Software Vulnerability Detection Using Deep Neural Networks: A Survey
The constantly increasing number of disclosed security vulnerabilities have become an important concern in the software industry and in the field of cybersecurity, suggesting that the current approaches for vulnerability detection demand further improvement. The booming of the open-source software c...
Saved in:
| Published in: | Proceedings of the IEEE Vol. 108; no. 10; pp. 1825 - 1848 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 0018-9219, 1558-2256 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | The constantly increasing number of disclosed security vulnerabilities have become an important concern in the software industry and in the field of cybersecurity, suggesting that the current approaches for vulnerability detection demand further improvement. The booming of the open-source software community has made vast amounts of software code available, which allows machine learning and data mining techniques to exploit abundant patterns within software code. Particularly, the recent breakthrough application of deep learning to speech recognition and machine translation has demonstrated the great potential of neural models' capability of understanding natural languages. This has motivated researchers in the software engineering and cybersecurity communities to apply deep learning for learning and understanding vulnerable code patterns and semantics indicative of the characteristics of vulnerable code. In this survey, we review the current literature adopting deep-learning-/neural-network-based approaches for detecting software vulnerabilities, aiming at investigating how the state-of-the-art research leverages neural techniques for learning and understanding code semantics to facilitate vulnerability discovery. We also identify the challenges in this new field and share our views of potential research directions. |
|---|---|
| AbstractList | The constantly increasing number of disclosed security vulnerabilities have become an important concern in the software industry and in the field of cybersecurity, suggesting that the current approaches for vulnerability detection demand further improvement. The booming of the open-source software community has made vast amounts of software code available, which allows machine learning and data mining techniques to exploit abundant patterns within software code. Particularly, the recent breakthrough application of deep learning to speech recognition and machine translation has demonstrated the great potential of neural models' capability of understanding natural languages. This has motivated researchers in the software engineering and cybersecurity communities to apply deep learning for learning and understanding vulnerable code patterns and semantics indicative of the characteristics of vulnerable code. In this survey, we review the current literature adopting deep-learning-/neural-network-based approaches for detecting software vulnerabilities, aiming at investigating how the state-of-the-art research leverages neural techniques for learning and understanding code semantics to facilitate vulnerability discovery. We also identify the challenges in this new field and share our views of potential research directions. |
| Author | Wen, Sheng Han, Qing-Long Lin, Guanjun Zhang, Jun Xiang, Yang |
| Author_xml | – sequence: 1 givenname: Guanjun orcidid: 0000-0003-3280-1307 surname: Lin fullname: Lin, Guanjun email: daniellin1986d@gmail.com organization: School of Information Engineering, Sanming University, Fujian, Sanming, China – sequence: 2 givenname: Sheng orcidid: 0000-0003-0655-666X surname: Wen fullname: Wen, Sheng email: swen@swin.edu.au organization: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia – sequence: 3 givenname: Qing-Long orcidid: 0000-0002-7207-0716 surname: Han fullname: Han, Qing-Long email: qhan@swin.edu.au organization: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia – sequence: 4 givenname: Jun orcidid: 0000-0002-2189-7801 surname: Zhang fullname: Zhang, Jun email: junzhang@swin.edu.au organization: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia – sequence: 5 givenname: Yang orcidid: 0000-0001-5252-0831 surname: Xiang fullname: Xiang, Yang email: yxiang@swin.edu.au organization: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia |
