DOCSLIB.ORG

Data Compression “Standard” Approaches

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Data Compression “Standard” Approaches Data Compression “Standard” Approaches GWDAW 2000 Benoit MOURS, Caltech & LAPP-Annecy LIGO-G000346-00-E Data Compression Why should you care? ● Interferometers produce several Mbytes/s (~100Tb/y) » Data Handling is complex. » Archiving cost is important. » Disk space is a limit in your analysis. ● Data compression could improve » Data access from the archive, network, nfs disks... » Data lifetime » Speed of your analysis program ● But data compression may change the data... LIGO-G000346-00-E GWDAW-2000 B. Mours Data Compression 2 Data Compression What could we do? ● Record the right data » Right sampling frequency? » Right electronic gain? (do not record too much electronic noise) » Right format (integer or float)? ● Compress the data » Without loss of information (like gzip). » With some loss of information. » By converting large vector to a few statistical information. Produce different kind of data sets (metadata) LIGO-G000346-00-E GWDAW-2000 B. Mours Data Compression 3 Available Frame Compression (Format Spec. & I/O library) ● Only lossless compression method ● Compression done at the vector level » No need to uncompress unused channels. ● Standard gzip » Integer are differentiate to improve compression rate. ● Zero suppress » Differentiate date are store with the minimal number of bit needed. » Available only for integer. LIGO-G000346-00-E GWDAW-2000 B. Mours Data Compression 4 Data Compression Performances ● Numbers from the November E2 Ligo run Full Frames Reduced' Frames Raw Size 3.2 Mb/s 1.5 MB/s Size after gzip + Zero supp. 1.7Mb/s 1.0 Mb/s Fraction of float 31% 64% Compression ratio for short 2.16 1.98 Compression ratio for float 1.33 1.33 gzip speed (float) 2Mb/s 2Mb/s Zero supress speed (short) 8Mb/s 8Mb/s Speed measured on a Sun Ultra 10. ● Remarks: » Poor speed and compression ratio for float » People want floating points » More floating points than originally foreseen LIGO-G000346-00-E GWDAW-2000 B. Mours Data Compression 5 New Compression for float? ● Principle: convert float to integer ● Method: » Differentiate the data and digitize the differences: (si+1- si)/k » Round off is done by checking that the rebuild data do not diverge from the original date. ● Data saved » First value, the differences convert to an integer, a scaling factor. ● One parameter: number of bits to store the integer ● Compression rate: 32/number of bits » Example: If result stored on 16 bits: compression of 2 ● Speed: Fast: 20 Mbytes/s (if result stored on 16 bits) LIGO-G000346-00-E GWDAW-2000 B. Mours Data Compression 6 Compression for float: Noise H2:LSC-AS_Q Original spectrum noise for 8 bits 6 10 noise for 12 bits noise for 16 bits noise for 16 bits, no integration check 5 10 <amplitude> 4 10 3 10 2 10 10 1 -1 10 2 3 10 10 10frequency[Hz] LIGO-G000346-00-E GWDAW-2000 B. Mours Data Compression 7 Noise for other channels H2:SUS-ETMX_SENSOR_LR H2:LSC-MICH_CTRL H2:ASC-ITMY_OPLEV_PITCH 4 Original spectrum 4 10 10 float to 8 bits 5 3 10 10 float to 12 bits 3 4 2 <amplitude> <amplitude> 10<amplitude> 10 10 float to 16 bits 3 10 10 2 10 2 10 1 -1 10 10 10 -2 1 10 1 -1 -3 10 10 -4 -2 10 10 200 400 600 800 1000 1000 2000 3000 4000 5000 6000 7000 8000 200 400 600 800 1000 frequency[Hz] frequency[Hz] frequency[Hz] H0:PEM-EX_V2 H0:PEM-BSC5_MIC H2:IOO-MC_REFLPD 6 6 10 3 