Analisis Sistem Mitigasi Distributed Denial of Service

Menggunakan Artificial Neural Netwok Dan Convolutional Neural

Network Pada Jaringan OpenFlow

LAPORAN TUGAS AKHIR

Diajukan Untuk Memenuhi

Persyaratan Guna Meraih Gelar Sarjana

Informatika Universitas Muhammadiyah Malang

Hafida Isnaini

201610370311100

Bidang Minat

Jaringan

PROGRAM STUDI INFORMATIKA

FAKULTAS TEKNIK

UNIVERSITAS MUHAMMADIYAH MALANG

2021

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ABSTRAK

Jaringan Softwared Defined Networking (SDN) membantu mengatasi

permasalahan jaringan tradisional yang ada dengan menggunakan controller

sebagai pengendali pusat dari segala aktivitas jaringan yang ada. Akan tetapi

controller yang bertugas sebagai pusat konfigurasi di dalam jaringan SDN memiliki

keamanan yang sangat rentan diserang. Terutama oleh Distributed Denial of

Services (DDoS). Penelitian ini dilakukan untuk mengklasifikasi adanya paket

serangan DDoS pada jaringan SDN. Dengan menggunakan metode Artificial

Neural Network (ANN) dan Convolutional Neural Network (CNN). Hasil dari

klasifikasi tersebut yang akan digunakan untuk melakukan mitigasi terhadap

serangan yang masuk. Dalam penelitian kali ini, penulis mengusulkan dataset

dengan skema fitur baru yang mendukung environtment pada jaringan SDN. Hasil

yang didapatkan dalam penelitian kali ini menunjukkan bahwa ANN memiliki

waktu yang lebih singkat dalam proses deteksi dan mitigasi dibandingkan metode

CNN yaitu hanya 0,7 detik. Hasil akurasi terbesar didapatkan dengan metode CNN

yakni mencapai 96,83%. Sedangkan hasil pengujian untuk CPU usage berbanding

lurus dengan jumlah packet rate per second yang digunakan. Semakin banyak

packet rate yang digunakan, maka beban yang ada dalam controller menjadi

semakin meningkat.

Kata Kunci : Software defined network, ANN, CNN, DDoS, flow mitigasi

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ABSTRACT

Softwared Defined Networking (SDN) helps solve traditional network

problems by using a controller as the central controller of all existing network

activities. However, the controller that acts as a configuration center on the SDN

network has security that is very vulnerable to attack. Mainly by Distributed Denial

of Services (DDoS). This research was conducted to classify the presence of DDoS

attack packets on SDN networks. By using the Artificial Neural Network (ANN)

and Convolutional Neural Network (CNN) methods. The results of this

classification will be used to mitigate incoming attacks. In this research, the authors

propose a dataset with a new feature scheme that supports environment on SDN

networks. The results obtained in this study indicate that ANN has a shorter time in

the detection and mitigation process than the CNN method which is only 0,7

second. The greatest accuracy results were obtained by the CNN method, reaching

96.83%. Meanwhile, the test results for CPU usage are directly proportional to the

number of packet rates per second used. The more packet rates used, the load on

the controller will increase.

Keywords : Kata Kunci : Software defined network, ANN, CNN, DDoS, flow mitigasi

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LEMBAR PERSEMBAHAN

Puji syukur kepada Allah SWT atas rahmat dan karunia-Nya sehingga penulis

dapat menyelesaikan Tugas Akhir ini. Penulis menyampaikan ucapan terima kasih

yang sebesar-besarnya kepada :

1. Orang tua dan keluarga tercinta yang selalu memberi semangat, saran dan doa

yang terbaik untuk saya.

2. Bapak Agus Eko Minarno, S.Kom., M.Kom selaku Dosen Pembimbing 1 dan

Bapak Fauzi Dwi Setiawan Sumadi, S.T., M.CompSc. selaku Dosen Pembimb-

ing 2 yang bersedia meluangkan waktu untuk membimbing, membantu dan

memberikan arahan kepada penulis.

3. Teman tersayang penulis Pongo, Wahyu, Harris dan Ibnu yang tidak pernah

lelah dalam mengingatkan dan memberi semangat untuk segera menyelesaikan

skripsi.