| BookMark | eNp9kM9LwzAYhoNMcJv-A3opeO5M0qRNvI05fyFOnPMa0vSLZNZ2pqlj_72dGx48eHr54H2-F54B6lV1BQidEjwiBMuL-6fn2WREMcUjKmVCZXKA-oRzEVPK0x7qY0xELCmRR2jQNEuMccLTpI-m89qGtfYQvbZlBV7nrnRhE11BABNcXUWLxlVv3Q2r6BFar8suwrr2781lNI7mrf-CzTE6tLps4GSfQ7S4nr5MbuOH2c3dZPwQGyp5iHPghaA54ywX0nBjLUmNzHBW0FQA0zJlhc6FzvO0IIXR3NpMGpqCLYyEDCdDdL77u_L1ZwtNUMu69VU3qShjGeeMsLRr0V3L-LppPFi18u5D-40iWG11qR9daqtL7XV1kPgDGRf01kDw2pX_o2c71AHA75YkWFCRJN9ktnr8 |
| CODEN | IEEPAD |
| CitedBy_id | crossref_primary_10_1002_int_23088 crossref_primary_10_1016_j_jisa_2023_103484 crossref_primary_10_1007_s10586_025_05506_7 crossref_primary_10_1016_j_jisa_2023_103483 crossref_primary_10_1109_TCSS_2022_3227270 crossref_primary_10_1007_s00500_022_07775_5 crossref_primary_10_1007_s10664_023_10405_9 crossref_primary_10_1109_TNSM_2023_3238402 crossref_primary_10_1145_3721977 crossref_primary_10_3390_app13095290 crossref_primary_10_1016_j_cose_2021_102496 crossref_primary_10_1145_3417978 crossref_primary_10_1016_j_jnca_2021_103009 crossref_primary_10_1145_3699711 crossref_primary_10_1007_s10664_022_10244_0 crossref_primary_10_1109_JIOT_2021_3126461 crossref_primary_10_1016_j_adhoc_2023_103247 crossref_primary_10_1016_j_cose_2024_104151 crossref_primary_10_1109_JAS_2022_106058 crossref_primary_10_1155_2021_6220166 crossref_primary_10_1016_j_infsof_2023_107274 crossref_primary_10_1109_TSE_2022_3171202 crossref_primary_10_1145_3664602 crossref_primary_10_1016_j_eswa_2023_120073 crossref_primary_10_1080_09540091_2024_2447373 crossref_primary_10_1109_TDSC_2020_3029845 crossref_primary_10_1016_j_cose_2024_103992 crossref_primary_10_1145_3708522 crossref_primary_10_32604_cmc_2023_040253 crossref_primary_10_1038_s41598_024_63021_y crossref_primary_10_1007_s10515_024_00413_4 crossref_primary_10_1109_TIFS_2020_3044773 crossref_primary_10_1016_j_sysarc_2021_102240 crossref_primary_10_3390_sym16101381 crossref_primary_10_1016_j_isatra_2023_01_021 crossref_primary_10_1109_ACCESS_2020_3034324 crossref_primary_10_1007_s10489_022_03350_5 crossref_primary_10_1016_j_csi_2023_103825 crossref_primary_10_1109_TII_2025_3534441 crossref_primary_10_1109_JAS_2024_124971 crossref_primary_10_1109_TDSC_2022_3143551 crossref_primary_10_1109_TII_2020_3036166 crossref_primary_10_1109_ACCESS_2022_3191115 crossref_primary_10_32604_cmc_2024_050019 crossref_primary_10_1177_18724981251360514 crossref_primary_10_1109_JAS_2021_1004261 crossref_primary_10_1109_JIOT_2022_3152364 crossref_primary_10_1007_s10207_025_00989_2 crossref_primary_10_1016_j_infsof_2025_107853 crossref_primary_10_1016_j_infsof_2024_107453 crossref_primary_10_3233_IDT_230241 crossref_primary_10_1016_j_asoc_2022_109756 crossref_primary_10_1016_j_procs_2022_04_036 crossref_primary_10_1109_JAS_2022_105860 crossref_primary_10_1016_j_infsof_2023_107371 crossref_primary_10_1109_TII_2022_3223234 crossref_primary_10_1109_JSEN_2023_3301517 crossref_primary_10_1145_3512345 crossref_primary_10_3390_app15126524 crossref_primary_10_1016_j_scico_2022_102810 crossref_primary_10_1109_TSE_2024_3427815 crossref_primary_10_1109_JIOT_2024_3384437 crossref_primary_10_1016_j_ins_2022_04_018 crossref_primary_10_32604_cmc_2023_038878 crossref_primary_10_1002_int_23074 crossref_primary_10_1109_JETCAS_2024_3491169 crossref_primary_10_1145_3750042 crossref_primary_10_1016_j_cose_2024_103787 crossref_primary_10_1016_j_comcom_2021_07_005 crossref_primary_10_1109_ACCESS_2024_3378533 crossref_primary_10_1145_3558001 crossref_primary_10_1016_j_isatra_2023_06_030 crossref_primary_10_1109_JIOT_2024_3460349 crossref_primary_10_1016_j_isatra_2023_06_029 crossref_primary_10_1109_JBHI_2021_3139591 crossref_primary_10_1109_JSYST_2021_3072154 crossref_primary_10_1002_int_22867 crossref_primary_10_1007_s10207_024_00901_4 crossref_primary_10_1002_int_22868 crossref_primary_10_1109_TSE_2024_3423712 