nyuuzyou/kompy huggingface dataset:
Foydalanilgan adabiyotlar ro‘yxati

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Foydalanilgan adabiyotlar ro‘yxati
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bet | 4/5 | Sana | 15.11.2023 | Hajmi | 2,91 Mb. | | #99023 |
Bog'liq kiberdanFoydalanilgan adabiyotlar ro‘yxati
«Axborotlashtirish to‘g‘risida».O‘zbekiston Respublikasining qonuni. Toshkent shahri, 2003 yil 11 dekabr.
Zamonaviy axborot-kommunikatsiya texnologiyalarini yanada joriy etish va rivojlantirish chora-tadbirlari to‘g‘risida. O‘zbekiston Respublikasi Prezidentining Qarori, 2012-yil 21-mart, PP-1730.
O‘zbekiston Respublikasi Prezidentining «Oliy ta’lim tizimini yanada rivojlantirish chora-tadbirlari to‘g‘risida»gi PQ-2909-sonli Qarori, 2017 yil 20 aprel//O‘zbekiston Respublikasi qonun hujjatlari to‘plami, 2017 y.
Talaba mustaqil ishini tashkil etish to‘g‘risida. O‘zR OO‘MTVning 2009 yil 21-fevraldagi 286 - sonli buyrug‘i.
Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universitetida kadrlar tayyorlash tizimini tubdan takomillashtirish va samaradorligini oshirish chora-tadbirlari to‘g‘risida. O‘zbekiston Respublikasi Vazirlar Mahkamasining qarori. Toshkent sh., 2018 yil 24 iyul, 569-son.
Cybersecurity Curricula 2017 – Curriculum Guide lines for Post-Secondary Degree Programs in Cybersecurity (ACM, IEEE-CS, AIS SIGSEC, IFIP WG 11.8) (Crosscuttingconcepts)
Axborot texnologiyalari va kommunikatsiyalarining jorim etilishini
nazorat kilish, ularni ximoya qilish tizimini takomillashtirish chora-tadbirlari to‘g‘risida. O‘zbekiston Respublikasi Prezidentining qarori. Toshkent sh., 2018 yil 21 noyabr, PK-4024.
O‘zR OO‘MTVning 2005-yil 21-fevraldagi 34-sonli «Talaba mustaqil ishini tashkil etish to‘g‘risida»gi buyruq bilan tasdiqlangan namunaviy nizom.
ISO/IEC 27000 Information technology. Security techniques. Information security management systems. Overview and vocabulary.
Ғаниев С. К., Каримов М. М., Ташев К. А. “Ахборот хавфсизлиги”, “Фан ва технологиялар”нашриёти, Тошкент 2016
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Topshiriq (4 tipli) – o’qituvchi javobni elektron ko’rinishda olish uchun ishlatish mumkin (ixtiyoiy formatda).’
 Ma’ruza

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Topshiriq (4 tipli) – o’qituvchi javobni elektron ko’rinishda olish uchun ishlatish mumkin (ixtiyoiy formatda).’
Ma’ruza
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bet | 2/4 | Sana | 27.05.2024 | Hajmi | 1,18 Mb. | | #254516 |
Bog'liq 8-mavzuTopshiriq (4 tipli) – o’qituvchi javobni elektron ko’rinishda olish uchun ishlatish mumkin (ixtiyoiy formatda).’
Ma’ruza – har bir sahifasi talaba javob berishi lozim bo’lgan savol bilan tugaydigan sahifalar to’plami. Javobning to’g’riligiga bog’liq holda, talaba keyingi sahifaga o’tadi yoki oldingi sahifaga qaytadi. Bu ta’lim yo’nalishini aniqlashni va o’qitiladigan fan tushunarsiz bo’lib qolmasligini ta’minlaydi.
So’rovlar – talabalarning ovoz berishi.
Sharh – kurs sahifasidagi ixtiyoriy matn va grafika.
Ish daftari – berilgan mavzu bo’yicha talaba fikrini bildiradigan joy.
Resurs – avtomatik tasvirlanadigan turli fayllarni yuklash va tasvirlash vositasi. Masalan, ma’ruza audioyozuvini yuklashda u mediaproigrivatel sifatida tasvirlanadi.
Seminar – qatnashchilar bir-birining ishini baholaydigan topshiriq.
Testlar – turli variantli testlar to’plami. Savollar bir nechta variantli javoblardan, to’g’ri/noto’g’ri tanlovdan, qisqa matnli javobdan va boshqalardan iborat bo’lishi mumkin.
Forum – forumning 3 ko’rinishi mavjud (savol-javob, hammaning o’z mavzusi, standart muzokara).
Chat – real vaqtdagi muzokara.
Moodle MO’T dunyoning 200 davlatida 70 tildagi 2 mln. ga yaqin ro’yxatdan o’tgan foydalanuvchilar, 46 ming ta’lim portallariga ega va 300 dan ziyod dasturchilarni birlashtiradi.
Moodle (http://www.gnu.org/copyleft/gpl.html) GPL lisenziyasi ostida (http://www.opensource.org/docs/definition_plain.html) ochiq joriy kodi bilan dasturiy ta’minot kabi tarqalmoqda.
Ochiq dasturiy kodli Moodle ga o’zgartirishlar kiritish, yaxshilash, modifikasiyalash uchun http://www.moodle.org manzil bo’yicha kirish mumkin.
Moodle ni ishlatish strategiyasi, filosofiyasi bo’yicha qo’shimcha axborotlarni http://thinkingdistance.org/ manzil bo’yicha olish mumkin.
“Moodle” so’zi – bu “Modular Object - Oriented Dynamic Learning Environment” so’zining abbreviaturasidir.
Rus tilida “Mudl” va “Moodus” (Modulnaya ob’ektno-orientirovannaya dinamicheskaya upravlyayuщaya sreda) nomlari ham qo’llaniladi [13,14].
Moodle Unix, Linux, FreeBSD, Windows, Mac OS X, Netware operasion tizimlarida va PHP ishlatilishi mumkin bo’lgan ixtiyoriy boshqa tizimlarda ham modifikasiyalarsiz qo’lanilishi mumkin. Ma’lumotlar MySQL va PostgreSQL ma’lumotlar bazasida saqlanadi, ammo ma’lumotlar bazasini boshqarishning tijorat tizimlari ham ishlatilishi mumkin.
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Bosh sahifa
Aloqalar
Bosh sahifa
Topshiriq (4 tipli) – o’qituvchi javobni elektron ko’rinishda olish uchun ishlatish mumkin (ixtiyoiy formatda).’
Ma’ruza
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Elektron ta’lim tizimi - 7-Mavzu: Moodle tizimining interfaol imkoniyatlari

