Smart Home Securty System Based on ANFIS LeeJeong-G 1,Lee Sang-Hyun 2, Moon Kyung-Il 2 1 Korea Electroncs Technology Insttute, Korea 2 Dept. o Computer Engneerng, Honam Unversty, Korea 2 Dept. o Computer Engneerng, Honam Unversty, Korea {leesang64, kmoon}honam.ac.kr, jklee@ket.re.kr Abstract.A smart home or buldng s a home or buldng, usually a new one that s equpped wthspecal structured wrng to enable occupants to remotely control or program an array oautomated home electronc devces by enterng a sngle command. Conventonal securtysystems keep homeowners, and ther property, sae rom ntruders. Smart home securty has two aspects, nsde and outsde. Insde securty covers the concept o securng home rom threats lke re etc. whereas, outsde securty s meant to secure home aganst any burglar/ntruder etc. In ths paper, we suggest an adaptve network uzzy nerence system or home securty.to deal wth nonlnear outputs, the system s modeled by multple ANFIS, and the optmzaton o multple outputs s ormulated as a multple objectve decson makng. Keywords: Fuzzy logc, Multple ANFIS, Smart Home Securty 1 Introducton A smart homecare system usng smart phones, wreless sensors, web servers and IP webcams s proposed by Lejdekkers et al [5]. It provdes aclty to elderly people to check ther health and status and provdes an easy way to contact to hosptal n an emergency. Ghorbel et al have proposed the ntegraton o networkng and communcaton technologes n the smart homes concept dedcated to people wth dsabltes. It s based on the UPnP protocol to dscover and control devces ndoor and uses wreless technologes to enhance moblty [2]. Popescu et al have proposed a securty archtecture allowng dgtal rghts management n home networks consstng o consumer electronc devces [2]. In the proposed model, devces are allowed to establsh dynamc groups n an envronment where legally acqured copyrghted content are seamlessly transmtted between devces. They have clamed that connectvty between devces has a mnmal relance on publc key cryptographc operatons. Gao et al have suggested the concept o a sel-programmng thermostat that wthout any human nterventon creates a best possble setback schedule by sensng the possesson statstcs o a home [1]. The system montors possesson usng smple sensors n the home and the user denes the desred balance between energy and comort usng a sngle knob. It s observed that ths approach has an advantage ISA 2013, ASTL Vol. 21, pp. 121-126, 2013 SERSC 2013 121
Proceedngs, The 7th Internatonal Conerence on Inormaton Securty and Assurance over EnergyStar setback schedule approach by reducng the heatng and coolng demand by up to 15%. The purpose o ths paper s to portray as to how Adaptve Network Fuzzy Inerence System (ANFIS) encounters the challenges posed to the sensor based classcal smart home systems and propose a methodology or mplementaton o these networks to buld an adaptve and ntellgent system. Home securty has two aspects, nsde and outsde. Insde securty covers the concept o securng home rom threats lke re etc. whereas, outsde securty s meant to secure home aganst any burglar/ntruder etc. Ths work s amed to provde a multple ANFIS soluton or home securty that takes decson dynamcally usng the pervasve devces. Also ths soluton has the eature to ntmate securty analyss results anywhere n the world usng nternet. In secton 2, we revew brely the tools related to the smart home securty, and represent a structure o ntellgent nerence module at nsde/outsde home securty server. In secton 3, a multple ANFIS model s proposed to provde optmal home securty soluton. In ths proposed model, sensors are used to detect abnormaltes wthn the house or outsde the house. In secton 4, smulaton results by the suggested model are demonstrated, and compared wth smple uzzy logc based method. Also, concludng remarks are gven n the last secton. 2 Smart Home securty Structure From some tools related to the smart home securty, t s observed that the home securty models have consdered some lmted securty concerns. Thereore one securty model may be good n one stuaton but cannot provde the requred results n other stuatons. To provde optmal home securty soluton, a new model s requred. In ths model, sensors are used to detect abnormaltes wthn the house or outsde the house. There s a dedcated server or the sensors used to collect data nsde the house. Ths server s responsble to collect normaton transmtted by the sensors and then analyze to detect any abnormalty. Smlarly, a separate server s used to process the normaton transmtted by sensors located