{"id":"https://openalex.org/W4414808123","doi":"https://doi.org/10.1109/icmla66185.2025.00145","title":"Memory-Augmented Log Analysis with Phi-4-mini: Enhancing Threat Detection in Structured Security Logs","display_name":"Memory-Augmented Log Analysis with Phi-4-mini: Enhancing Threat Detection in Structured Security Logs","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W4414808123","doi":"https://doi.org/10.1109/icmla66185.2025.00145"},"language":"en","primary_location":{"id":"doi:10.1109/icmla66185.2025.00145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00145","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.00529","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119840454","display_name":"Anbi Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anbi Guo","raw_affiliation_strings":["Pennsylvania State University,School of Electrical Engineering and Computer Science,University Park,PA,USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University,School of Electrical Engineering and Computer Science,University Park,PA,USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5119840455","display_name":"Mahfuza Farooque","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mahfuza Farooque","raw_affiliation_strings":["Pennsylvania State University,School of Electrical Engineering and Computer Science,University Park,PA,USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University,School of Electrical Engineering and Computer Science,University Park,PA,USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5119840454"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31230834,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"966","last_page":"971"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9857000112533569,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9857000112533569,"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":0.9753999710083008,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9671000242233276,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/context","display_name":"Context (archaeology)","score":0.6439999938011169},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5892999768257141},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.35659998655319214},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.34290000796318054},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.3393999934196472},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.33889999985694885},{"id":"https://openalex.org/keywords/buffer-overflow","display_name":"Buffer overflow","score":0.31949999928474426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7979999780654907},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6439999938011169},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5892999768257141},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37869998812675476},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.35659998655319214},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.34290000796318054},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.33889999985694885},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3255000114440918},{"id":"https://openalex.org/C40842320","wikidata":"https://www.wikidata.org/wiki/Q19423","display_name":"Buffer overflow","level":2,"score":0.31949999928474426},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.30489999055862427},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C2780264999","wikidata":"https://www.wikidata.org/wiki/Q7445032","display_name":"Security domain","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.28940001130104065},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2833999991416931},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C154908896","wikidata":"https://www.wikidata.org/wiki/Q2167404","display_name":"Security policy","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2612000107765198},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2603999972343445}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icmla66185.2025.00145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00145","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2510.00529","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.00529","pdf_url":"https://arxiv.org/pdf/2510.00529","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.00529","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.00529","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.00529","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.00529","pdf_url":"https://arxiv.org/pdf/2510.00529","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414808123.pdf","grobid_xml":"https://content.openalex.org/works/W4414808123.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Structured":[0],"security":[1],"logs":[2],"are":[3],"critical":[4],"for":[5,34,44,52],"detecting":[6],"advanced":[7],"persistent":[8],"threats":[9],"(APTs).":[10],"Large":[11],"language":[12],"models":[13],"(LLMs)":[14],"struggle":[15],"in":[16,89],"this":[17],"domain":[18,24],"due":[19],"to":[20],"limited":[21],"context":[22,61],"and":[23,47,62,81,86,96],"mismatch.":[25],"We":[26],"propose":[27],"DM-RAG,":[28],"a":[29,40,48],"dual-memory":[30],"retrieval-augmented":[31],"generation":[32],"framework":[33],"structured":[35,64],"log":[36],"analysis.":[37],"It":[38],"integrates":[39],"short-term":[41],"memory":[42,51],"buffer":[43],"recent":[45],"summaries":[46],"long-term":[49],"FAISS-indexed":[50],"historical":[53],"patterns.":[54],"An":[55],"instruction-tuned":[56],"Phi-4-mini":[57],"processes":[58],"the":[59,74],"combined":[60],"outputs":[63],"predictions.":[65],"Bayesian":[66],"fusion":[67],"promotes":[68],"reliable":[69],"persistence":[70],"into":[71],"memory.":[72],"On":[73],"UNSW-NB15":[75],"dataset,":[76],"DM-RAG":[77],"achieves":[78],"53.64%":[79],"accuracy":[80],"98.70%":[82],"recall,":[83],"surpassing":[84],"fine-tuned":[85],"RAG":[87],"baselines":[88],"recall.":[90],"The":[91],"architecture":[92],"is":[93],"lightweight,":[94],"interpretable,":[95],"scalable,":[97],"enabling":[98],"real-time":[99],"threat":[100],"monitoring":[101],"without":[102],"extra":[103],"corpora":[104],"or":[105],"heavy":[106],"tuning.":[107]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2025-10-10T00:00:00"}