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7-Mavzu: Moodle tizimining interfaol imkoniyatlari
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bet | 3/4 | Sana | 27.05.2024 | Hajmi | 1,18 Mb. | | #254516 |
Bog'liq 8-mavzuElektron ta’lim tizimi.
Elektron ta’lim (inglizcha. E-learning, Electronic Learning inglizcha kiskartirma) — axborot va eletron texnologiyalar yordamida ta’lim olish tizimi
YuNESKO mutaxassislari tarifi: «e-Learning — internet va multimediya yordamida ta’lim olish».
Elektron ta’lim o‘quv - uslubiy majmualarning asosiy manbai, bilimni nazorat qilish va internet tarmog‘i o‘quv jarayoni ishtrokchilari orasida axborot muxitining transport vositasi
Elektron ta’limga kuyidagilar mansub:
Shaxsiy kompyuterlar, smartfonlar, planshetlar, DVD, televizor va boshqalar orqaliy elektron resurslar orqali mustaqil ta’lim olish;
Kaysi xududda joylashganidan qattiy nazar mutahassisning (professor-o‘qituvchi) masofadan turib o‘zaro yordami, maslahatlari, baholar olish imkoniyati;
Ijtimoiy tarmoqlarda vertual o‘quv faoliyati bilan shug‘illanayotgan mutaxassislar uyushmalarini tashkil etish;
O‘quv materillarni, standarlar, elektron ta’lim tehnologiyalarini holagan vaqtda olish imkoniyati;
Ta’lim muassasalarida raxbarlarida, bo‘limlarida zamonaviy axborot komunikatsion-texnologiyalar madaniyati shakllantirish va yuksaltirish, ish faoliyati samaradorligini oshirish erishish;
Innovatsion pedagogik texnologiyalarni professor-o‘qituvchilar tomonidan o‘zlashtirish va ommalashtirishga erishish;
O‘quv veb-resrularni modernizatsiya qilish imkoniyati;
Xolagan vaqtda, holagan joyda va dunyoning holagan nuqtasida kerakli bilimlarni olish imkoniyati;
Oliy ta’limni olishda imkoniyati cheklangan shaxlar foydalanish kulayligi.
Learning
Management
System
(LMS — tizimlari ) kuchli 20 talik
Xabarlar bilan almashish
Amaliy
Test
Chat
Maruza (Лекция)
Kitob (Книга)
Xavola (Ссылка)
Izoh (Пояснение)
Taqdimotт (Презентация)
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Tabliy ekotizimning klassifikatsiyasi

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Tabliy ekotizimning klassifikatsiyasi
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bet | 19/119 | Sana | 07.02.2024 | Hajmi | 0,61 Mb. | | #152531 |
Bog'liq Ekologiya 11. Rom уоrм ато. Л tos II кenт z 1 L-fayllar.org3.3 Tabliy ekotizimning klassifikatsiyasi
Ekotizimlar yerning geografik landshaftini tashkil qiluvchi Ьo'lgani uchun tabliy geografik landshaftning ajralmas qismi bo'lib hisoЫanadi. Biosenozlar yerning ustki qismida Ьiosferaning asosiy qismini tashkil qiluvchi Ьiogeosfyerani hosil qiladi.
Landshaft-tabliy geografik majmua bo'lib, o'z ichiga litosferaning
yuqori gorizontlari, relef, iqlim, suv, tuproqlaming o'zaro aloqasini taminlaydi, rivojl·anishda yagona tizim uchun Ьir xil sharoit yaratib byeradi. Kelib chiqishiga qarab, landshaftni ikkiga Ьo'linadi: tabliy va antropogen.
Tabliy landshaftlar inson ishtirokisiz taЬiiy omillar tasirida shakllanadi. Tabliy landshaftlar o'z navbatida 3 ga bo'linadi:
Geokimyoviy, yeming ustki qismidagi kimyoviy elementlar soni
va tarkibl Ьirikmalari.
Elementar-ma'lum uchaskada aniq Ьir jinslar ma'lum Ьir relefda joylashishi, yer osti suvlari, o'simliklar qoplarni va Ьir xil tuproq sikllarida bo'lishiga aytiladi.
Qo'riqlanadigan ma'lum Ьir hududlarda mutlaqo insonlar tomonidan u yoki bu ishlar olib borilishi mutlaqo taqiqlanadi.
Hozirgi paytda antropogen landshaftlar tabliy landshaftlarga qara
ganda ustunlik qilmoqda. Inson tasirida xo'jalik ish yuritish natijasida
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taЬiiy landshaft mutloq o'zgarib ketadi. Antropogen landshaftlar o'z navbatida:
-agromadaniy-(qishloq xo'jaligi) bunda qishloq o'simliklarini ekish va Ьog'lar tashkil qilish natijasida yuzaga keladi.
-texnogen-insonning texnogen faoliyati natijasida hosil bo'lgan struktura bo'lib baquvvat texnik vositalardan foydalanish natijasida
tuproq strukturasi buzilishi, unumdorligi pasayishi, atmosferaga zararli gazlaming tashlanishi va boshqalar.
-indшtrial-yirik sanoat majmualari natijasida muhitning o'zga
rishi.
-shahar-(urbanizatsiya) ga xos qurilgan uy-joylar, ko'chalar, istirohat bog'lari va turli komunikatsiyalar.
Landshaft qoЬig'ining asosiy energiya manbai-quyosh nuri bo'lib, u fotosintez borishidagi muhim komponentdir. Shuni aytish kerakki Yerga tushadigan Quyosh energiyasini bori-yo'g'i 2-3% igina fotosintez
jarayonida foydalaniladi. Quyosh nuridan foydalanishni 5% ga etkazilganda olinadigan mahsulotlar miqdori ikki baravarga oshadi.
Quyosh nurini qolgan qismi ablotik muhitga tushadi o'z-o'zidan
to'kilish, fizik-kimyoviy chirish jarayonlarida ishtirok etadi. O'simlik va hayvonot olami tablatdagi kuchli Ьirikma bo'lib ular tabliy muhitga tasir qilib, uni o'zlariga xos holga moslashtiradilar hamda alohida mikroiqlim paydo qiladilar. Turli ekotizimlarda butun planeta bo'ylab mahsuldorlik miqdori turlicha bo'ladi. Mahsuldorlikning o'zgarishi iqlim, oziqlanish maydoni, tuproq, ekologik omillar tasirida boradi.
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Ekotizimning mahsuldorligi