10 10 5 5 10 10 2 <amplitude> <amplitude> <amplitude> 10 4 4 10 10 10 3 10 3 10 1 2 10 2 10 -1 10 10 10 -2 1 10 -1 1 10 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800 1000 frequency[Hz] frequency[Hz] frequency[Hz] LIGO-G000346-00-E GWDAW-2000 B. Mours Data Compression 8 Noise for other channels H2:SUS-ETMX_SENSOR_LR H2:LSC-MICH_CTRL H2:ASC-ITMY_OPLEV_PITCH Original spectrum 6 float to 8 bits 10 4 4 10 10 float to 12 bits 5 10 3 float to 16 bits 10 4 <amplitude> 3 <amplitude> <amplitude> 2 10 10 10 3 10 10 2 10 2 10 1 -1 10 10 10 -2 1 10 -1 -3 1 10 10 -2 -4 10 10 2 3 2 3 2 3 10 10 frequency[Hz]10 10 10 frequency[Hz]10 10 10 frequency[Hz]10 H0:PEM-EX_V2 H0:PEM-BSC5_MIC H2:IOO-MC_REFLPD 6 6 10 3 10 10 5 5 10 10 2 <amplitude> <amplitude> <amplitude> 10 4 10 4 10 10 3 10 3 10 1 2 10 2 10 -1 10 10 10 -2 1 10 -1 1 10 2 3 2 3 2 3 10 10 frequency[Hz]10 10 10 frequency[Hz]10 10 10 frequency[Hz]10 LIGO-G000346-00-E GWDAW-2000 B. Mours Data Compression 9 Summary ● The best ‘compression’ is to record only what you need: » right channel, right frequency, right type (integer are better than float) ● Existing tools » Work OK short integer. » Poor on float. ● Simple lossy compression for float seems possible » Small white noise introduced. » Fast. » Data could be stored in 8 to 16 bits. » But not as good as if the data were stored as integer (8 bits enough). ● More aggressive compression? see S. Klimenko talk LIGO-G000346-00-E GWDAW-2000 B. Mours Data Compression 10.
Load more

Recommended publications
  • Contrasting the Performance of Compression Algorithms on Genomic Data
    Contrasting the Performance of Compression Algorithms on Genomic Data Cornel Constantinescu, IBM Research Almaden Outline of the Talk: • Introduction / Motivation • Data used in experiments • General purpose compressors comparison • Simple Improvements • Special purpose compression • Transparent compression – working on compressed data (prototype) • Parallelism / Multithreading • Conclusion Introduction / Motivation • Despite the large number of research papers and compression algorithms proposed for compressing genomic data generated by sequencing machines, by far the most commonly used compression algorithm in the industry for FASTQ data is gzip. • The main drawbacks of the proposed alternative special-purpose compression algorithms are: • slow speed of either compression or decompression or both, and also their • brittleness by making various limiting assumptions about the input FASTQ format (for example, the structure of the headers or fixed lengths of the records [1]) in order to further improve their specialized compression. 1. Ibrahim Numanagic, James K Bonfield, Faraz Hach, Jan Voges, Jorn Ostermann, Claudio Alberti, Marco Mattavelli, and S Cenk Sahinalp. Comparison of high-throughput sequencing data compression tools. Nature Methods, 13(12):1005–1008, October 2016. Fast and Efficient Compression of Next Generation Sequencing Data 2 2 General Purpose Compression of Genomic Data As stated earlier, gzip/zlib compression is the method of choice by the industry for FASTQ genomic data. FASTQ genomic data is a text-based format (ASCII readable text) for storing a biological sequence and the corresponding quality scores. Each sequence letter and quality score is encoded with a single ASCII character. FASTQ data is structured in four fields per record (a “read”). The first field is the SEQUENCE ID or the header of the read.
