cluster slack - A first model (2009) Proceedings of the 2009 1st IEEE International Workshop on Information Forensics and Security, WIFS 2009, art. no. 5386487, pp. 31-35.
Vassil Roussev, Hashing and data fingerprinting in digital forensics, IEEE Security and Privacy 7(2), 2009, pp. 49-55
C. Nickel, C. Busch, X. Zhou, Template protection via piecewise hashing, IIH-MSP 2009 - 2009 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, art. no. 5337554, pp. 1056-1060
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Wu, Y., Yang, K., Zhang, J. Using DBSCAN clustering algorithm in spam identifying (2010) ICETC 2010 - 2010 2nd International Conference on Education Technology and Computer, 1, art. no. 5529221, pp. V1398-V1402.
Fang, J., Jiang, Z.L., Yiu, S.M., Hui, L.C.K. An efficient scheme for hard disk integrity check in digital forensics by hashing with combinatorial group testing (2011) International Journal of Digital Content Technology and its Applications, 5 (2), pp. 300-308.
Jiang, Z.L., Fang, J.-B., Hui, L.C.K., Yiu, S., Chow, K.P., Sheng, M.-M. K-Dimensional hashing scheme for hard disk integrity verification in computer forensics (2011) Journal of Zhejiang University: Science C, 12 (10), pp. 809-818.
Harald Baier, Frank Breitinger: Security Aspects of Piecewise Hashing in Computer Forensics, 6th International Conference on IT Security Incident Management & IT Forensics (IMF), Stuttgart (Germany). May 2011.
Frank Breitinger, Harald Baier: Performance Issues about Context Triggered Piecewise Hashing, 3rd International ICST Conference on Digital Forensics & Cyber Crime (ICDF2C), Dublin (Ireland). October 2011.
Wardman, B., Stallings, T., Warner, G., Skjellum, A. High-performance content-based phishing attack detection (2011) eCrime Researchers Summit, eCrime, art. no. 6151977, .
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Carlos G. Figuerola, Raquel Gómez Díaz, José L. Alonso Berrocal, Angel F. Zazo Rodríguez, Web Document Duplicate Detection Using Fuzzy Hashing, in Trends in Practical Applications of Agents and Multiagent Systems, Advances in Intelligent and Soft Computing, Volume 90, 2011, pp. 117-125, Springer
Jozwiak, I., Kedziora, M. Efficient N-Byte slack space hashing in retrieving and identifying partially recovered data (2011) ICSOFT 2011 - Proceedings of the 6th International Conference on Software and Database Technologies, 1, pp. 309-312.
Song, X., Deng, H., Xiong, Z. Using piecewise hashing and Lagrange interpolation polynomial to preserve electronic evidence (2011) Communications in Computer and Information Science, 201 CCIS (PART 1), pp. 472-480.
Frank Breitinger, Harald Baier: A Fuzzy Hashing Approach based on Random Sequences and Hamming Distance, 7th annual Conference on Digital Forensics, Security and Law (ADFSL), Richmond (Virginia, US). May 2012.
Frank Breitinger and Harald Baier, “Performance Issues About Context-Triggered Piecewise Hashing,” in P. Gladyshev and M. K. Rogers (Eds): ICDF2C 2011, LNICST 88, 2012, pp. 141-155, 2012.
Thonnard, O., Bilge, L., O'Gorman, G., Kiernan, S., Lee, M. Industrial espionage and targeted attacks: Understanding the characteristics of an escalating threat (2012) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7462 LNCS, pp. 64-85.
Garcia, J. Quantifying the benefits of file size information for forensic hash matching (2012) SECRYPT 2012 - Proceedings of the International Conference on Security and Cryptography, pp. 333-338.
Frank Breitinger, Knut Petter Åstebøl, Harald Baier, Christoph Busch: mvHash-B – A new Approach for Similarity Preserving Hash Function. 7th International Conference on IT Security Incident Management & IT Forensics (IMF), Nürnberg (Germany). March 2013.
Christian Rathgeb, Frank Breitinger, Christoph Busch: Alignment-Free Cancelable Iris Biometric Templates based on Adaptive Bloom Filters, In Proceedings of the 6th IAPR International Conference on Biometrics (ICB’13), Madrid (Spain). June 2013. To appear.
Frank Breitinger, Georgios Stivaktakis, Harald Baier: FRASH: A framework to test algorithms of similarity hashing, In Proceedings of the 13th Digital Forensics Research Conference (DFRWS’13), Monterey (Californien, US). August 2013. To appear.
Books that mention SSDEEP: Harlan Carvey, Windows Forensic Analysis, Syngress, June 2009,
Cameron H. Malin, Eoghan Casey, James M. Aquilina, Malware Forensics: Investigating and Analyzing Malicious Code, Syngress, Aug 2008
Cameron H. Malin, Eoghan Casey, James M. Aquilina, Malware Forensics Field Guide for Windows Systems, Elsevier, May 2012,
Frank Breitinger, Kaloyan Petrov: Reducing time cost in hashing operations. Ninth Annual IFIP WG 11.9 International Conference on Digital Forensics (IFIP WG11.9), Orlando (Florida, US). January 2013.
Roussev, V. Building open and scalable digital forensic tools (2011) 2011 6th IEEE International Workshop on Systematic Approaches to Digital Forensic Engineering, SADFE 2011, art. no. 6159116, .
Vassil Roussev, “An evaluation of forensic similarity hashes,” Digital Investigation 8 (2011), S34-S41
Breitinger, F.; Baier, H., "Properties of a similarity preserving hash function and their realization in sdhash," Information Security for South Africa (ISSA), 2012, vol., no., pp.1,8, 15-17 Aug. 2012
Frank Breitinger, Harald Baier, Jesse Beckingham: Security and Implementation Analysis of the Similarity Digest sdhash, 1st International Baltic Conference on Network Security & Forensics (NeSeFo), Tartu (Estland). August 2012.
Frank Breitinger, Harald Baier: Properties of a Similarity Preserving Hash Function and their Realization in sdhash. 2012 Information Security South Africa (ISSA 2012), Johannesburg (South Africa). August 2012.
Vassil Roussev, “Managing terabyte-scale investigations with similarity digests,” IFIP Advances in Informaiton and Communication Technology 383, AICT, pp. 19-34
Clay Shields, O. Frieder, M. Maloof, “A system for the proactive, continuous, and efficient collection of digital forensic evidence,” DFRWS 2011 Annual Conference, pp. S3-S13
Frank Breitinger, Harald Baier: Similarity Preserving Hashing: Eligible Properties and a new Algorithm MRSH-v2. 4th International ICST Conference on Digital Forensics & Cyber Crime (ICDF2C), Lafayette (Indiana, US). October 2012.