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Ekotizimning mahsuldorligi
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bet | 24/119 | Sana | 07.02.2024 | Hajmi | 0,61 Mb. | | #152531 |
Bog'liq Ekologiya 11. Rom уоrм ато. Л tos II кenт z 1 L-fayllar.org5.2. Ekotizimning mahsuldorligi
Bunda produtsentlarning quyosh nuri yordamida fotosintez jarayonlaming borishi va organik moddalarning hosil bo'lish tezligidir yoki ozuqa sifatida istemol qilinishidir. Produtsentlar tomonidan ma'lum vaqt Ьirligida hosil bo'lgan organik massa Ьirlamchi mahsulot, vaqt Ьirligida konsumentlar massasining oshishi, ikkilamchi mahsulot olish deyiladi. Ekotizimlardagi hamma tirik organizmlar Ьir butun yagona massani hosil qiladi. ,
Har Ьir ozuqa zanjiri, Ьirlamchi va ikkilamchi mahsuldorlikning hosil bo'lish tezligi hamda mahsulotlaming taqsimlanishi turlichadir. Ekotizimlarda Ьirlamchi va ikkilamchi hosildorlikning miqdoriy ko'rsatkichlari mavjud. Bunga hosildorlikpiramidasi qonunideyiladi.
Piramidalaming quyidagi turlari mavjud:
Son piramidasi-organizmlar soni turli darajada aks ettiradi;
Вiomassa piramidasi-tirik organizmlaming massasini ko'rsatadi;
Mahsulot piramidasi-Ьirlarnchi mahsulotning turli ozuqa zanjirlarida o'zgarib borishiga aytiladi;
Son piramidasida o'simliklar soni ulami istemol qiladigan hayvonlarga qaraganda ko'p.
Biomassa piramidasida o'simliklar Ьiomassasi ulami yeydigan hayvonlar Ьiomassasiga qaraganda ko'proq.
Mahsuldorlik piramidasi har Ьir trofa zanjirida hosil bo'lgan
Ьiomassa miqdori o'sha vaqt Ьirligida keyigni ozuqa zanjariga qara ganda ko'p bo'ladi.
Insonlar tomonidan hosil qilingan agrotsenozlar (Ьirlamchi mah
suldorlik resursi) Ьo'lib mahsuloti jamiyat uchun ozuqa zahirasini to'plab byeradi. lkkilamchi mahsuldorlik qishloq xo'jalik hayvonlari va baliqlardan olinadi.
O'simlik va hayvondan olinadigan Ьiomassa miqdorini doimo joylashgan muhit ularni turi, ko'payishi, yetilishini hisobga olgan holda
foydalanish zarur. Уer sharida Ьiologik mahsuldorlik notekis taqsimlan gan, unga zarar yetkazish ekotizimga zarar berish hisoЫanadi yoki
ozuqa zanjirining buzilishiga olib keladi. 1969 yil davlatlar o'rtasidagi xalqaro kelishuvga Ьinoan «Xalqaro Ьiologik Dastur» ish olib boradi.
Iqlimi mo"tadil hududlarda o'simliklaming Ьiomassasi yuqori bo'ladi, buning uchun avalo havo harorati va yog'ingarchilik yetarli
bo'lishi kerak. Tropik va subtropik hududlarda o'simliklar 250-300 sentnyer hosil byersa respuЬlikamiz cho'l va sahrolarida Ьiologik hosil
dorlik 2-20 ts/ga ni tashkil qiladi.
Ekotizimning o'zi o'zini saqlab qolish uchun turli noqulayliklarni yo'qotib tizim barqarorligini saqlab qoladi yoki o'zini Ьoshqaradi. Ekotizim o'zini Ьir qator mexanizmlar asosida ijobly va salЬiy aloqalar
orqali o'zini saqlab qoladi. Masalan populyatsiyada Ьir tur yirtqichning ko'payib ketishi dastlab o'ljaning, keyinchalik uning o'zining kamayib
borishiga olib keladi. Populyatsiya chegaralovchi omillar yordamida yirtqich kamayib boradi.
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S.3. Biotsenoz tur, tarqalishi va ekologik strukturasi

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S.3. Biotsenoz tur, tarqalishi va ekologik strukturasi
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bet | 25/119 | Sana | 07.02.2024 | Hajmi | 0,61 Mb. | | #152531 |
Bog'liq Ekologiya 11. Rom уоrм ато. Л tos II кenт z 1 L-fayllar.orgS.3. Biotsenoz tur, tarqalishi va ekologik strukturasi
Jamoaning mavjudligi uchun faqatgina turlaming soni emas balki ulaming xilma-xilligi ham lozim, xilma-xillik tablatda Ьiologik xilma xillikni saqlab turadi. Bioxilma-xillik deyilganda ma'lum Ьir chegaradagi turlar va ekotizim tushuniladi. Xilma-xillik turlarning va populyatsiyalaming doimiy holda saqlanishiga imkon byeradi.
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Biotsenoz turlar, joylar va ekologik struktura bo'yicha Ьir-Ьiridan farqlanadi.
Tur strukturasi-blotsenoz hosil qiluvchi turlar sonining ular
massasi yoki soniga nisbati:
-Biotsenozning tur strukturasida Ьir tur soni yoki massasi egallagan maydon yoki joyga nisbatan ko'p bo'lishi mumkin.
-Kam uchrashi-ma'lum joyda Ьir tuming soni yoki massasi umumiy soniga qaraganda namuna olish yoki hisob maydonchasidan
aniqlash orqali aniqlanadi.
-Dominantlik darajasi-Ьir tuming ushbu hududdagi jami turlarga nisbati.
Tur xilma-xilligi-ushbu jamoa yoki hududda uchraydigan turlar
soniga aytiladi. Biotsenozlarda turlar soni juda ko'p va kam bo'lishi mumkin. Turlar xilma-xilligi o'simlik yoki hayvon turining yoshiga ham Ьog'liq Ьo'ladi. Turlar yosh bo'lsa tur soni kam bo'ladi, shuningdek, tur soni ekologik omillardan ham kelib chiqadi, o'sish va yashashga sharoit qulay bo'lsa turlar soni ko'p bo'ladi. Tog'lar, sahrolar, sho'r tuproqlarda Ьioxilma-xillik kam bo'ladi.•
Jamoada dominant turlar bo'lib ular son jihatdan ko'p Ьo'lishi, ikkinchi darajali va kam sonli kamyob bo'lishi mumkin. Dominantlar ichida Ьioxilma-xillikni saqlab turuvchi-quruvchi edifikatorlar bo'ladi, bular o'sha joyda mikroklimat hosil qilib turlarning shakllanishi uchun sharoit yaratadi.
Joy blotsenozi- bu turli turga mansub organizmlaming vyertikal yoki gorizontal joylashishidir. Joy strukturasi eng avvalo o'simliklar
Ьiotsenozida yaqqol ko'rinadi. Biotsenozning vyertikal strukturasida yaruslar bo'yicha joylanishni ko'rish mumkin, gorizontal Ьiotsenozda rangbaranglik yoki mozaikani ko'rish mumkin. Bu yerda uchraydigan o'simliklar ichida dub, lipa, chinor Ьirinchi darajali bo'lsa, olma, nok,
do'lana, pastda ikkinchi darajalilar, qorag'at, yovvoyi bodomcha va
boshqa butalar, so'ng chalabuta va baland bo'yli o'tlar uchinchi darajalilarga kiradi.
Ekologik strukturasi-turli ekologik guruhlarga kiruvchi organizm laming o'zaro nisbatidir. Ekologik strukturasi o'xshash Ьiotsenozlar turli tur tarkiЬiga ega bo'lishi mumkin.
Yashash joyi-bu populyatsiyalaming yashaydigan quruqlikda yoki
suvdagi hududi yoki akvatoriyasi hisoЫanadi. Tuming yashash joyi uning ekologik nishasi tarkibldir. Jamoaning yashab oziqlanadigan joyiga blota deyiladi.
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Nazorat savollari - Ekologiya 11. Rom уоrм ато\. Л tos II кenт z 1: L