    [Show full text]
  • Pack, Encrypt, Authenticate Document Revision: 2021 05 02
    PEA Pack, Encrypt, Authenticate Document revision: 2021 05 02 Author: Giorgio Tani Translation: Giorgio Tani This document refers to: PEA file format specification version 1 revision 3 (1.3); PEA file format specification version 2.0; PEA 1.01 executable implementation; Present documentation is released under GNU GFDL License. PEA executable implementation is released under GNU LGPL License; please note that all units provided by the Author are released under LGPL, while Wolfgang Ehrhardt’s crypto library units used in PEA are released under zlib/libpng License. PEA file format and PCOMPRESS specifications are hereby released under PUBLIC DOMAIN: the Author neither has, nor is aware of, any patents or pending patents relevant to this technology and do not intend to apply for any patents covering it. As far as the Author knows, PEA file format in all of it’s parts is free and unencumbered for all uses. Pea is on PeaZip project official site: https://peazip.github.io , https://peazip.org , and https://peazip.sourceforge.io For more information about the licenses: GNU GFDL License, see http://www.gnu.org/licenses/fdl.txt GNU LGPL License, see http://www.gnu.org/licenses/lgpl.txt 1 Content: Section 1: PEA file format ..3 Description ..3 PEA 1.3 file format details ..5 Differences between 1.3 and older revisions ..5 PEA 2.0 file format details ..7 PEA file format’s and implementation’s limitations ..8 PCOMPRESS compression scheme ..9 Algorithms used in PEA format ..9 PEA security model .10 Cryptanalysis of PEA format .12 Data recovery from
    [Show full text]
  • Improved Neural Network Based General-Purpose Lossless Compression Mohit Goyal, Kedar Tatwawadi, Shubham Chandak, Idoia Ochoa
    JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 DZip: improved neural network based general-purpose lossless compression Mohit Goyal, Kedar Tatwawadi, Shubham Chandak, Idoia Ochoa Abstract—We consider lossless compression based on statistical [4], [5] and generative modeling [6]). Neural network based data modeling followed by prediction-based encoding, where an models can typically learn highly complex patterns in the data accurate statistical model for the input data leads to substantial much better than traditional finite context and Markov models, improvements in compression. We propose DZip, a general- purpose compressor for sequential data that exploits the well- leading to significantly lower prediction error (measured as known modeling capabilities of neural networks (NNs) for pre- log-loss or perplexity [4]). This has led to the development of diction, followed by arithmetic coding. DZip uses a novel hybrid several compressors using neural networks as predictors [7]– architecture based on adaptive and semi-adaptive training. Unlike [9], including the recently proposed LSTM-Compress [10], most NN based compressors, DZip does not require additional NNCP [11] and DecMac [12]. Most of the previous works, training data and is not restricted to specific data types. The proposed compressor outperforms general-purpose compressors however, have been tailored for compression of certain data such as Gzip (29% size reduction on average) and 7zip (12% size types (e.g., text [12] [13] or images [14], [15]), where the reduction on average) on a variety of real datasets, achieves near- prediction model is trained in a supervised framework on optimal compression on synthetic datasets, and performs close to separate training data or the model architecture is tuned for specialized compressors for large sequence lengths, without any the specific data type.
    [Show full text]
  • User Commands GZIP ( 1 ) Gzip, Gunzip, Gzcat – Compress Or Expand Files Gzip [ –Acdfhllnnrtvv19 ] [–S Suffix] [ Name ... ]
    User Commands GZIP ( 1 ) NAME gzip, gunzip, gzcat – compress or expand files SYNOPSIS gzip [–acdfhlLnNrtvV19 ] [– S suffix] [ name ... ] gunzip [–acfhlLnNrtvV ] [– S suffix] [ name ... ] gzcat [–fhLV ] [ name ... ] DESCRIPTION Gzip reduces the size of the named files using Lempel-Ziv coding (LZ77). Whenever possible, each file is replaced by one with the extension .gz, while keeping the same ownership modes, access and modification times. (The default extension is – gz for VMS, z for MSDOS, OS/2 FAT, Windows NT FAT and Atari.) If no files are specified, or if a file name is "-", the standard input is compressed to the standard output. Gzip will only attempt to compress regular files. In particular, it will ignore symbolic links. If the compressed file name is too long for its file system, gzip truncates it. Gzip attempts to truncate only the parts of the file name longer than 3 characters. (A part is delimited by dots.) If the name con- sists of small parts only, the longest parts are truncated. For example, if file names are limited to 14 characters, gzip.msdos.exe is compressed to gzi.msd.exe.gz. Names are not truncated on systems which do not have a limit on file name length. By default, gzip keeps the original file name and timestamp in the compressed file. These are used when decompressing the file with the – N option. This is useful when the compressed file name was truncated or when the time stamp was not preserved after a file transfer. Compressed files can be restored to their original form using gzip -d or gunzip or gzcat.