crossref_primary_10_1002_int_22884 crossref_primary_10_1016_j_future_2024_04_006 crossref_primary_10_3390_s24206601 crossref_primary_10_1002_smr_2422 crossref_primary_10_1109_JIOT_2022_3142823 crossref_primary_10_1016_j_cose_2023_103138 crossref_primary_10_1109_JIOT_2021_3062626 crossref_primary_10_1016_j_jss_2024_112234 crossref_primary_10_1016_j_jisa_2024_103718 crossref_primary_10_1007_s10586_024_04472_w crossref_primary_10_1109_ACCESS_2020_3034766 crossref_primary_10_1016_j_infsof_2023_107246 crossref_primary_10_1145_3744709 crossref_primary_10_1080_09540091_2022_2139353 crossref_primary_10_1145_3640333 crossref_primary_10_1002_int_23022 crossref_primary_10_1007_s10515_025_00532_6 crossref_primary_10_1016_j_jisa_2023_103620 crossref_primary_10_1007_s11042_024_19682_y crossref_primary_10_1016_j_cosrev_2022_100500 crossref_primary_10_1007_s10664_021_10029_x crossref_primary_10_1007_s00521_024_10797_9 crossref_primary_10_3390_a14110335 crossref_primary_10_1007_s10207_025_00983_8 crossref_primary_10_1007_s10515_024_00464_7 crossref_primary_10_1145_3735554 crossref_primary_10_1016_j_infsof_2023_107219 crossref_primary_10_1109_ACCESS_2024_3396410 crossref_primary_10_1016_j_hcc_2024_100268 crossref_primary_10_1007_s10515_024_00440_1 crossref_primary_10_3390_app12147337 crossref_primary_10_1109_TDSC_2022_3153325 crossref_primary_10_1016_j_jisa_2023_103511 crossref_primary_10_3390_electronics12112495 crossref_primary_10_3390_electronics14050975 crossref_primary_10_1016_j_simpat_2021_102391 crossref_primary_10_1145_3698773 crossref_primary_10_32604_cmc_2023_045522 crossref_primary_10_1016_j_eswa_2023_121865 crossref_primary_10_1016_j_jss_2023_111623 crossref_primary_10_1186_s40537_023_00727_2 crossref_primary_10_1016_j_procs_2023_08_142 crossref_primary_10_1109_TII_2024_3485707 crossref_primary_10_1016_j_jisa_2023_103521 crossref_primary_10_1016_j_infsof_2023_107304 crossref_primary_10_1016_j_jisa_2023_103524 crossref_primary_10_1016_j_ijcip_2022_100540 crossref_primary_10_1016_j_jisa_2024_103925 crossref_primary_10_1145_3728884 crossref_primary_10_1016_j_compeleceng_2022_108308 crossref_primary_10_1109_JIOT_2025_3541090 crossref_primary_10_1145_3694782 crossref_primary_10_1016_j_infsof_2025_107797 crossref_primary_10_1007_s11517_023_02912_0 crossref_primary_10_1016_j_jnca_2022_103370 crossref_primary_10_1109_JIOT_2022_3147186 crossref_primary_10_1016_j_inffus_2024_102748 crossref_primary_10_1109_ACCESS_2021_3072635 crossref_primary_10_1002_int_23013 crossref_primary_10_3390_app14135750 crossref_primary_10_1007_s11235_023_01085_3 crossref_primary_10_1007_s10515_024_00418_z crossref_primary_10_1016_j_jss_2025_112570 crossref_primary_10_1016_j_cose_2022_102823 crossref_primary_10_3233_JIFS_230009 crossref_primary_10_1016_j_cose_2022_102813 crossref_primary_10_1016_j_jnca_2023_103761 crossref_primary_10_1145_3735968 crossref_primary_10_1016_j_jss_2024_112038 crossref_primary_10_1016_j_comcom_2021_08_002 crossref_primary_10_1016_j_scico_2025_103357 crossref_primary_10_1007_s10489_021_03128_1 crossref_primary_10_1145_3744555 crossref_primary_10_1155_2021_8493795 crossref_primary_10_1002_cpe_8292 crossref_primary_10_1007_s10207_024_00848_6 crossref_primary_10_1109_ACCESS_2023_3296789 crossref_primary_10_3390_electronics12092046 crossref_primary_10_1016_j_eswa_2023_123041 crossref_primary_10_3390_aerospace11060488 crossref_primary_10_1016_j_infsof_2022_106822 crossref_primary_10_1016_j_jisa_2023_103555 crossref_primary_10_1109_JIOT_2022_3196269 crossref_primary_10_1155_2022_7424094 crossref_primary_10_3390_electronics12102258 crossref_primary_10_1002_int_22819 crossref_primary_10_1007_s00521_022_08046_y crossref_primary_10_1007_s11227_025_07198_7 crossref_primary_10_1016_j_jss_2024_112014 crossref_primary_10_1016_j_isatra_2023_03_034 crossref_primary_10_1155_2023_9970205 crossref_primary_10_3390_s21072456 crossref_primary_10_2478_pomr_2024_0014 crossref_primary_10_1016_j_isatra_2023_04_025 crossref_primary_10_1109_TCSS_2021_3135586 crossref_primary_10_1016_j_isatra_2023_04_022 crossref_primary_10_1016_j_isatra_2023_04_021 