{"id":"https://openalex.org/W4403721651","doi":"https://doi.org/10.1109/access.2024.3486180","title":"File Fragment Type Classification Using Light-Weight Convolutional Neural Networks","display_name":"File Fragment Type Classification Using Light-Weight Convolutional Neural Networks","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4403721651","doi":"https://doi.org/10.1109/access.2024.3486180"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3486180","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3486180","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2024.3486180","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037740657","display_name":"Muhamad Felemban","orcid":"https://orcid.org/0000-0001-6414-3391"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":true,"raw_author_name":"Muhamad Felemban","raw_affiliation_strings":["Computer Engineering Department, KFUPM, Dhahran, Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0001-6414-3391","affiliations":[{"raw_affiliation_string":"Computer Engineering Department, KFUPM, Dhahran, Saudi Arabia","institution_ids":["https://openalex.org/I134085113"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007631351","display_name":"Mustafa Ghaleb","orcid":"https://orcid.org/0000-0003-2842-6532"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Mustafa Ghaleb","raw_affiliation_strings":["Computer Engineering Department, KFUPM, Dhahran, Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0003-2842-6532","affiliations":[{"raw_affiliation_string":"Computer Engineering Department, KFUPM, Dhahran, Saudi Arabia","institution_ids":["https://openalex.org/I134085113"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041467104","display_name":"Kunwar Muhammed Saaim","orcid":"https://orcid.org/0000-0002-3035-8148"},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Kunwar Saaim","raw_affiliation_strings":["Department of Computing Science, University of Alberta, Edmonton, AB, Canada"],"raw_orcid":"https://orcid.org/0000-0002-3035-8148","affiliations":[{"raw_affiliation_string":"Department of Computing Science, University of Alberta, Edmonton, AB, Canada","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013253181","display_name":"Saleh M. Al\u2010Saleh","orcid":"https://orcid.org/0000-0003-4506-8407"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Saleh AlSaleh","raw_affiliation_strings":["Computer Engineering Department, KFUPM, Dhahran, Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0003-4506-8407","affiliations":[{"raw_affiliation_string":"Computer Engineering Department, KFUPM, Dhahran, Saudi Arabia","institution_ids":["https://openalex.org/I134085113"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001203590","display_name":"Ahmad Almulhem","orcid":"https://orcid.org/0000-0003-3773-0579"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Ahmad Almulhem","raw_affiliation_strings":["Computer Engineering Department, KFUPM, Dhahran, Saudi Arabia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Engineering Department, KFUPM, Dhahran, Saudi Arabia","institution_ids":["https://openalex.org/I134085113"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5037740657"],"corresponding_institution_ids":["https://openalex.org/I134085113"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":7.3335,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.97164306,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"12","issue":null,"first_page":"157179","last_page":"157191"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12034","display_name":"Digital and Cyber Forensics","score":0.9768000245094299,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12034","display_name":"Digital and Cyber Forensics","score":0.9768000245094299,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9523000121116638,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7496882677078247},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6937341690063477},{"id":"https://openalex.org/keywords/fragment","display_name":"Fragment (logic)","score":0.6185027956962585},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49389439821243286},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36526164412498474},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24287667870521545}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7496882677078247},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6937341690063477},{"id":"https://openalex.org/C2776235265","wikidata":"https://www.wikidata.org/wiki/Q18392052","display_name":"Fragment (logic)","level":2,"score":0.6185027956962585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49389439821243286},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36526164412498474},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24287667870521545}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2024.3486180","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3486180","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:4d11a51a-b401-46cc-9990-be2c79da7203","is_oa":false,"landing_page_url":"https://avesis.kocaeli.edu.tr/publication/details/4d11a51a-b401-46cc-9990-be2c79da7203/oai","pdf_url":null,"source":{"id":"https://openalex.org/S7407055291","display_name":"Kocaeli \u00dcniversitesi - AVES\u0130S","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:doaj.org/article:79aee79ef8ba4f61b9403b83583fa4f1","is_oa":true,"landing_page_url":"https://doaj.org/article/79aee79ef8ba4f61b9403b83583fa4f1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 157179-157191 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3486180","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3486180","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1549358575","https://openalex.org/W1603441233","https://openalex.org/W1746799182","https://openalex.org/W1923835230","https://openalex.org/W1992236679","https://openalex.org/W2027166822","https://openalex.org/W2064675550","https://openalex.org/W2069143585","https://openalex.org/W2069867586","https://openalex.org/W2076342816","https://openalex.org/W2081747167","https://openalex.org/W2097117768","https://openalex.org/W2101926813","https://openalex.org/W2211686775","https://openalex.org/W2337532746","https://openalex.org/W2531409750","https://openalex.org/W2604319603","https://openalex.org/W2752782242","https://openalex.org/W2758475033","https://openalex.org/W2796263526","https://openalex.org/W2796588635","https://openalex.org/W2804103759","https://openalex.org/W2887831059","https://openalex.org/W2889034351","https://openalex.org/W2942945504","https://openalex.org/W2949676527","https://openalex.org/W2960717050","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2964081807","https://openalex.org/W2982083293","https://openalex.org/W2994179000","https://openalex.org/W3036399259","https://openalex.org/W3046419603","https://openalex.org/W3204840549","https://openalex.org/W4243094468","https://openalex.org/W4250482878","https://openalex.org/W4285204040","https://openalex.org/W4297775537","https://openalex.org/W4383501674","https://openalex.org/W4389428567","https://openalex.org/W4389524555","https://openalex.org/W4392302076","https://openalex.org/W4392902819","https://openalex.org/W4400832419","https://openalex.org/W6604766352","https://openalex.org/W6631190155","https://openalex.org/W6632978964","https://openalex.org/W6676001293","https://openalex.org/W6676179485","https://openalex.org/W6703704083","https://openalex.org/W6713710995","https://openalex.org/W6737664043","https://openalex.org/W6762718338","https://openalex.org/W6766225098","https://openalex.org/W6769906912"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W1973480752","https://openalex.org/W2805502594","https://openalex.org/W3132641048","https://openalex.org/W4403688099","https://openalex.org/W4253208712","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"In":[0],"digital":[1,236],"forensics,":[2],"file":[3,15,25,38,49,86,118,138,173],"carving":[4],"is":[5,26,30,242],"used":[6,74],"to":[7,33,44,61,75,79,96,103,147,161,178],"extract":[8],"files":[9],"without":[10,51],"relying":[11,52],"on":[12,53,124,167,196],"the":[13,24,35,46,63,149,168,189,197,221],"underlying":[14],"system":[16],"metadata.":[17],"This":[18,158],"process":[19],"can":[20],"be":[21],"challenging":[22],"if":[23],"fragmented.":[27],"Therefore,":[28],"it":[29],"important":[31],"first":[32],"identify":[34,45,62],"type":[36,47,88,120,140],"of":[37,48,100,151],"fragment.":[39],"There":[40],"exist":[41],"several":[42],"techniques":[43],"fragments":[50],"metadata,":[54],"for":[55,85,234],"example,":[56],"using":[57,200],"headers":[58],"and":[59,107,204,214],"footers":[60],"fragment":[64,87,119,139],"type.":[65],"Recently,":[66],"convolutional":[67],"neural":[68],"network":[69],"(CNN)":[70],"models":[71,78,84,122,180,231],"have":[72],"been":[73],"build":[76],"classification":[77,89,121,141,163],"achieve":[80],"this":[81],"task.":[82],"Existing":[83],"often":[90],"require":[91],"significant":[92],"computational":[93,133,156,240],"resources":[94],"due":[95],"their":[97],"large":[98],"number":[99],"parameters,":[101],"leading":[102],"slower":[104],"inference":[105],"times":[106],"higher":[108],"memory":[109],"consumption.":[110],"To":[111],"address":[112],"these":[113,229],"challenges,":[114],"we":[115],"propose":[116],"light-weight":[117,137,230],"based":[123],"separable":[125,145],"CNNs":[126],"that":[127,228],"maintain":[128],"comparable":[129,176],"accuracy":[130,177,195],"while":[131,154],"reducing":[132,155],"demands.":[134],"Our":[135,225],"proposed":[136],"model":[142,212],"leverages":[143],"depthwise":[144],"convolutions":[146],"improve":[148],"efficiency":[150,241],"feature":[152],"extraction":[153],"overhead.":[157],"approach":[159],"leads":[160],"improved":[162],"performance":[164],"by":[165],"focusing":[166],"most":[169],"relevant":[170],"features":[171],"within":[172],"fragments,":[174],"achieving":[175],"state-of-the-art":[179],"with":[181,192],"significantly":[182],"fewer":[183],"parameters.":[184],"The":[185],"evaluation":[186],"results":[187,226],"demonstrate":[188,227],"model\u2019s":[190],"effectiveness,":[191],"a":[193,208,215],"79%":[194],"FFT-75":[198],"dataset":[199],"nearly":[201],"100K":[202],"parameters":[203],"164M":[205],"FLOPs":[206],"\u2014representing":[207],"4x":[209],"reduction":[210],"in":[211,218],"size":[213],"6x":[216],"improvement":[217],"speed":[219],"over":[220],"best-performing":[222],"existing":[223],"classifier.":[224],"are":[232],"effective":[233],"real-time":[235],"forensic":[237],"applications":[238],"where":[239],"critical.":[243]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2024-10-25T00:00:00"}