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Ekologiya 11. Rom уоrм ато\. Л tos II кenт z 1: L
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bet | 20/119 | Sana | 07.02.2024 | Hajmi | 0,61 Mb. | | #152531 |
Bog'liq Ekologiya 11. Rom уоrм ато. Л tos II кenт z 1 L-fayllar.orgNazorat savollari
Biosenoz deb nimaga aytiladi?
Mikro blosenoz tushunchasiga tarif byering?
Produsentlar tushunchasiga tarif byering?
Konsumentlar tushunchasiga tarif byering ular qanday farqlanadi?
Fosfor aylanishi nima?
Tabliy landshaftlar nima?
8. Agrosenozda qaysi o'simliklar o'sadi?
11
IV. ВОВ POPULYATSIYALAR EKOLOGIYASI
4.1 Populyatsiyalar tarifi
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Populyatsiya- deganda Ьir turga kiruvchi, o'zi ko'payuvchi ma'lum maydonda tarqalgan va Ьir-Ьiri Ьilan o'zaro bog'langan guruh tushuni-ladi. «Populyatsiya» so'zi lotincha «populyus» so'zidan olingan bo'lib «xalq», «aholi» degan manoni anglatadi.
Areal-ma'\um Ьir populyatsiya va turning butun urnri davomida yashaydigan joyiga yoki bo'shliq maydonga aytiladi. Areallar tarqalishi va hajmiga qarab yaxlit va bo'laklarga bo'lingan bo'lishi mumkin, albat
ta ma'lum geografik to'siqlar, bo'shliq\ar tasirida ma'lum tur rivoj\ana olmasligi mumkin.
Tur-bu har xil xususiyatlarga ega bo'lib irsiy o'xshashliklarga ega
bo'lgan ma'lum Ьir guruh bo'lib, ular urug'lanish yordamida mahsuldor avlod byeruvchi, ma'lum Ьir hududda yashashga moslashgan va tuproq iqlim sharoitlariga moslashgan individlar guruhiga aytiladi.
4.2 Populyatsiyalar klassifikatsiyasi
Populyatsiyalar ega\lagan hududi va ular o'rtasida aloqaning dara jasiga qarab ular quyidagicha farqlanadi: elementar, (bo'laklangan) ekologik va geografik.
Elementar populyatsiya- guruhlaming eng kichik bo'lagi bo'lib,
Ьir-Ьirini to'ldiradi. Ekologik populyatsiya-aralash elementar populyatsi yalaming ma'lum Ьir muhitda o'zaro bog'\iqligi.
Geografik populyatsiya- ma'lum Ьir joydagi aralash ekologik populyatsiyalaming o'zaro bog'liqligidir.
Populyatsiyalar ko'payishi usullarga qarab panliktiv, klonal va klonal-panliktiv Ьo'ladi. Panliktiv populyatsiyalar jinsiy уо'\ Ьilan ko'payib chetdan changlanishga moyil bo'ladi. Кlonal populyatsiyalar
faqat jinssiz ko'payishga moyil bo'ladilar. Кlonal-panliktiv populyat siyalar Ьir martajinsiy, Ьir martajinssiz уо'\ Ьilan ko'payadilar.
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«Jayron Ekomarkazi» - Ekologiya 11. Rom уоrм ато\. Л tos II кenт z 1: L

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Ekologiya 11. Rom уоrм ато\. Л tos II кenт z 1: L
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Bog'liq Ekologiya 11. Rom уоrм ато. Л tos II кenт z 1 L-fayllar.org13.11. «Jayron Ekomarkazi»
1977 yil may oyida ushbu markaz tashkil etilgan. Noyob hayvon turlari hamda tablat resurslarini saqlashga yo'naltirilgan Markaz aslida Xalqaro tablat va tablat resurslarini saqlash uyushmasi ekomarkazni tashkil etilishiga sababchi bo'lgan. 60 yillarda ko'pgina davlatlar o'zlarining Davlat Qizil kitoblga ega bo'lgan. 70-yillaming o'rtalarida SSSR xukumati respuЫikalardagi 13 ta Jayronning saqlanadigan joylami tashkil etishga qaror qiladi. Bu kabl jayron saqlanadigan joylar asosan Turkmaniston, Ozarboyjon, Gruziya, Tojikiston va O'zbekiston RespuЫikalarida tashkil etilgan.
Boshida, O'zbekistonda tashkil etilgan bu Markazning nomi
«Buxoro Jayronlarini saqlash» markazi deb atalgan edi. Ushbu markazning vazifasi ushbu noyob hayvon turini ko'paytirish, uning Ьiologiyasi hamda populyatsiya resurslaridan foydalanish, samarali natijalarga ega bo'lish, Jayrondan tashqari boshqa kamyob hayvonlar qulon, sayg'ok, Buxoro kiyigi, Prjevalskiy otlari, gippard kabllarni ham ko'paytirishni ham yo'lga qo'yilgan edi. Hozirgi kunda ham bu ekomarkaz jayron, qulon, Prjevalskiy oti, Buxoro tog' qo'yi kaЬi hayvonlar ham qo'riqxonalarda saqlanadi. Ekomarkaz shtatida jami 33 kishi ishlaydi. Shulardan 12 kishi faoliyat ko'rsatadi. «Jayron» ekomarkazi O'zbekistonning janubl-g'arblning Qizilqum cho'li hamda Buxoro viloyatidan 42 km uzoqlikda joylasl1gan. Hudud ikki qismdan iborat:
Birinchsi qism maydoni 5145 gektar, atrofi 2 metrli sim to'r Ьilan o'ralgan. Hudud aniq chegaralar Ьilan ajratilgan: Sharqiy tomonida trassa qatnovi mavjud. G'arЬiy tomoni temir yo'I Ьilan, Janubly tomoni Amu-Buxoro kanali Ьilan hamda shimoliy tomoni Quyimozor kollektori
Ьilan chegaradosh.
Ikkinchi maydoni 27.050 gektarga teng. Ushbu qismning chegarasi yuqori qismdagidek bo'lib, To'da ko'I Ьilan chegaradosh.
Geografik jihatdan Ekomarkaz Janubly Qizilqum qumlari Ьilan
qoplangan Qamabcho'I massivida joylashgan. Hudud murakkab
237
landshaftga ega bo'lib, relefi Qayqaloch tog'i dengiz sathidan 332 m balandlikni tashkil etadi. Hudud tuprog'i proallyuvial hamda allyuvia\ ir. Bu tuproq turlaridan tashqari taqir tuproq turi ham uchraydi. O'rtacha tepalik joylarida allyuvia\ tuproq turlari sal mayda kesakli, loyli tuproqdan iborat. Tuproqning taqirli qismi odatda sho'rxok bo'ladi. Tuproq strukturasi bo'yicha, hudud kulrangnamo to'q jigarrang cho'llardan iborat, bu kaЬi cho'I ko'rinishi hududda 40 % ni tashkil qilsa, taqir va sho'rxok cho'llar 60 % ni tashki\ qiladi.
O'simliklari cho'lga xos bo'lib, saksovul hamda butasimon o'simliklar o'sadi.
Janubly qismda Ьirinchi hudud ko'l komplekslaridan iborat, ushbu
ko'llar turli xil darajada bo'lgan ko'ldan iborat.Foydalaniladigan suvi asosan Amu-Buxoro kanalidan iborat.
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Bog'liq Ekologiya 11. Rom уоrм ато. Л tos II кenт z 1 L-fayllar.orgAtamalar
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Ta'riflar
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Avariya
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Ishdan chiqish, ish vaqtida, harakatlanishda biron Ьi1· mexanizatsiyani, mashinani va h.z. sl1ikastlanisbl
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Ma'lumotlar bazasi
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Ma'lumotlar yig'indisi, Masalan maqollar, tizimlashtirilgan hisoЬ\ar, shunday bo'lish kerakki, ulami elektшn hasoЬ\ash mashinalarida (ЕНМ) topish va ish\ov bve1·ish mumkin bo'lsin.
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Chiqindilar turi
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Bir xildagi fizik-kimyoviy, fizik-mexanik, sanitar- gigiena xususiyatlariga va Ьir xildagi tavsiflash alomatlariga ega bo'lgan chiaindilar vig'indisi.
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Zararli modda
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Xavfsizlik talaЫari buzilganda moddalami inson a'zosiga ta'sir qilib ishlab chiqarishjarohatini keltirish, professional kasallik yoki salomatligiga, ish jarayonida zamonaviy usullar Ьilan Ьilsa bo'ladigan, shuningdek, hozirgi va keyingi hayot avlod\a1·ga o'zgartirish kiritishi mumkin bo'lgan moddalar.
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Ikkilamchi resurslar
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Isblab chiqai·ish va ishlab chiqarislti bo'lmagan muhitda hosil bo'ladigan hamma chiqindilar hajmi
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Ishlatiladigan ikkilamchi resurslar
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Utilizatsiya qilish texnologiyasi bo'lgan chiqindilar hajmi (ikkilamchi resurslar qismi). Ulami xalq xo'jaligi aylanma harakatga t01iish, iamoatni iatisodiv imkonivat Ьilan ta'minlaydi.
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Ikkilamchi moddiy
resurslai·
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Ishlab chiqarish va iste'mol chiqindilarini ilm va
texnikani muayyan rivojlanish bosqichida xalq xo'jaligida potentsial xom ashyo yoki qo'shimcha mahsulot sifatida ishlatish mumkin.
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Chiqindi genezisi
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Chiqindining hosil bo'lishi, kelib chiqishi va paydo bo'lishi.
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lfloslanish
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Yer usti yoki hovuz tubini jismlar (ular qismlari), oziq-ovqat va maishiy tashlamalar, ishlab chiqarishni anchagina katta bo'lakli chiqindilari va
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mramor girok, oq ko'z piroki, skana, oqdum!i orlan, qora grif, botqoq lushi, botqoq burguti, botqoq pustyolkasi, shaxin, drofa-prasotka.
Sudralib yuruvchilar: 2 turda: kulrang echkiemar , afg'on litoringi;
ba/iqlar: 5 turda: Katta Amudaryo «Iopatonos»,.«oq ko'z» (Ьelo-lazka),
« Суковидный шйерех », Turkiston «usachi», Orol shipovkasi.
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Next–Generation Intrusion Detection for Iot evcs: Integrating cnn, lstm, and gru models