crossref_primary_10_1016_j_isatra_2023_04_020 crossref_primary_10_1007_s13042_024_02153_z crossref_primary_10_1080_09540091_2021_2013443 crossref_primary_10_1016_j_knosys_2025_114146 crossref_primary_10_1016_j_isatra_2023_04_019 crossref_primary_10_1016_j_isatra_2023_04_018 crossref_primary_10_3390_math10234482 crossref_primary_10_1016_j_isatra_2022_12_020 crossref_primary_10_1016_j_jss_2023_111679 crossref_primary_10_1109_TSE_2022_3147265 crossref_primary_10_1016_j_infsof_2024_107458 crossref_primary_10_1109_TIFS_2023_3326985 crossref_primary_10_1145_3664806 crossref_primary_10_1145_3610228 crossref_primary_10_1145_3662184 crossref_primary_10_21822_2073_6185_2025_52_2_98_106 crossref_primary_10_1016_j_isatra_2023_04_010 crossref_primary_10_1007_s10664_022_10276_6 crossref_primary_10_1093_comjnl_bxae041 crossref_primary_10_1002_nem_2198 crossref_primary_10_1145_3705616 crossref_primary_10_3390_app13020825 crossref_primary_10_1016_j_eswa_2025_128431 crossref_primary_10_1016_j_heliyon_2023_e17599 crossref_primary_10_1109_TSUSC_2023_3248965 crossref_primary_10_1002_cpe_70070 crossref_primary_10_1109_TCYB_2022_3164625 crossref_primary_10_1016_j_jisa_2023_103467 crossref_primary_10_1155_2020_8859489 crossref_primary_10_1016_j_cose_2022_103006 crossref_primary_10_1016_j_cose_2021_102308 crossref_primary_10_1109_JAS_2024_125034 crossref_primary_10_3390_s24175465 crossref_primary_10_3390_make6020050 crossref_primary_10_1080_1206212X_2025_2452882 crossref_primary_10_1155_2022_6039690 crossref_primary_10_1109_TIFS_2024_3369479 crossref_primary_10_1007_s00521_024_10892_x crossref_primary_10_1145_3529757 crossref_primary_10_1016_j_cose_2022_103017 crossref_primary_10_3390_e25060888 crossref_primary_10_1007_s10664_024_10519_8 crossref_primary_10_1145_3436751 crossref_primary_10_1007_s41019_024_00260_z crossref_primary_10_1093_comjnl_bxac080 crossref_primary_10_1007_s11227_023_05282_4 |
| Cites_doi | 10.1145/2420950.2421003 10.1109/TDSC.2019.2942930 10.1109/TII.2018.2821768 10.1109/SP.2018.00003 10.3115/v1/D14-1181 10.1109/ICASSP.2013.6639345 10.1162/neco.1997.9.8.1735 10.1007/s11277-017-5069-3 10.1155/2019/6230953 10.2307/3001968 10.14722/ndss.2018.23158 10.1145/3196398.3196448 10.1109/IWCMC.2019.8766500 10.18653/v1/N18-1202 10.1109/ICSE.2012.6227135 10.1109/MINES.2012.202 10.1145/1218063.1217938 10.1109/SP.2017.62 10.1109/TC.2013.2295802 10.1145/3092566 10.1007/s00165-014-0326-7 10.1145/2810103.2813604 10.1007/s10994-006-6226-1 10.1145/1653662.1653717 10.1109/SP.2012.13 10.1109/COMST.2016.2615098 10.6028/NIST.TN.1995 10.1145/2939672.2939778 10.1007/s10664-011-9190-8 10.1109/TIFS.2019.2932228 10.1145/2884781.2884804 10.1109/MSR.2015.38 10.1007/BF02551274 10.1145/1062455.1062514 10.1145/2187671.2187673 10.1016/j.sysarc.2010.06.003 10.1145/2664243.2664269 10.1145/3133956.3138840 10.1145/3212695 10.1109/TDSC.2014.2373377 10.1016/j.future.2018.04.016 10.1109/ICSE.2013.6606584 10.1109/TPDS.2012.98 10.1145/2746194.2746198 10.1109/TSE.1976.233837 10.3115/v1/D14-1179 10.1109/ICMLA.2018.00120 10.1145/375360.375365 10.1145/1414004.1414065 10.1109/TSE.2014.2340398 10.1109/SP.2014.44 10.1109/SP.2015.54 10.1109/TDSC.2019.2954088 10.3115/v1/P14-2105 10.1109/TCYB.2019.2940940 10.1016/j.asoc.2014.11.023 10.1109/TSE.2010.81 10.6028/jres.123.005 10.1109/COMST.2018.2885561 10.3115/v1/P15-1150 10.1109/CompComm.2017.8322752 10.1145/2857705.2857720 10.1145/1095430.1081755 10.1016/j.infsof.2013.04.002 10.6028/NIST.SP.500-297 10.1145/1287624.1287630 10.1145/2508859.2516665 10.1109/DSC.2016.33 10.1038/nature14539 10.1145/1315245.1315311 10.1109/TKDE.2017.2697856 10.1145/2351676.2351733 10.1145/502059.502041 10.1109/CGO.2004.1281665 10.1109/JPROC.2017.2761740 10.1109/COMST.2018.2800740 10.1016/j.cose.2017.11.013 10.24963/ijcai.2017/214 10.1109/ICMLA.2015.99 10.1007/978-3-319-25159-2_49 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 |
| DBID | 97E RIA RIE AAYXX CITATION 7SP 8FD L7M |
| DOI | 10.1109/JPROC.2020.2993293 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Technology Research Database Advanced Technologies Database with Aerospace |