    [Show full text]
  • The Ark Handbook
    The Ark Handbook Matt Johnston Henrique Pinto Ragnar Thomsen The Ark Handbook 2 Contents 1 Introduction 5 2 Using Ark 6 2.1 Opening Archives . .6 2.1.1 Archive Operations . .6 2.1.2 Archive Comments . .6 2.2 Working with Files . .7 2.2.1 Editing Files . .7 2.3 Extracting Files . .7 2.3.1 The Extract dialog . .8 2.4 Creating Archives and Adding Files . .8 2.4.1 Compression . .9 2.4.2 Password Protection . .9 2.4.3 Multi-volume Archive . 10 3 Using Ark in the Filemanager 11 4 Advanced Batch Mode 12 5 Credits and License 13 Abstract Ark is an archive manager by KDE. The Ark Handbook Chapter 1 Introduction Ark is a program for viewing, extracting, creating and modifying archives. Ark can handle vari- ous archive formats such as tar, gzip, bzip2, zip, rar, 7zip, xz, rpm, cab, deb, xar and AppImage (support for certain archive formats depends on the appropriate command-line programs being installed). In order to successfully use Ark, you need KDE Frameworks 5. The library libarchive version 3.1 or above is needed to handle most archive types, including tar, compressed tar, rpm, deb and cab archives. To handle other file formats, you need the appropriate command line programs, such as zipinfo, zip, unzip, rar, unrar, 7z, lsar, unar and lrzip. 5 The Ark Handbook Chapter 2 Using Ark 2.1 Opening Archives To open an archive in Ark, choose Open... (Ctrl+O) from the Archive menu. You can also open archive files by dragging and dropping from Dolphin.
    [Show full text]
  • Arrow: Integration to 'Apache' 'Arrow'
    Package ‘arrow’ September 5, 2021 Title Integration to 'Apache' 'Arrow' Version 5.0.0.2 Description 'Apache' 'Arrow' <https://arrow.apache.org/> is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. This package provides an interface to the 'Arrow C++' library. Depends R (>= 3.3) License Apache License (>= 2.0) URL https://github.com/apache/arrow/, https://arrow.apache.org/docs/r/ BugReports https://issues.apache.org/jira/projects/ARROW/issues Encoding UTF-8 Language en-US SystemRequirements C++11; for AWS S3 support on Linux, libcurl and openssl (optional) Biarch true Imports assertthat, bit64 (>= 0.9-7), methods, purrr, R6, rlang, stats, tidyselect, utils, vctrs RoxygenNote 7.1.1.9001 VignetteBuilder knitr Suggests decor, distro, dplyr, hms, knitr, lubridate, pkgload, reticulate, rmarkdown, stringi, stringr, testthat, tibble, withr Collate 'arrowExports.R' 'enums.R' 'arrow-package.R' 'type.R' 'array-data.R' 'arrow-datum.R' 'array.R' 'arrow-tabular.R' 'buffer.R' 'chunked-array.R' 'io.R' 'compression.R' 'scalar.R' 'compute.R' 'config.R' 'csv.R' 'dataset.R' 'dataset-factory.R' 'dataset-format.R' 'dataset-partition.R' 'dataset-scan.R' 'dataset-write.R' 'deprecated.R' 'dictionary.R' 'dplyr-arrange.R' 'dplyr-collect.R' 'dplyr-eval.R' 'dplyr-filter.R' 'expression.R' 'dplyr-functions.R' 1 2 R topics documented: 'dplyr-group-by.R' 'dplyr-mutate.R' 'dplyr-select.R' 'dplyr-summarize.R'
    [Show full text]
  • Gzip, Bzip2 and Tar EXPERT PACKING
    LINUXUSER Command Line: gzip, bzip2, tar gzip, bzip2 and tar EXPERT PACKING A short command is all it takes to pack your data or extract it from an archive. BY HEIKE JURZIK rchiving provides many bene- fits: packed and compressed Afiles occupy less space on your disk and require less bandwidth on the Internet. Linux has both GUI-based pro- grams, such as File Roller or Ark, and www.sxc.hu command-line tools for creating and un- packing various archive types. This arti- cle examines some shell tools for ar- chiving files and demonstrates the kind of expert packing that clever combina- tained by the packing process. If you A gzip file can be unpacked using either tions of Linux commands offer the com- prefer to use a different extension, you gunzip or gzip -d. If the tool discovers a mand line user. can set the -S (suffix) flag to specify your file of the same name in the working di- own instead. For example, the command rectory, it prompts you to make sure that Nicely Packed with “gzip” you know you are overwriting this file: The gzip (GNU Zip) program is the de- gzip -S .z image.bmp fault packer on Linux. Gzip compresses $ gunzip screenie.jpg.gz simple files, but it does not create com- creates a compressed file titled image. gunzip: screenie.jpg U plete directory archives. In its simplest bmp.z. form, the gzip command looks like this: The size of the compressed file de- Listing 1: Compression pends on the distribution of identical Compared gzip file strings in the original file.