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Bog'liq Ekologiya 11. Rom уоrм ато. Л tos II кenт z 1 L-fayllar.org13.10. Qizilqum davlat qo'riqxonasi
Hozirgi kunda qo'riqxonaning umumiy maydoni 10.311 ga ni tashkil etib, shundan 5338 ga sm o'rmon Ьilan qoplangan. Qizilqum Qo'riqxonasi ikki viloyatning Ьir qator rayonlarida joylashgan. Buxoro viloyatining Romiton rayoni 1467 ga hamda Xorazm viloyatining Hazorasp rayoni 8844 maydonni egallagan.
lqlimi. Qo'riqxona iqlimi quruq va keskin kontinental.. Cho'I zonalari o'zining quruq havosi. keskin harorati, yog'ingarchilikning kamligi, yozda yuqori harorati qish haroratining uncha sovuqmasligi,
ko'p bug'lanishi, kam bulutli bo'lishi hamda kuchli issiq shamol (gannsel) bo'lisbl Ьilan farqlanadi.
To'rt оу, yani may-avgustda harorat 41-44° bo'ladi. Qishda esa - 20-21° bo'ladi. Qor juda kam bo'ladi, yani tezyerib ketadi. Birinchi qor yog'ishining nшddati dekabr o'rtalarida bo'lib, tugashi esa fevral o'rtalarida bo'ladi.
Tuprog'i. Qo'riqxona tuprog'ida quyidagi tuproq tiplari bor: 1. Qum-clю'I tuprog'i; 2. Taqir-tuproq; 3. Allyuvial o'tloq; 4. Allyuvial botqoq-o'tloq tuproq; 5. Sho'rxok tuproq. Qum-cho'l tuprog'ida butalardan saksovul, chye1·kez, qandim, bundan tashqa1·i o't o'simliklar
o'sadi. Taqir tuproqda esa yantoq, qora saksovul, taroq o't va yarim butasimonlami uchratish mumkin. Allyuvial o'tloq va allyuvial botqoqli o'tloqlarda yer osti suvi tuprog'i uchraydi.
Florasi. Qizilqum qo'riqxonasida 150 xil yuqori o'simliklar mavjud. O'simliklar quyidagi Ьiologik tipga bo'linadi:
а) Daraxtli to'qaylar guruhi: to'rang'illi to'qaylar, landshaftli jiydalilar va to'qaylar.
Ь) Butasimonlar guruhi: Taroq o'tli to'qay landshafti, tol to'qaylari.
v) Ко'р yillik o'simlikli to'qaylar: yantoqlar landshafti, ajriq, yerimrius, chuchukmiya va boshqalar.
Faunasi. Qo'riqxonadagi Buxoro bug'usi taЬiiy ko'payadi. 2007 yil hisoЬ-kitoЬiga ko'ra qo'riqxonada 110-130 ta gacha bo'lgan turlar yashaydi. Qo'riqxonada ikki turli fazan (Amudaryo va Xiva Fazani),
1.
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1000 dan oshiq tur Ьo'lib, toshqin paytida fazan va boshqa hayvonlar cho'I tomonga yura Ьoshlaydi.
2006 yilda qo'riqxonadagi hayvonlaming soni quyidagicha:
Baliq. 27 tur; Amfiblya (yer-suv hayvoni)-2 tur; Sudralib yuruv hilar 29 tur; Sut emizuvchilar 35 tur qushlar 267 turi mavjud.
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Related Work - Next–Generation Intrusion Detection for Iot evcs: Integrating cnn, lstm, and gru models