| DatabaseTitle | CrossRef Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts |
| DatabaseTitleList | Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1558-2256 |
| EndPage | 1848 |
| ExternalDocumentID | 10_1109_JPROC_2020_2993293 9108283 |
| Genre | orig-research |
| GroupedDBID | -DZ -~X .DC 0R~ 123 1OL 29P 3EH 4.4 6IK 85S 97E 9M8 AAJGR AAWTH ABAZT ABFSI ABJNI ABQJQ ABVLG ACBEA ACGFS AENEX AETEA AETIX AFOGA AGNAY AGQYO AGSQL AHBIQ AIBXA ALLEH ALMA_UNASSIGNED_HOLDINGS AZLTO BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD FA8 HZ~ H~9 IAAWW IBMZZ ICLAB IDIHD IFIPE IFJZH IPLJI JAVBF LAI M43 MVM O9- OCL RIA RIE RIU RNS TAE TN5 TWZ UDY UHB UKR UQL VOH WHG XJT XOL YNT ZCA ZXP ZY4 ~02 AAYXX CITATION 7SP 8FD L7M RIG |
| ID | FETCH-LOGICAL-c295t-be5d82b454b89c5cff16c9707d268e4a964dab8abb6d1dca5ff79c26efdc9e703 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 302 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000574742200006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0018-9219 |
| IngestDate | Fri Jul 25 07:53:19 EDT 2025 Tue Nov 18 21:49:15 EST 2025 Sat Nov 29 06:01:43 EST 2025 Wed Aug 27 02:31:55 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 10 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c295t-be5d82b454b89c5cff16c9707d268e4a964dab8abb6d1dca5ff79c26efdc9e703 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-7207-0716 0000-0002-2189-7801 0000-0001-5252-0831 0000-0003-0655-666X 0000-0003-3280-1307 |
| PQID | 2447554146 |
| PQPubID | 85453 |
| PageCount | 24 |
| ParticipantIDs | ieee_primary_9108283 proquest_journals_2447554146 crossref_primary_10_1109_JPROC_2020_2993293 crossref_citationtrail_10_1109_JPROC_2020_2993293 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-10-01 |
| PublicationDateYYYYMMDD | 2020-10-01 |
| PublicationDate_xml | – month: 10 year: 2020 text: 2020-10-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Proceedings of the IEEE |
| PublicationTitleAbbrev | JPROC |
| PublicationYear | 2020 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref57 ref56 ref59 gal (ref31) 2016 le (ref51) 2014 alon (ref8) 2019; 3 black (ref10) 2006; 500 ref50 ref46 ref45 ramos (ref83) 2015 ref42 ref41 ref44 ref43 bahdanau (ref9) 2014 cao (ref14) 2016 (ref1) 2017 vaswani (ref107) 2017 ref7 ref6 lecun (ref53) 2015; 521 ref101 ref40 ref35 ref30 ref33 ref32 harer (ref38) 2018 gupta (ref36) 2017 chollet (ref19) 2015 ref39 wheeler (ref113) 2016 sukhbaatar (ref100) 2015 (ref4) 2016 ref23 sukhbaatar (ref99) 2015 harer (ref37) 2018 ref25 ref20 kipf (ref48) 2016 ref21 ref28 dam (ref24) 2017 ref27 ref29 weston (ref112) 2014 csáji (ref22) 2001; 24 ramsundar (ref84) 2018 (ref3) 2014 thanassis (ref106) 2011 parr (ref77) 2013 ref12 ref128 ref15 ref126 ref127 ref96 socher (ref97) 2011 (ref5) 2017 ref124 ref11 ref125 ref98 wu (ref118) 2019 ref17 ref18 li (ref55) 2018 cadar (ref13) 2008; 8 ref93 ref92 ref95 ref94 ref91 ref90 lin (ref58) 2019 ref86 ref85 ref88 graves (ref34) 2014 chess (ref16) 2019 (ref2) 2009 ref82 ref81 le (ref52) 2018 devlin (ref26) 2018 ref80 ref79 ref108 ref78 ref109 mikolov (ref67) 2013 sestili (ref89) 2018 ref75 ref104 ref105 ref76 sutton (ref103) 2007 newsome (ref74) 2005 kingma (ref47) 2013 krizhevsky (ref49) 2012 ref71 ref111 ref70 ref73 ref72 ref110 lee (ref54) 2017 ref119 ref117 ref69 ref64 ref115 ref63 ref116 sabottke (ref87) 2015 ref66 ref65 ref114 mikolov (ref68) 2013 sutskever (ref102) 2014 ref60 ref122 ref123 ref62 ref120 ref61 ref121 |
| References_xml | – ident: ref120 doi: 10.1145/2420950.2421003 – start-page: 3111 year: 2013 ident: ref68 article-title: Distributed representations of words and phrases and their compositionality publication-title: Proc Adv Neural Inf Process Syst – ident: ref127 doi: 10.1109/TDSC.2019.2942930 – year: 2019 ident: ref16 publication-title: Rough Auditing Tool for Security – year: 2014 ident: ref112 article-title: Memory networks publication-title: arXiv 1410 3916 – year: 2014 ident: ref3 publication-title: Shellshock All You Need to Know About the Bash Bug Vulnerability – ident: ref61 doi: 10.1109/TII.2018.2821768 – ident: ref109 doi: 10.1109/SP.2018.00003 – ident: ref46 doi: 10.3115/v1/D14-1181 – start-page: 1345 year: 2017 ident: ref36 article-title: Deepfix: Fixing common C language errors by deep learning publication-title: Proc 31st AAAI Conf Artif Intell – ident: ref25 doi: 10.1109/ICASSP.2013.6639345 – year: 2009 ident: ref2 publication-title: Securely taking on new executable software of uncertain provenance (STONESOUP) – ident: ref41 doi: 10.1162/neco.1997.9.8.1735 – year: 2018 ident: ref55 article-title: SySeVR: A framework for using deep learning to detect software vulnerabilities publication-title: arXiv 1807 06756 – ident: ref27 doi: 10.1007/s11277-017-5069-3 – ident: ref30 doi: 10.1155/2019/6230953 – ident: ref115 doi: 10.2307/3001968 – ident: ref56 doi: 10.14722/ndss.2018.23158 – year: 2013 ident: ref47 article-title: Auto-encoding variational Bayes publication-title: arXiv 1312 6114 – ident: ref28 doi: 10.1145/3196398.3196448 – year: 2013 ident: ref67 article-title: Efficient estimation of word representations in vector space publication-title: arXiv 1301 3781 [cs] – ident: ref98 doi: 10.1109/IWCMC.2019.8766500 – ident: ref80 doi: 10.18653/v1/N18-1202 – ident: ref40 doi: 10.1109/ICSE.2012.6227135 – ident: ref62 doi: 10.1109/MINES.2012.202 – ident: ref82 doi: 10.1145/1218063.1217938 – year: 2018 ident: ref89 article-title: Towards security defect prediction with AI publication-title: arXiv 1808 09897 – ident: ref39 doi: 10.1109/ICSE.2012.6227135 – ident: ref45 doi: 10.1109/SP.2017.62 – start-page: 1041 year: 2015 ident: ref87 article-title: Vulnerability disclosure in the age of social media: Exploiting Twitter for predicting real-world exploits publication-title: Proc Usenix Secur Symp – ident: ref111 doi: 10.1109/TC.2013.2295802 – year: 2016 ident: ref48 article-title: Semi-supervised classification with graph convolutional networks publication-title: arXiv 1609 02907 – ident: ref33 doi: 10.1145/3092566 – start-page: 2440 year: 2015 ident: ref100 article-title: End-to-end memory networks publication-title: Proc Adv Neural Inf Process Syst – ident: ref128 doi: 10.1007/s00165-014-0326-7 – ident: ref79 doi: 10.1145/2810103.2813604 – year: 2013 ident: ref77 publication-title: The Definitive Antlr 4 Reference – ident: ref32 doi: 10.1007/s10994-006-6226-1 – year: 2011 ident: ref106 article-title: AEG: Automatic exploit generation publication-title: Proc Symp Netw Distrib Syst Security – start-page: 1050 year: 2016 ident: ref31 article-title: Dropout as a Bayesian approximation: Representing model uncertainty in deep learning publication-title: Proc Int Conf Mach Learn – ident: ref66 doi: 10.1145/1653662.1653717 – ident: ref42 doi: 10.1109/SP.2012.13 – ident: ref43 doi: 10.1109/COMST.2016.2615098 – year: 2017 ident: ref5 publication-title: Equifax Had Patch 2 Months Before Hack and Didn't Install It Security Group Says – year: 2016 ident: ref113 publication-title: Flawfinder – year: 2017 ident: ref24 article-title: Automatic feature learning for vulnerability prediction publication-title: arXiv 1708 02368 – year: 2014 ident: ref9 article-title: Neural machine translation by jointly learning to align and translate publication-title: arXiv 1409 0473 – start-page: 1188 year: 2014 ident: ref51 article-title: Distributed representations of sentences and documents publication-title: Proc Int Conf Mach Learn – year: 2018 ident: ref38 article-title: Automated software vulnerability detection with machine learning publication-title: arXiv 1803 04497 – ident: ref12 doi: 10.6028/NIST.TN.1995 – ident: ref85 doi: 10.1145/2939672.2939778 – ident: ref96 doi: 10.1007/s10664-011-9190-8 – year: 2015 ident: ref99 article-title: Weakly supervised memory networks publication-title: arXiv 1503 08895 – ident: ref15 doi: 10.1109/TIFS.2019.2932228 – ident: ref110 doi: 10.1145/2884781.2884804 – ident: ref114 doi: 10.1109/MSR.2015.38 – start-page: 5998 year: 2017 ident: ref107 article-title: Attention is all you need publication-title: Proc Adv Neural Inf Process Syst – ident: ref23 doi: 10.1007/BF02551274 – ident: ref70 doi: 10.1145/1062455.1062514 – ident: ref90 doi: 10.1145/2187671.2187673 – start-page: 1097 year: 2012 ident: ref49 article-title: Imagenet classification with deep convolutional neural networks publication-title: Proc Adv Neural Inf Process Syst – ident: ref20 doi: 10.1016/j.sysarc.2010.06.003 – start-page: 219 year: 2019 ident: ref58 article-title: Deep learning-based vulnerable function detection: A benchmark publication-title: Proc Int Conf Inf Commun Secur – start-page: 3 year: 2005 ident: ref74 article-title: Dynamic taint analysis for automatic detection, analysis, and signature generation of exploits on commodity software publication-title: Proc NDSS – volume: 8 start-page: 209 year: 2008 ident: ref13 article-title: KLEE: Unassisted and automatic generation of high-coverage tests for complex systems programs publication-title: Proc OSDI – ident: ref81 doi: 10.1145/2664243.2664269 – year: 2017 ident: ref1 publication-title: The Heartbleed Vulnerability – volume: 3 start-page: 40 year: 2019 ident: ref8 publication-title: Proc ACM Program Lang POPL – year: 2014 ident: ref34 article-title: Neural turing machines publication-title: arXiv 1410 5401 – ident: ref60 doi: 10.1145/3133956.3138840 – year: 2018 ident: ref26 article-title: BERT: Pre-training of deep bidirectional transformers for language understanding publication-title: arXiv 1810 04805 – year: 2017 ident: ref54 article-title: Learning binary code with deep learning to detect software weakness publication-title: Proc KSII 9th Int Conf Internet Symp – year: 2015 ident: ref19 publication-title: Keras – ident: ref7 doi: 10.1145/3212695 – ident: ref91 doi: 10.1109/TDSC.2014.2373377 – ident: ref126 doi: 10.1016/j.future.2018.04.016 – ident: ref71 doi: 10.1109/ICSE.2013.6606584 – ident: ref124 doi: 10.1109/TPDS.2012.98 – ident: ref69 doi: 10.1145/2746194.2746198 – ident: ref65 doi: 10.1109/TSE.1976.233837 – ident: ref17 doi: 10.3115/v1/D14-1179 – ident: ref86 doi: 10.1109/ICMLA.2018.00120 – ident: ref72 doi: 10.1145/375360.375365 – year: 2016 ident: ref4 publication-title: Rough Auditing Tool for Security (RATS) – ident: ref95 doi: 10.1145/1414004.1414065 – start-page: 49 year: 2015 ident: ref83 article-title: Under-constrained symbolic execution: Correctness checking for real code publication-title: Proc Usenix Secur Symp – ident: ref88 doi: 10.1109/TSE.2014.2340398 – ident: ref108 doi: 10.1109/SP.2018.00003 – start-page: 1145 year: 2016 ident: ref14 article-title: Deep neural networks for learning graph representations publication-title: Proc 13th AAAI Conf Artif Intell – ident: ref119 doi: 10.1109/SP.2014.44 – ident: ref121 doi: 10.1109/SP.2015.54 – ident: ref59 doi: 10.1109/TDSC.2019.2954088 – ident: ref123 doi: 10.3115/v1/P14-2105 – ident: ref21 doi: 10.1109/TCYB.2019.2940940 – ident: ref64 doi: 10.1016/j.asoc.2014.11.023 – start-page: 129 year: 2011 ident: ref97 article-title: Parsing natural scenes and natural language with recursive neural networks publication-title: Proc 28th Int Conf Mach Learn (ICML) – ident: ref94 doi: 10.1109/TSE.2010.81 – ident: ref11 doi: 10.6028/jres.123.005 – year: 2007 ident: ref103 publication-title: Fuzzing Brute Force Vulnerability Discovery – year: 2019 ident: ref118 article-title: A comprehensive survey on graph neural networks publication-title: arXiv 1901 00596 – volume: 500 start-page: 2 year: 2006 ident: ref10 article-title: Samate's contribution to information assurance publication-title: NIST Special Publication – ident: ref101 doi: 10.1109/COMST.2018.2885561 – ident: ref105 doi: 10.3115/v1/P15-1150 – ident: ref116 doi: 10.1109/CompComm.2017.8322752 – ident: ref35 doi: 10.1145/2857705.2857720 – ident: ref57 doi: 10.1145/1095430.1081755 – ident: ref93 doi: 10.1016/j.infsof.2013.04.002 – ident: ref75 doi: 10.6028/NIST.SP.500-297 – ident: ref6 doi: 10.1145/1287624.1287630 – ident: ref122 doi: 10.1145/2508859.2516665 – ident: ref44 doi: 10.1109/DSC.2016.33 – volume: 521 start-page: 436 year: 2015 ident: ref53 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – ident: ref73 doi: 10.1145/1315245.1315311 – year: 2018 ident: ref52 article-title: Maximal divergence sequential autoencoder