{"id":"https://openalex.org/W2887436536","doi":"https://doi.org/10.1109/cns.2018.8433159","title":"Detecting Data Integrity Attacks on Correlated Solar Farms Using Multi-layer Data Driven Algorithm","display_name":"Detecting Data Integrity Attacks on Correlated Solar Farms Using Multi-layer Data Driven Algorithm","publication_year":2018,"publication_date":"2018-05-01","ids":{"openalex":"https://openalex.org/W2887436536","doi":"https://doi.org/10.1109/cns.2018.8433159","mag":"2887436536"},"language":"en","primary_location":{"id":"doi:10.1109/cns.2018.8433159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cns.2018.8433159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Conference on Communications and Network Security (CNS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068665347","display_name":"Kin Gwn Lore","orcid":null},"institutions":[{"id":"https://openalex.org/I4401726811","display_name":"Hartford Financial Services (United States)","ror":"https://ror.org/00mwq1g96","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726811"]}],"countries":[],"is_corresponding":false,"raw_author_name":"Kin Gwn Lore","raw_affiliation_strings":["United Technologies Research Center, East Hartford, CT, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"United Technologies Research Center, East Hartford, CT, US","institution_ids":["https://openalex.org/I4401726811"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012259366","display_name":"Devu Manikantan Shila","orcid":"https://orcid.org/0000-0001-9882-147X"},"institutions":[{"id":"https://openalex.org/I4401726811","display_name":"Hartford Financial Services (United States)","ror":"https://ror.org/00mwq1g96","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726811"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Devu Manikantan Shila","raw_affiliation_strings":["United Technologies Research Center, East Hartford, Connecticut"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"United Technologies Research Center, East Hartford, Connecticut","institution_ids":["https://openalex.org/I4401726811"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101680379","display_name":"Lingyu Ren","orcid":"https://orcid.org/0000-0001-8440-3939"},"institutions":[{"id":"https://openalex.org/I4401726811","display_name":"Hartford Financial Services (United States)","ror":"https://ror.org/00mwq1g96","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726811"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lingyu Ren","raw_affiliation_strings":["United Technologies Research Center, East Hartford, Connecticut"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"United Technologies Research Center, East Hartford, Connecticut","institution_ids":["https://openalex.org/I4401726811"]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4401726811"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10917","display_name":"Smart Grid Security and Resilience","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10917","display_name":"Smart Grid Security and Resilience","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9941999912261963,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9894999861717224,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7072791457176208},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6095777153968811},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5178823471069336},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5056593418121338},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4752497375011444},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.45951759815216064},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4564533233642578},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45438137650489807},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.41945239901542664},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3735613524913788},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3395736813545227},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2627429962158203},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10917237401008606}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7072791457176208},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6095777153968811},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5178823471069336},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5056593418121338},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4752497375011444},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.45951759815216064},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4564533233642578},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45438137650489807},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.41945239901542664},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3735613524913788},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3395736813545227},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2627429962158203},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10917237401008606},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cns.2018.8433159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cns.2018.8433159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Conference on Communications and Network Security (CNS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"},{"id":"https://openalex.org/F4320338289","display_name":"Brookhaven National Laboratory","ror":"https://ror.org/02ex6cf31"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1484932616","https://openalex.org/W1553799822","https://openalex.org/W1599867512","https://openalex.org/W2051054517","https://openalex.org/W2064675550","https://openalex.org/W2085723914","https://openalex.org/W2089468765","https://openalex.org/W2096766405","https://openalex.org/W2100495367","https://openalex.org/W2109553965","https://openalex.org/W2127979711","https://openalex.org/W2130942839","https://openalex.org/W2137983211","https://openalex.org/W2143842190","https://openalex.org/W2145094598","https://openalex.org/W2160815625","https://openalex.org/W2173575153","https://openalex.org/W2186910770","https://openalex.org/W2246905723","https://openalex.org/W2292851276","https://openalex.org/W2295733304","https://openalex.org/W2474046725","https://openalex.org/W2521362385","https://openalex.org/W2551992465","https://openalex.org/W2619874920","https://openalex.org/W2756950148","https://openalex.org/W2997574889","https://openalex.org/W3146803896","https://openalex.org/W4297814361","https://openalex.org/W6633035237","https://openalex.org/W6679436768","https://openalex.org/W6681096077","https://openalex.org/W6720514713","https://openalex.org/W6738520201"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W3210364259","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4300558037","https://openalex.org/W4377864969","https://openalex.org/W3030345572"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3],"novel":[4],"server-centric":[5],"anomaly":[6,45],"detection":[7],"strategy":[8],"for":[9],"large-scale":[10],"multi-modal":[11],"time-series":[12],"data":[13,33,83,104],"from":[14],"NSERC":[15],"solar":[16,28],"farm.":[17],"The":[18],"proposed":[19,98],"framework":[20,99],"utilizes":[21],"spatial":[22],"and":[23,36,72,91],"temporal":[24],"correlation":[25],"between":[26,41],"multiple":[27],"farms":[29],"to":[30,43],"defend":[31],"against":[32],"integrity":[34,84],"attacks":[35,85],"learns":[37],"the":[38,49,77,97],"inter-farm/intra-farm":[39],"relationship":[40],"measurements":[42],"perform":[44],"detection.":[46],"We":[47],"demonstrated":[48],"performance":[50],"of":[51,82,102,110],"five":[52,80],"machine":[53],"learning":[54],"algorithms-vector":[55],"autoregressive":[56],"models":[57,78],"(VAR),":[58],"deep":[59,73],"neural":[60],"networks":[61,66],"(DNN),":[62],"long":[63],"short-term":[64],"memory":[65],"(LSTM),":[67],"inverse":[68],"PCA":[69],"reconstruction":[70],"(iPCA),":[71],"auto-encoders":[74],"(DAE)-and":[75],"evaluated":[76],"on":[79],"varieties":[81],"including":[86],"replay,":[87],"correlated,":[88],"random,":[89],"delay,":[90],"scaling":[92],"attacks.":[93,111],"Results":[94],"show":[95],"that":[96],"is":[100],"capable":[101],"detecting":[103],"anomalies":[105],"over":[106],"these":[107],"large":[108],"variety":[109]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}