Mathematics
2024, 12, 571
8 of 26
Third, a substantial body of work focuses on the application of advanced ML tech-
niques, such as deep neural network (DNN) and LSTM algorithms, to counteract cyber
threats in EVCS [
22
–
25
]. This research spans the development of effective IDS, the chal-
lenges posed by the integration of EVCS with emerging technologies such as 5G, and the use
of techniques such as WCGAN combined with DL classifiers for enhanced attack detection.
Finally, the realm of privacy preservation in EVCS has been addressed through research
into adaptive, differentially private federated learning mechanisms [
26
]. This is crucial
in optimizing privacy while maintaining data utility in federated learning environments,
presenting solutions to balance privacy and model performance.
In contrast to earlier models that primarily focus on Distributed Denial of Service
(DDoS) attacks using datasets like IoT-23, our model’s ability to classify more complex
and diverse attacks such as injection, scanning, malware, and Man-In-The-Middle (MITM)
sets a new benchmark in the field. Additionally, the utilization of the Edge-IIoTset dataset,
which captures real-world traffic, further validates the practical applicability of our model
in real-time IoT environments, a distinct edge over the CIC-IDS2018 dataset used in some
prior studies (Table
1
).
MSC:
68T07
1. Introduction - Next–Generation Intrusion Detection for Iot evcs: Integrating cnn, lstm, and gru models

Mathematics
2024, 12, 571
2 of 26
(GRU) models, evaluating its performance with the “Edge-IIoTset” dataset [
4
], optimizing
it for resource efficiency, and benchmarking it against existing solutions. We evaluate the
ensemble model’s efficacy in enhancing detection accuracy, its performance using compre-
hensive datasets, its feasibility in resource-constrained environments, and its adaptability
to evolving cyber threats.
The major contributions of this study are as follows.
1.
Innovative Ensemble Architecture: We introduce a cutting-edge model merging CNNs,
LSTMs, and GRUs, harnessing their combined strengths for nuanced intrusion detection.
2.
Use of Real-World Datasets: Our approach is validated using authentic datasets,
ensuring practical applicability in IoT EVCS environments.
3.
Advanced Data Processing Techniques: Sophisticated preprocessing techniques are
employed to manage complex IoT security data, enhancing model learning efficiency.
4.
Comprehensive Performance Analysis: Our model outperforms existing IDS solutions
in accuracy and resilience, proven through extensive testing.
5.
Practical Implications and Scalability: Designed for real-world IoT applications, our
model’s scalability and adaptability offer significant cybersecurity advancements.
6.
Benchmark for Future Research: Setting a new standard in IoT security, our work
paves the way for future innovations in ensemble and hybrid model applications.
In this article, we present a groundbreaking approach to intrusion detection tailored
to the unique challenges of IoT-based EVCS. Through significant advancements in IoT
cybersecurity, we demonstrate the effectiveness and viability of an ensemble model in this
vital domain.
In the remainder of this article, we comprehensively explore network IDS (NIDS) in
the context of IoT-based EVCS. Section
2
presents the nuances of IoT in EVCS, covering the
challenges, cybersecurity threats specific to IoT-based EVCS, and the critical role of NIDS
in safeguarding them. It also highlights recent technological and scientific advancements,
setting the stage for future research directions. Section
3
presents a review of related studies,
providing a scholarly context for our research. In Section
4
, we introduce our proposed
NIDS framework for IoT-based EVCS, detailing its architectural overview, the integration
of CNN, LSTM, and GRU models, data preprocessing techniques, evaluation metrics, and
implementation specifics. Section
5
presents our experimental results, including binary, six-
class, and fifteen-class classification outcomes. Next, in Section
6
, we discuss these results
and interprete their implications. Finally, Section
7
concludes this study, summarizing our
contributions and envisioning the impact of our work in the realm of IoT security.
Table 2.
Distribution of Processed Records.
Class

Mathematics
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4.6. Implementation Details
In this study, the implementation of the ensemble model was skillfully executed using
Python for its wide-ranging library support, particularly TensorFlow and Keras for DL,
alongside Scikit-learn for data preprocessing. Pandas and NumPy complement these for
effective data manipulation. Version control was meticulously managed using Git, with
the project’s codebase and version history accessible at the repository
https://github.com/
TATU-hacker/CNN-LSTM-GRU.git
, uploaded on 17 November 2023. The computational
backbone of the project was the Kaggle GPU P100 platform, known for its formidable
processing capabilities, which significantly expedited the training and inference phases.
To address the constraints of IoT environments, the ensemble model was designed
with scalability and efficiency at its core. It can adapt seamlessly to varying data volumes,
a critical feature for IoT applications. To ensure compatibility with IoT devices, known
for their limited processing capabilities, the model was optimized for computational and
memory efficiency and tailored for potential integration with edge computing, thereby
minimizing latency and reducing bandwidth requirements. This thoughtful combination
of software choices and hardware optimization ensures the model’s applicability in the
dynamic and resource-constrained landscape of IoT-based EVCS.
Disclaimer/Publisher’s Note - Next–Generation Intrusion Detection for Iot evcs: Integrating cnn, lstm, and gru models

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Next–Generation Intrusion Detection for Iot evcs: Integrating cnn, lstm, and gru modelsBog'liq mathematics-12-00571Disclaimer/Publisher’s Note:
The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
Document Outline - Introduction
- Network Intrusion Detection in IoT-Based Vehicle Charging Stations
- Related Work
- Proposed NIDS Framework for IoT-Based EVCS
- Experimental Results
- Discussion
- Conclusions
- References
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Figure 3 - Next–Generation Intrusion Detection for Iot evcs: Integrating cnn, lstm, and gru models

Mathematics
2024, 12, 571
13 of 26
on categorical variables, which are predominant in network datasets. Variables such as
HTTP methods, DNS query lengths, and MQTT topics were subjected to label encoding.
This step transformed these categorical strings into a numerical format, a prerequisite for
subsequent ML algorithms.
Following the encoding, the numerical representations underwent one-hot encoding.
This transformation is particularly crucial because it converts categorical integer features
into a binary matrix, thereby mitigating any misleading ordinal relationships that traditional
numerical encoding might imply. One-hot encoding expands the feature space, enabling the
model to better understand and differentiate categorical data. Consequently, the number of
features after the one-hot encoding increased to 119.
Given the expanded feature space, the next step involved streamlining the dataset.
•
The dataset was scrutinized for duplicate records, and such instances were removed
to prevent biases in the model’s learning process.
•
The data was examined for null values, ensuring the integrity and consistency of
the dataset.
•
A novel approach was adopted in which a hash function was employed for each
column to identify identical columns. By comparing the hashes, groups of identical
columns were identified, and all but one in each group were removed. This step is cru-
cial for reducing redundancy in the dataset, thereby enhancing the model’s efficiency.
After the reduction and cleaning processes, the feature count decreased to 99. To fur-
ther refine the dataset, a Chi-squared test was applied. This statistical test is instrumental
in feature selection because it evaluates the independence of each feature against the target
variable. The Chi-squared test scored each of the 99 features, allowing us to identify and
select the top 93 features that exhibited the most significant relationships with the target
variable. This selection was influenced by the intrinsic ability of the CNN component in
the ensemble to discern and use the most pertinent features effectively.
The data processing efforts culminated in the distribution of network traffic as follows,
effectively delineating the varied landscape of normal and anomalous activities within the
dataset (Table
2
).
Figure 2 - Next–Generation Intrusion Detection for Iot evcs: Integrating cnn, lstm, and gru models