outsde the house. Both these servers are connected to a man server whch process the normaton provded by these servers. ANFIS tool s used to detect any abnormalty. In case a threat s detected then man server report about the threat to concern people usng nternet besdes settng the alarms on. Sx nput types are provded to the system. Multple sensors o each type are used to collect data. All nputs o same sensor type are provded to an ntal ANFIS nerence module, whch s responsble to calculate the threshold value. These calculated threshold value o each nput type s then provded to respectve server responsble or nsde or outsde securty. An overall threshold value o these sx ntal threshold values s separately calculated usng ANFIS module on nsde/outsde securty servers respectvely. Both nsde/outsde securty threshold values are provde to man server or analyss. Fnal decson s made based on these values. I any o the value s above the crtcal value then alarm sgnal s generated to respectve person/department. Usng ths method, t s possble to generate derent output alarms consderng the ntensty and relevance o threshold value to that specc 122
Smart Home Securty System Based on ANFIS person/department. Threshold values calculated at the nsde/outsde servers are collated at man server or decson makng process. Ater collaton process, threshold value s calculated and alarm sgnal type or each desred destnaton (polce, rescue staton, owner, et al) s calculated. Multple ANFIS s an extenson o the sngle output neuro-uzzy system ANFIS [4], or producng multple outputs. Smart home securty problem s a process wth multple outputs. Thereore, modelng and optmzaton o a process wth multple outputs s requred. A neuro-uzzy system can serve as a nonparametrc regresson tool, whch model the regresson relatonshp non-parametrcally wthout reerence to any pre-speced unctonal orm. Every sngle ANFIS n an multple ANFIS smulatesthe unctonal relatons, =1,,m. ANFIS can be consdered as anetwork presentaton o a TSK uzzy nerencesystem, and the -then rules n TSKare comprsed n the network structure. Tollustrate the archtecture o ANFIS, an examplewth a two-dmensonal nput s vsualzedn Fg.1. Fg. 1.ANFIS archtecture 3 An ANFIS desgn By means o the learnng process, multple ANFISobtans an estmaton o desred outputswth gven nputs. Let, =1,...,m, be the-th output o multple ANFIS, and they are estmateso multple responses y 1,...,y m, respectvely.to ndcate these estmates are unctonso the nput varables x, they wll bedenoted as (x), =1,...,m.Snce the system under dscusson has multpleresponses, the optmzaton o the systemn act nvolves the optmzaton o severalndvdual responses at the same tme.for all the system responses, they can be dvdednto three sets: (a) thelargerthebetter, denoted by L; (b) thesmallerthebetter, denotedby S; and 3) thenomnalthebest, denotedby N. We have ormulated ths optmzatonproblem as a multple objectve decsonmakng problem wth the ollowng orm: (1) mn max ( x), l L mn ( x), s S ( x) T, t N t l s t s. t. x B 123
Proceedngs, The 7th Internatonal Conerence on Inormaton Securty and Assurance HereT t s the nomnal target o the t-th response;and B s a easble regon o x. We ollow the dea ozmmermann's maxmn approach. Accordngto the maxmn approach, the above solutoncan be obtaned by maxmzng anoverall satsactory degree among all ndvdualobjectves. That s, or each objectve,t has ts own satsactory degree, andthe overall satsacton s an ntersecton o allndvdual satsactory degrees, where the ntersectons dened through a mn operator.the satsactory degree or each objectve sevaluated by userdened membershpuncton.each response's membershp unctonμ should be well chosen so as torelect ts characterstc. For the response belongedto the set o thelargerthebetter, tsdegree o satsacton reaches 1 when t s at = max x B{ ( x) } and then decreases monotoncally to 0 at = max x B{ ( x) }. Atypcal membershp uncton or, L, could be stated as (2) µ = ( ) /( 1, > 0, < ), For the response belonged to the set o thesmallerthebetter, we set the satsactorydegree to 1 when a response s at - andthen t decreases monotoncally to 0 at. Such type o membershp unctons canbe expressed as (3) µ = ( ) /( 1, 0, < ), > Smlarly, or the response o the set thenomnalthebest, the degree o satsactons maxmzed when t s at ts target T,and decreases as t s away rom T. The maxmum o λ cannot bedrectly solved by the use o dervatve-basedmethods due to unknown unctonal orms o. Dervatve-ree methods aredeally suted or solvng problems where dervatvenormaton s unavalable. Alternatvely,we can approxmate the dervatveswth numercal methods. 