{"id":"https://openalex.org/W7116891506","doi":"https://doi.org/10.1145/3727967.3756834","title":"IoT-AI Security for Dynamic Data-driven Environments","display_name":"IoT-AI Security for Dynamic Data-driven Environments","publication_year":2025,"publication_date":"2025-06-17","ids":{"openalex":"https://openalex.org/W7116891506","doi":"https://doi.org/10.1145/3727967.3756834"},"language":null,"primary_location":{"id":"doi:10.1145/3727967.3756834","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727967.3756834","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 29th International Conference on Evaluation and Assessment in Software Engineering Companion","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3727967.3756834","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121119318","display_name":"Ankita Sharma","orcid":null},"institutions":[{"id":"https://openalex.org/I74319210","display_name":"Chitkara University","ror":"https://ror.org/057d6z539","country_code":"IN","type":"education","lineage":["https://openalex.org/I74319210"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Ankita Sharma","raw_affiliation_strings":["Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India"],"raw_orcid":"https://orcid.org/0000-0002-5523-052X","affiliations":[{"raw_affiliation_string":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India","institution_ids":["https://openalex.org/I74319210"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121076162","display_name":"Shalli Rani","orcid":null},"institutions":[{"id":"https://openalex.org/I74319210","display_name":"Chitkara University","ror":"https://ror.org/057d6z539","country_code":"IN","type":"education","lineage":["https://openalex.org/I74319210"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shalli Rani","raw_affiliation_strings":["Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India"],"raw_orcid":"https://orcid.org/0000-0002-8474-9435","affiliations":[{"raw_affiliation_string":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India","institution_ids":["https://openalex.org/I74319210"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111122026","display_name":"Muhammad Azeem Akbar","orcid":null},"institutions":[{"id":"https://openalex.org/I63548447","display_name":"Lappeenranta-Lahti University of Technology","ror":"https://ror.org/0208vgz68","country_code":"FI","type":"education","lineage":["https://openalex.org/I63548447"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Muhammad Azeem Akbar","raw_affiliation_strings":["Department of Software Engineering, LUT University, Lapeenranta, Finland"],"raw_orcid":"https://orcid.org/0000-0002-4906-6495","affiliations":[{"raw_affiliation_string":"Department of Software Engineering, LUT University, Lapeenranta, Finland","institution_ids":["https://openalex.org/I63548447"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5121119318"],"corresponding_institution_ids":["https://openalex.org/I74319210"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.62953807,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"102","last_page":"107"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.22750000655651093,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12127","display_name":"Software System Performance and Reliability","score":0.22750000655651093,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.14139999449253082,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.11230000108480453,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.61080002784729},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.525600016117096},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.48350000381469727},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.44940000772476196},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4397999942302704},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.41609999537467957},{"id":"https://openalex.org/keywords/data-analysis","display_name":"Data analysis","score":0.39899998903274536}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7139999866485596},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.61080002784729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5278000235557556},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.525600016117096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.498199999332428},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4934000074863434},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.48350000381469727},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.44940000772476196},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4397999942302704},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.41609999537467957},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3725000023841858},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3727967.3756834","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727967.3756834","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 29th International Conference on Evaluation and Assessment in Software Engineering Companion","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3727967.3756834","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727967.3756834","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 29th International Conference on Evaluation and Assessment in Software Engineering Companion","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4503679871559143}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2947214952","https://openalex.org/W2963753542","https://openalex.org/W4206310306","https://openalex.org/W4241172984","https://openalex.org/W4285818136","https://openalex.org/W4307162961","https://openalex.org/W4309994923","https://openalex.org/W4318816989","https://openalex.org/W4321609416","https://openalex.org/W4391222025","https://openalex.org/W4400130126","https://openalex.org/W4400324527","https://openalex.org/W4400327757","https://openalex.org/W4400770731","https://openalex.org/W4400891489","https://openalex.org/W4401749006","https://openalex.org/W4403125095","https://openalex.org/W4403192527"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,81,88,93,96,109,123,140,143,156,159,170],"high":[2],"amount":[3],"of":[4,65,95,142,158],"data":[5,66,175],"in":[6,24,56,92,122,152,169],"applications":[7],"like":[8],"healthcare,":[9],"education,":[10],"and":[11,26,48,70,74,104,114,155,176],"finance,":[12],"there":[13],"is":[14,36,39,87,120],"a":[15,33,40,57,117],"requirement":[16],"for":[17,52],"sophisticated":[18],"analytical":[19],"methods":[20],"that":[21,38,67,78,99],"can":[22],"tune":[23,80],"real-time":[25],"generate":[27],"precise":[28],"forecasts.":[29],"In":[30],"this":[31],"paper,":[32],"proposed":[34,97,144,160],"methodology":[35,98],"introduced":[37],"novel":[41],"model":[42,82,110,124,161],"combining":[43],"both":[44],"artificial":[45],"intelligence":[46],"(AI)":[47],"machine":[49,75],"learning":[50,76,102],"(ML)":[51],"predictive":[53,165],"analytics":[54,166],"enhancement":[55],"dynamic":[58],"setting.":[59],"Technologies":[60],"presented":[61],"are":[62],"AI-assisted":[63],"pre-processing":[64],"detects":[68],"faults":[69],"extracts":[71],"relevant":[72],"features,":[73],"algorithms":[77],"dynamically":[79],"parameters.":[83],"The":[84,136],"resilience":[85],"mechanism":[86],"most":[89],"critical":[90],"component":[91],"working":[94],"utilizes":[100],"adaptive":[101],"rates":[103],"anomaly":[105],"detection":[106],"to":[107,111,125,133,163,172],"enable":[108,126],"detect":[112,177],"noise":[113],"anomalies.":[115,178],"Furthermore,":[116],"feedback":[118],"loop":[119],"included":[121],"continuous":[127],"optimization":[128],"based":[129],"on":[130],"performance":[131],"metrics":[132],"increase":[134],"accuracy.":[135],"following":[137],"work":[138],"discusses":[139,149],"design":[141],"methodology,":[145],"includes":[146],"an":[147],"algorithm,":[148],"its":[150],"applicability":[151],"different":[153],"industries,":[154],"benefits":[157],"compared":[162],"existing":[164],"models,":[167],"specifically":[168],"ability":[171],"rapidly":[173],"update":[174]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-12-23T00:00:00"}