Mathematics
2024, 12, 571
11 of 26
The synthesis of CNN, LSTM, and GRU models facilitates a dual-phase analysis–spatial
followed by temporal–each phase tackling different data aspects. CNNs are adept at ex-
tracting spatial features from input data (Figure
3
). They apply a series of learnable filters
to input sequences, capturing essential features such as the signature of packet headers or
the frequency of specific network events. This is particularly crucial in the context of EVCS,
where the myriad of devices and communication protocols introduce a complex and dense
feature space [
12
].
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Bog'liq TURIZM DARSLIKBo‘sh vaqtning mavjudligi. Odamlar sayohatga nafaqat pullarini balki vaqtlarini ham sarflaydilar. Xatto eng yuqori daromadga ega bo‘lgan shaxs ham agar vaqti bo‘lmasa tur sayohatini amalga oshira olmaydi. Rivojlangan jamiyatda vaqtning yetishmasligi (xuddi daromadi yetishmasligi kabi) turizmni chegaralovchi asosiy omildir.
So‘nggi vaqlarda aholining bo‘sh vaqti hajmi oshdi bunga ish kunlarini haftasining qisqartirilishi, uzoqroq ta’til bayram kunlari, maktab ta’tillari, to‘liq ish kunining bo‘lmasligi, nafaqaga chiqish, nodavlat ishlarda band bo‘lishligi va ma’lumot darajasidagi o‘zgarishlar asosiy ta’sir etuvchi omillar bo‘lib xizmat qiladi.
Urbanizatsiya oqibatida qishloq xo‘jaligida band bo‘lgan va kichik aholi yashaydigan punktlardagi aholi soni qisqarmokda. Avval “sayohat qilmaganlar”ning bir qismini tashkil etgan aynan shu ijtimoiy aholi turizm xizmat bozoriga yangi segment sifatida kirib kelmoqda.
Oxirgi yillardagi har xil siyosiy va ijtimoiy-iqtisodiy o‘zgarishlarga qaramay, mamlakatimizdagi turizm sohasi yildan-yilga barqaror ravishda
mahsulotlar va xizmatlar ishlab chiqarishni ko‘paytirib borayotgan xalq xo‘jaligi tarmog‘i bo‘lib qolmoqda. Mamlakatimizdagi turistlik xizmatlar bozorida hozirgi vaqtda asosan an’anaviy shakllar faoliyat ko‘rsatmoqda. Agar yo‘llanmalarning narxi doimiy oshib boradigan bo‘lsa, dam oluvchilar soni ham tegishlicha kamayib boradi. Yuqorida aytilganlardan shu narsa kelib chiqadiki, hozirgi sharoitda O‘zbekistonda eng dolzarb muammolardan biri turistlik sohani qayta tashkil qilish, uning rentabelligini, birinchi navbatda, yuqori texnologiyali komplekslar va tegishli infrastrukturaga ega turistlik markazlarni tashkil qilish evaziga oshirish hisoblanadi.
Tayanch iboralar: Turistlik xizmat bozori, turistlik xizmat bozorini rejalashtirish, turizmning iqtisodiy ko‘rsatkichlari, segmentatsiya, turistlik xizmat bozori kon’yunkturasi, turistlik mahsulotlar va xizmatlar bozori, turistlik xizmat bozoridagi marketing strategiyasi, , infrastruktura, O‘zbekistondagi turistlik xizmat bozori.
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Bog'liq TURIZM DARSLIKOmmaviy turistlik xizmat bozori-katta guruhlardan iborat sayohat qiluvchi asosiy turistlar massasini o‘z ichiga qamrab oladi. Bunday turlarning haridorlari ilgari kelishilgan dasturlar, xizmat va ko‘ngil-ochar tadbirlar, hamda oldindan to‘lab qo‘yilgan xizmatlarning standart jamlanmasiga rozi bo‘lgan kishilardir.
Mashhur turistlik xizmat bozori-bir muncha kamchiliklardan iborat turistlardan tashkil topgan bo‘lib, qimmatroq, inklyuziv turlar, ya’ni yuqori darajadagi servis va qimmatbaho mehmonxonalardan iborat, shuningdek sayohat davrida istaklariga qarab qo‘shimcha xizmatlar uchun haq to‘lanadi.
Individual turistlik xizmat bozori-ushbu holatda turistlar yakka tartibda o‘z marshrutlarini mustaqil belgilaydilar, zaruriy hollarda transport xizmati va mehmonxonada joylashish uchun buyurtmalar beradilar. Bu turlar badavlat kishilar uchun ancha qiziqarli bo‘lib, ularni o‘ziga jalb qiladi. Bundan tashqari o‘z dam olishlarini biznes yoki boshqa bir mashg‘ulot bilan birgalikda davom ettiradilar, bu esa o‘z navbatida oldindan dam olishni tugallash yoki qaytish muddatini belgilashlari kerakligiga yo‘l qo‘ymaydilar.
Sayohatni tashkil qilishning tavsiflanishi bilan bog‘liq bo‘lgan tashkillashtirilgan va tashkillashtirilmagan turizm xizmat bozorlariga ajratiladi.
Harakatlanish uslubiga ko‘ra: piyoda, temir yo‘l, aviatsiya, dengiz va daryo, hamda avtomobil turizm xizmat bozorlariga ajratiladi.
Har bir qayt etib o‘tilgan turistlik xizmat bozorlari xilma-xil bo‘lib: ish bilan bog‘liq turizm o‘z ichga delegatsiyalar yoki alohida shaxslarning kelishuvlarda, majlislarda, ko‘rgazma va yarmarkalarda ishtirok etish maqsadini o‘z ichiga oladi. Boshqa turdagi ish bilan bog‘liq turizm maxsuslashgan guruhlar (kasb, ixtisoslik) bo‘yicha tuzilgan, ammo ularning yo‘naltirilgan maqsadli holdagi aniq bir dastur orqali sayohatlari ularni ish bilan turizmning alohida bir guruhiga ajratish lozimligini bildiradi.
Marketing faoliyatining alohida xususiyati va tarkibiga ko‘ra quyidagi bozorlar ajratiladi:
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Psixologik o‘zini idora qilish segmentatsiyasi