4 Smulaton results Home securty system s congured by sensor nodes connected to server. These sensor nodes nclude rado requency, ultrasonc, temperature, lght, sound and vdeo sensors. Threshold value or each nput s above 90% and or a vdeo sensor, used n outsde securty, dstance threshold s taken as 1 eet. I value s ncreased rom any threshold value then alarm s on, and noted to speced locaton through nternet. For a sample scenaro, where only three types o sensors are used namely vdeo, re and voce. Fve layers o neural networksresultng rom the ANFIS have been provded n gure 2.Eect othreshold values o nput sensors and ANFIS output Polce s somewhat lower than smple uzzy logc based one, and eect o 124
Smart Home Securty System Based on ANFIS threshold values o nput sensors and ANFIS output Rescue staton s somewhat hgher than smple uzzy method. Also, eect o threshold values o nput sensors and ANFIS output Owner s somewhat hgher than smple uzzy method. For example, n case o vdeo Sensor=88.2, Fre Sensor=89.5, and Voce Sensor=86.4, eect o threshold values o nput sensors and respectve ANFIS output s 78.8, 84.0 and 95.0. Eect o threshold values o nput sensors and respectve uzzy logc based output s 81.9, 81.5 and 92.5. Snce the outputs Owner and Rescue belongs to the set o thelargerthebetter, ts membershp uncton should takethe orm o (2); and the output Polce has anomnal target, so t wll take the membershpuncton (3). In order to determne thesemembershp unctons, the maxmum andmnmum or ndvdual output must be obtaned.maxmum and mnmum o outputs can be obtaned by ormulatng sngle objectveprogrammng problems or ndvdualresponses, and solvng the problems wth anydervatve-ree algorthm. Alternatvely, theycan also be subjectvely determned accordngto users' judgment or ther expectaton. In our scenaro, t s desred that the outputs o Owner and Rescue to be held between 92 and 98,thereore, t s reasonable to set 92 and 98 asthe mnmum and maxmum o ths output,respectvely. Smlarly, the mnmum and maxmum o Polce are set as 70 and 91, respectvely.in gure 3, 3D graph show the relatonshp betweenvoce sensors, re sensors and output threshold orrescue. Eect o threshold values s represented very well than smple uzzy logc based output. In gure 4, 3D graph show the relatonshp betweenvoce sensors, re sensors and output threshold orpolce. It s more reasonable than the relatonshp by smple uzzy logc. Fg. 2.ANFIS structure or sample scenaro 125
Proceedngs, The 7th Internatonal Conerence on Inormaton Securty and Assurance Fg. 3. Rescue surace or re and voce sensorfg. 4. Polce surace or re and voce sensor 5 Concludng Remarks Ths study used an adaptve network uzzy nerence system orsmart home securty. Ths system provdes the advantage omodelng a nonlnear and complcated systemwthout the need o ndng sutable unctonalorms or the system, and ts neural networklearnng ablty also equps multple ANFIS wthhgh ecency n smart home securty system modelng.proposed systemnherts the propertes o ANFIS and thus provdesntermedary values as compare to Boolean logc bvalueoutputs.however,snce we use the nonparametrc regressontool or multple ANFIS to model outputs, exactunctonal orms o outputs are not knownand hence dervatve-based optmzaton cannot be drectly appled to obtanthe optmal soluton. Reerences 1. Gao,G.,Whtehouse,K.: The Sel-ProgrammngThermostat: Optmzng Setback Schedules based on HomeOccupancy Patterns, Proceedngs o BuldSys 09, November 3,Berkeley, CA, USA (2009) 2. Ghorbel,M., Segarra,M., Kerdreux,J., Keryell,R., A.,Thepaut,and M. Mokhtar.: Networkng and Communcaton n SmartHome or People wth Dsabltes, Computers Helpng Peoplewth Specal Needs, Sprnger Berln / Hedelberg, 624, (2004) 3. Hou,J.,O Bren, D. C.: Vertcal handover-decson-makng algorthm usng uzzy logc or the ntegrated radoand OW system, IEEE Transactons on Wreless Communcatons,5(1),176--185, (2006) 4. Jang,J. S. R.: ANFIS: Adaptve network based uzzy nerence system,ieee Transactons on Systems, Man andcybernetcs, 23(3),665--685, (1993) 5.Lejdekkers,P., Gay,V., Lawrence,E.: Smart HomecareSystem or Health Tele-montorng, Proceedngs o the FrstInternatonal Conerence on the Dgtal Socety, IEEEComputer Socety(2007) 6. Popescu,B. C., Crspo,B., Tanenbaum,A. S.,Kamperman, F. L. A. J.: A DRM Securty Archtecture or HomeNetworks, Proceedngs o the 4th ACM workshop ondgtal rghts management, October 25, Washngton, DC,USA.(2004) 7. Reyhan,S. Z.,Mahdav,M.: User Authentcaton Usng Neural Network n Smart HomeNetworks, Internatonal Journal o Smart Home, 1(2), July (2007) 126