    [Show full text]
  • Parquet Data Format Performance
    Parquet data format performance Jim Pivarski Princeton University { DIANA-HEP February 21, 2018 1 / 22 What is Parquet? 1974 HBOOK tabular rowwise FORTRAN first ntuples in HEP 1983 ZEBRA hierarchical rowwise FORTRAN event records in HEP 1989 PAW CWN tabular columnar FORTRAN faster ntuples in HEP 1995 ROOT hierarchical columnar C++ object persistence in HEP 2001 ProtoBuf hierarchical rowwise many Google's RPC protocol 2002 MonetDB tabular columnar database “first” columnar database 2005 C-Store tabular columnar database also early, became HP's Vertica 2007 Thrift hierarchical rowwise many Facebook's RPC protocol 2009 Avro hierarchical rowwise many Hadoop's object permanance and interchange format 2010 Dremel hierarchical columnar C++, Java Google's nested-object database (closed source), became BigQuery 2013 Parquet hierarchical columnar many open source object persistence, based on Google's Dremel paper 2016 Arrow hierarchical columnar many shared-memory object exchange 2 / 22 What is Parquet? 1974 HBOOK tabular rowwise FORTRAN first ntuples in HEP 1983 ZEBRA hierarchical rowwise FORTRAN event records in HEP 1989 PAW CWN tabular columnar FORTRAN faster ntuples in HEP 1995 ROOT hierarchical columnar C++ object persistence in HEP 2001 ProtoBuf hierarchical rowwise many Google's RPC protocol 2002 MonetDB tabular columnar database “first” columnar database 2005 C-Store tabular columnar database also early, became HP's Vertica 2007 Thrift hierarchical rowwise many Facebook's RPC protocol 2009 Avro hierarchical rowwise many Hadoop's object permanance and interchange format 2010 Dremel hierarchical columnar C++, Java Google's nested-object database (closed source), became BigQuery 2013 Parquet hierarchical columnar many open source object persistence, based on Google's Dremel paper 2016 Arrow hierarchical columnar many shared-memory object exchange 2 / 22 Developed independently to do the same thing Google Dremel authors claimed to be unaware of any precedents, so this is an example of convergent evolution.
    [Show full text]
  • Is It Time to Replace Gzip?
    bioRxiv preprint doi: https://doi.org/10.1101/642553; this version posted May 20, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Is it time to replace gzip? Comparison of modern compressors for molecular sequence databases Kirill Kryukov*, Mahoko Takahashi Ueda, So Nakagawa, Tadashi Imanishi Department of Molecular Life Science, Tokai University School of Medicine, Isehara, Kanagawa 259-1193, Japan. *Correspondence: [email protected] Abstract Nearly all molecular sequence databases currently use gzip for data compression. Ongoing rapid accumulation of stored data calls for more efficient compression tool. We systematically benchmarked the available compressors on representative DNA, RNA and Protein datasets. We tested specialized sequence compressors 2bit, BLAST, DNA-COMPACT, DELIMINATE, Leon, MFCompress, NAF, UHT and XM, and general-purpose compressors brotli, bzip2, gzip, lz4, lzop, lzturbo, pbzip2, pigz, snzip, xz, zpaq and zstd. Overall, NAF and zstd performed well in terms of transfer/decompression speed. However, checking benchmark results is necessary when choosing compressor for specific data type and application. Benchmark results database is available at: http://kirr.dyndns.org/sequence-compression-benchmark/. Keywords: compression; benchmark; DNA; RNA; protein; genome; sequence; database. Molecular sequence databases store and distribute DNA, RNA and protein sequences as compressed FASTA files. Currently, nearly all databases universally depend on gzip for compression. This incredible longevity of the 26-year-old compressor probably owes to multiple factors, including conservatism of database operators, wide availability of gzip, and its generally acceptable performance.