for binary software vulnerability detection publication-title: Proc Int Conf Learn Represent (ICLR)ICLR – ident: ref125 doi: 10.1109/TKDE.2017.2697856 – ident: ref92 doi: 10.1145/2351676.2351733 – ident: ref29 doi: 10.1145/502059.502041 – start-page: 7933 year: 2018 ident: ref37 article-title: Learning to repair software vulnerabilities with generative adversarial networks publication-title: Proc Adv Neural Inf Process Syst – ident: ref50 doi: 10.1109/CGO.2004.1281665 – ident: ref104 doi: 10.1109/JPROC.2017.2761740 – volume: 24 start-page: 7 year: 2001 ident: ref22 article-title: Approximation with artificial neural networks – ident: ref63 doi: 10.1109/COMST.2018.2800740 – ident: ref117 doi: 10.1016/j.cose.2017.11.013 – year: 2018 ident: ref84 publication-title: TensorFlow for Deep Learning From Linear Regression to Reinforcement Learning – ident: ref18 doi: 10.24963/ijcai.2017/214 – ident: ref76 doi: 10.1109/ICMLA.2015.99 – start-page: 3104 year: 2014 ident: ref102 article-title: Sequence to sequence learning with neural networks publication-title: Proc Adv Neural Inf Process Syst – ident: ref78 doi: 10.1007/978-3-319-25159-2_49 |
| SSID | ssj0003563 |
| Score | 2.6950276 |
| Snippet | The constantly increasing number of disclosed security vulnerabilities have become an important concern in the software industry and in the field of... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1825 |
| SubjectTerms | Artificial neural networks Computer bugs Computer security Cybersecurity Data mining Deep learning deep neural network (DNN) Feature extraction Literature reviews Machine learning machine learning (ML) Machine translation Neural networks Open source software representation learning Semantics Software engineering Software reliability software vulnerability Source code Speech recognition |
| Title | Software Vulnerability Detection Using Deep Neural Networks: A Survey |
| URI | https://ieeexplore.ieee.org/document/9108283 https://www.proquest.com/docview/2447554146 |
| Volume | 108 |
| WOSCitedRecordID | wos000574742200006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-2256 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0003563 issn: 0018-9219 databaseCode: RIE dateStart: 19630101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED_m8EEf_Jri_KIPvmm1SZO08U2mIiJTnI69lSS9gjA22Yey_94k7YaiCL61kITyu95dr_nldwDHCbVZQxIWUmVrVcZjZeOgkiEnuSm4UTr3lP_ufdJup72efKzB6eIsDCJ68hmeuUu_l58PzdT9Kju3qc0WCPESLCWJKM9qLaJuzKuuacQ6sHXD-QGZSJ7fPT49tGwpSKMzG3xjKuNvSch3VfkRin1-uVn_35NtwFr1HRlclobfhBoOtmD1i7pgA647NsR-qBEG3WnfiUt7HuwsuMKJ518NAs8XsPf4FjiRDrteu2SFjy-Cy6AzHb3jbBtebq6fW7dh1TUhNFTySaiR5ynVjDOdSsNNURBhZBIlORUpMiUFy5VOldYitwZRvCgSaajAIjcSbQDYgfpgOMBdCBRDYx1acyI0SyVRBSPIU1IYRiMUURPIHMbMVJLirrNFP_OlRSQzD33moM8q6JtwspjzVgpq_Dm64cBejKxwbsLB3FpZ5XPjjDrtQtfVXOz9PmsfVtzaJRXvAOqT0RQPYdm8T17HoyP_On0C6WbHJg |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB50FdSDb3F11R68ad0mTdrG2-IDH-sqvvBWknQKwrK77EPx35uk3UVRBG8tJG35pjPTSb98A7AfU5M1BGE-laZWZTyUJg5K4XOS6ZxrqTJH-X9uxq1W8vIi7qbgcLIXBhEd-QyP7KH7l5919cguldVNajMFQjgNM5wxGhS7tSZxN-Rl3zRiXNg44niLTCDqV3f3tyemGKTBkQm_IRXhtzTk-qr8CMYuw5wv_e_ZlmGx_JL0GoXpV2AKO6uw8EVfcA3OHkyQfZd99J5HbSsv7ZiwH94pDh0Dq-M5xoA5x55nZTrM9VoFL3xw7DW8h1H_DT_W4en87PHkwi_7JviaCj70FfIsoYpxphKhuc5zEmkRB3FGowSZFBHLpEqkUlFmTCJ5nsdC0wjzTAs0IWADKp1uBzfBkwy1cWnFSaRYIojMGUGekFwb_DEKqkDGMKa6FBW3vS3aqSsuApE66FMLfVpCX4WDyZxeIanx5-g1C_ZkZIlzFWpja6Wl1w1SatULbV_zaOv3WXswd_F400ybl63rbZi39ymIeTWoDPsj3IFZ_TZ8HfR33av1CXBAym0 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Software+Vulnerability+Detection+Using+Deep+Neural+Networks%3A+A+Survey&rft.jtitle=Proceedings+of+the+IEEE&rft.au=Lin%2C+Guanjun&rft.au=Sheng+Wen&rft.au=Qing-Long%2C+Han&rft.au=Zhang%2C+Jun&rft.date=2020-10-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=0018-9219&rft.eissn=1558-2256&rft.volume=108&rft.issue=10&rft.spage=1825&rft_id=info:doi/10.1109%2FJPROC.2020.2993293&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9219&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9219&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9219&client=summon |