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Bog'liq TURIZM DARSLIKPsixologik o‘zini idora qilish segmentatsiyasi (yo‘nalish tartibini o‘zi belgilaydi) -bunda iste’molchilar yashash tarzi va maqsadli yo‘nalishlari, ya’ni ularning bo‘sh vaqtini belgilash tartiblari, shaxsiy tavsiflari bo‘yicha guruhlarga bo‘linadi. Bunday turistlik harakat me’zonlariga sayohat maqsadi, turistning psixologik sur’ati, mavsumiylik, sayohat turi va uni tashkil qilish, foydalaniladigan transport vositalari, sayohat davrida turar-joy, moliyaviy mablag‘i, konsultantlar va o‘rtadagi dallollarning harakati tur sayohatda qaror qabul qilishga sabab bo‘ladi.
Turizm xizmat bozoridagi ko‘plab segmentlar yana kichik segmentlarga bo‘linadi. Masalan, rekreatsion turizmda - bu da’volanish maqsadidagi va ta’til turizmi hisoblanadi. Ko‘pincha motivatsiyaning aloxida segmentlari bo‘yicha aniq chegaralar o‘tkazish oson emas. Ishga bog‘liq turizm vaziyatni o‘rganish bilan bog‘liq turizmga va sport turizmi esa rekreatsion turizmga bog‘lanib ketadi. Masalan, turizm xizmat
bozorining segmentatsiyasi hayot tarzining me’zoni bo‘yicha quyidagi iste’molchilar guruhlarini ajratadi:
Hayotdan lazzat izlovchi shaxs;
Aniq bir maqsad sari intiluvchi faol shaxs;
Ishbilarmonlar davrasining a’zosi;
“Ko‘k yoqaliklar”;
An’anaviy uy egasi.
Hayotdan lazzat izlovchi shaxs- bir xil ishni bajaruvchi va toza havoda haqiqiy faoliyat turlaridan qoniqish hosil qilishni istovchi shaxs. Baliq ovlashga, ovchilikka, sport bilan shug‘ullanishga va qimmatbaho sport avtomobillariga qiziqadi. Uning daromadi yaxshi, lekin harid bo‘yicha qarorlar u tomonidan qo‘l uchida hal qilinadi. Hayotini uzoqni ko‘zlab rejalashtirmaydi. Sport, sarguzasht va boshqa dasturlarning doimiy muxlisi.
Aniq bir maqsad sari intiluvchan faol shaxs-o‘zida barcha mahoratini va energiyasini kasbi bo‘yicha ko‘tarilish uchun ishga soladi va o‘z ishiga katta qizaqish bilan qaraydi. O‘ziga ishongan, hayotning liberal bo‘lishini xoxlaydi va zamonaviy hisoblanadi. Doimo faol faoliyat, yangi taassurotlar olishga harakat qiladi. Masalan, yaxtada suzish, chetga sayohat qilish, chang‘ida uchish va hokazo. Zamonaviy madaniyatning oxirgi yo‘nalishlaridan, hamda voqea va hodisalardan doimo xabardor bo‘lishi uchun turli jurnallar o‘qiydi. Xabarlar, ko‘ngilochar va sport dasturlarini ko‘radi.
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Experimental Results - Next–Generation Intrusion Detection for Iot evcs: Integrating cnn, lstm, and gru models

5. Experimental Results The essence of empirical validation lies in the rigor of experimental analysis, wherein
theoretical models confront the test of practical performance. This section presents a
detailed exposition of the experimental results derived from the evaluation of the ensemble
model, tailored for intrusion detection within the intricate framework of IoT-based EVCS.
Using a methodological approach, the model was subjected to various tests, ranging from
binary to multifaceted multiclass classifications. Each test was meticulously designed
to probe the model’s predictive prowess across a spectrum of scenarios that mirror the
heterogeneity of potential security breaches in IoT environments.
Binary classification trials were aimed at discerning the presence or absence of intru-
sion attempts, thus laying the groundwork for the model’s capability to distinguish between
normal operations and anomalies. Progressing to more granular levels, six-class and fifteen-
class classification tests were orchestrated to evaluate the model’s ability to identify specific
types of intrusions, each with its unique signature and implications (Table
3
).
Table 3. Model Performance Metrics.
Performance Metric 2 Class 6 Class 15 Class Test Loss
0.0000
0.0532
0.0632
Test Accuracy (%)
100
97.44
96.90
Epoch
6
50
50
Training time (s)
1885.46
14803.63
14719.47
Testing time (s)
42.53
42.20
40.65
5.1. Binary Classification Results
In an era where precision is paramount, the ensemble model demonstrates remarkable
proficiency in binary classification within the specialized sphere of IoT security for EVCS.
The model’s acumen, distilled through only six epochs, yielded a test loss imperceptible
to statistical significance and a test accuracy that epitomizes perfection (Figure
6
). Such
exemplary performance, encapsulated within 1885.46 s of training and only 42.53 s of
inferential judgment, heralds not only the model’s computational efficiency but also its
potential deployment in scenarios where the immediacy of threat detection is paramount.
The ensemble’s binary classification endeavor, delineating “No Intrusion” from “In-
trusion”, culminated in an exemplary synchronization with the ground truth, as evinced by
the congruence of precision, recall, and F1-score, each reaching the pinnacle of 1.00 for both
categories (Table
4
). This zenith of classification metric unanimity, seldom achieved in the
Mathematics 2024, 12, 571
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intricate domain of cybersecurity analytics, highlights the model’s sophisticated capacity
to discern with meticulous exactitude.
Conclusions - Next–Generation Intrusion Detection for Iot evcs: Integrating cnn, lstm, and gru models

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Next–Generation Intrusion Detection for Iot evcs: Integrating cnn, lstm, and gru modelsBog'liq mathematics-12-005717. Conclusions
Our investigation into the domain of cybersecurity for IoT infrastructures, particularly
focusing on EVCS, culminates with a suite of notable contributions that set a new bench-
mark for IDS. The introduction of an innovative ensemble architecture that leverages the
combined strengths of CNN, LSTM, and GRU, represents a leap forward in the detection of
intricate intrusion patterns. The model, rigorously trained and validated against real-world
datasets, demonstrates a superior ability to navigate the complexities of cyber threats
with impressive accuracy. This study not only demonstrates the feasibility of employing
advanced neural network architectures for intrusion detection but also paves the way for
future research in securing IoT ecosystems against sophisticated attacks.
The advanced data processing techniques and comprehensive performance analysis
employed in this study underscore the depth and rigor of our approach. By achieving high
accuracy across binary, six-class, and fifteen-class classifications, the proposed model is
robust and adaptable to several potential security breaches. The practical implications of
this research extend well beyond theoretical exploration, offering scalable solutions for
real-time applications across various IoT scenarios.
As we lay down the groundwork for future explorations, the proposed model stands
as a benchmark in the field and a touchstone for ensuing innovations in cybersecurity,
inviting the scholarly community to engage with our findings, replicate our success, and
venture further into the untapped potential of DL models. Thus, this study does not signal
a terminus but rather a beacon, illuminating the path toward a more secure and resilient
digital future.
Author Contributions:
This manuscript was designed and written by W.K., D.K. and D.T. W.K.
conceived the main idea of this study. D.K. and D.T. wrote the programs and conducted all the
experiments. W.K., D.K. and D.T. contributed to the analysis and discussion of the algorithms and
results. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was supported by the Basic Science Research Program through the National
Research Foundation of Korea (NRF) (no. NRF2022R1F1A1074767).
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