    [Show full text]
  • Comparison and Model of Compression Techniques for Smart Cloud Log File Handling
    Copyright IEEE. The final publication is available at IEEExplore via https://doi.org/10.1109/CCCI49893.2020.9256609. Comparison and Model of Compression Techniques for Smart Cloud Log File Handling Josef Spillner Zurich University of Applied Sciences Winterthur, Switzerland [email protected] Abstract—Compression as data coding technique has seen tight pricing of offered cloud services. Increasing diversity and approximately 70 years of research and practical innovation. progress in generic compression tools and log-specific algo- Nowadays, powerful compression tools with good trade-offs exist rithms [4], [5], [6] leaves many operators without a systematic for a range of file formats from plain text to rich multimedia. Yet in the dilemma of cloud providers to reduce log data sizes as much framework to choose suitable and economic log compression as possible while having to keep as much as possible around for tools and respective configurations. This prevents a systematic regulatory reasons and compliance processes, many companies solution to exploit cost tradeoffs, such as increasing investment are looking for smarter solutions beyond brute compression. into better compression levels while saving long-term storage In this paper, comprehensive applied research setting around cost. In this paper, such a framework is constructed by giving network and system logs is introduced by comparing text com- pression ratios and performance. The benchmark encompasses a comprehensive overview with benchmark results of 30 to- 13 tools and 30 tool-configuration-search combinations. The tool tal combinations of compression tools, decompression/search and algorithm relationships as well as benchmark results are tools and associated configurations. modelled in a graph.
    [Show full text]
  • The Lzip Format Why a New Format and Tool?
    The lzip format Antonio Díaz Díaz [email protected] http://www.nongnu.org/lzip/lzip_talk_ghm_2019.html http://www.nongnu.org/lzip/lzip_talk_ghm_2019_es.html GNU Hackers Meeting Madrid, September 4th 2019 Introduction There are a lot of compression algorithms Most are just variations of a few basic algorithms The basic ideas of compression algorithms are well known Algorithms much better than those existing are not probable to appear in the foreseeable future Formats existing when lzip was designed in 2008 (gzip and bzip2) have limitations that aren’t easily fixable Therefore... It seemed adequate to pack a good algorithm like LZMA into a well designed format Lzip is an attempt at developing such a format 2 / 20 Antonio Díaz Díaz --- [email protected] The lzip format Why a new format and tool? Adding LZMA compression to gzip doesn't work The gzip format was designed long ago It has limitations ➔ 32-bit uncompressed size ➔ No index If extended it would impose those limitations to the new algorithm +=============+=====================+-+-+-+-+-+-+-+-+ | gzip header | compressed blocks | CRC32 | ISIZE | <-- no index +=============+=====================+-+-+-+-+-+-+-+-+ A new format with support for 64-bit file sizes is needed 3 / 20 Antonio Díaz Díaz --- [email protected] The lzip format LZMA algorithm Features (thanks to Igor Pavlov) Wide range of compression ratios and speeds Higher compression ratio than gzip and bzip2 Faster decompression speed than bzip2 LZMA variants used by lzip Fast (used by option ‘-0’) Normal (used by all other compression
    [Show full text]
  • I Came to Drop Bombs Auditing the Compression Algorithm Weapons Cache
    I Came to Drop Bombs Auditing the Compression Algorithm Weapons Cache Cara Marie NCC Group Blackhat USA 2016 About Me • NCC Group Senior Security Consultant Pentested numerous networks, web applications, mobile applications, etc. • Hackbright Graduate • Ticket scalper in a previous life • @bones_codes | [email protected] What is a Decompression Bomb? A decompression bomb is a file designed to crash or render useless the program or system reading it. Vulnerable Vectors • Chat clients • Image hosting • Web browsers • Web servers • Everyday web-services software • Everyday client software • Embedded devices (especially vulnerable due to weak hardware) • Embedded documents • Gzip’d log uploads A History Lesson early 90’s • ARC/LZH/ZIP/RAR bombs were used to DoS FidoNet systems 2002 • Paul L. Daniels publishes Arbomb (Archive “Bomb” detection utility) 2003 • Posting by Steve Wray on FullDisclosure about a bzip2 bomb antivirus software DoS 2004 • AERAsec Network Services and Security publishes research on the various reactions of antivirus software against decompression bombs, includes a comparison chart 2014 • Several CVEs for PIL are issued — first release July 2010 (CVE-2014-3589, CVE-2014-3598, CVE-2014-9601) 2015 • CVE for libpng — first release Aug 2004 (CVE-2015-8126) Why Are We Still Talking About This?!? Why Are We Still Talking About This?!? Compression is the New Hotness Who This Is For Who This Is For The Archives An archive bomb, a.k.a. zip bomb, is often employed to disable antivirus software, in order to create an opening for more traditional viruses • Singly compressed large file • Self-reproducing compressed files, i.e. Russ Cox’s Zips All The Way Down • Nested compressed files, i.e.
    [Show full text]