4. Terimakasih kepada rekan skripsi SDN Oxicusa yang telah membantu dan

memberi semangat selama pengerjaan skripsi.

5. Terima kasih kepada teman-teman selama menempuh pendidikan di Universi-

tas Muhammadiyah Malang

6. Seluruh teman-teman yang tidak bisa saya sebutkan satu per satu, terima kasih

banyak atas dukungan, bantuan dan do’a nya.

Malang, 4 Desember 2020

Hafida Isnaini

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KATA PENGANTAR

Dengan memanjatkan puji syukur kehadirat Allah SWT. Atas limpahan

rahmat dan hidayah-Nya sehingga peneliti dapat menyelesaikan tugas akhir yang

berjudul :

“Analisis Sistem Mitigasi Distributed Denial of Service

Menggunakan Artificial Neural Netwok Dan Convolutional Neural

Network Pada Jaringan OpenFlow”

Di dalam tulisan ini disajikan pokok-pokok bahasan yang meliputi penjelasan

terkait penelitian-penelitian terdahulu, metode yang digunakan, deteksi dan

mitigasi untuk serangan DDoS yang ada dalam jaringan SDN dengan menggunakan

metode ANN dan CNN.

Peneliti menyadari sepenuhnya bahwa dalam penulisan tugas akhir ini masih

banyak kekurangan dan keterbatasan. Oleh karena itu peneliti mengharapkan saran

yang membangun agar tulisan ini bermanfaat bagi perkembangan ilmu

pengetahuan.

Malang, 4 Desember 2020

Hafida Isnaini

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Daftar Isi

LEMBAR PERSETUJUAN..................................................................................... i

LEMBAR PENGESAHAN .................................................................................... ii

LEMBAR PERNYATAAN .................................................................................... iii

ABSTRAK ............................................................................................................. iv

ABSTRACT ............................................................................................................ v

LEMBAR PERSEMBAHAN ................................................................................ vi

KATA PENGANTAR ............................................................................................ vii

Daftar Isi............................................................................................................... viii

DAFTAR GAMBAR .............................................................................................. x

DAFTAR TABEL ................................................................................................... xi

BAB I ...................................................................................................................... 1

1.1. Latar Belakang .......................................................................................... 1

1.2. Rumusan Masalah ..................................................................................... 4

1.3. Tujuan Penelitian ...................................................................................... 4

1.4. Batasan Masalah ....................................................................................... 4

1.5. Sistematika Penulisan ............................................................................... 5

BAB II ..................................................................................................................... 6

2.1. Landasan Teori .......................................................................................... 6

2.2. Software Defined Network ....................................................................... 7

2.3. Arsitektur Software Defined Network ...................................................... 8

2.4. Ryu Controller ........................................................................................... 9

2.5. OpenFlow ................................................................................................ 10

2.6. Mininet ..................................................................................................... 11

2.7. Wireshark ................................................................................................. 11

2.8. Scapy ....................................................................................................... 12

2.9. Tcpreplay ................................................................................................. 12

2.10. Distributed Denial of Service.................................................................. 12

2.11. Evaluasi Deep learning ........................................................................... 13

2.12. ANN ........................................................................................................ 14

2.13. CNN ........................................................................................................ 15

BAB III ................................................................................................................. 16

3.1. Alur Penelitian ........................................................................................ 16

3.2. Analisis Masalah ..................................................................................... 17

3.3. Analisis Sistem........................................................................................ 18

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3.4. Perancangan Topologi ............................................................................. 18

3.5. Perancangan Sistem ................................................................................ 20

3.5.1 Perancangan Dataset ............................................................................... 20

3.5.2 Perancangan paket .................................................................................. 24

3.5.3 Perancangan Deep learning..................................................................... 25

3.5.4 Perancangan Mitigasi .............................................................................. 25

3.6. Skenario Pengujian ................................................................................. 27

3.6.1 Menghitung Nilai Akurasi, Precission, Recall, F-1 score ....................... 27

3.6.2 Menghitung Waktu Instalasi Flowrule .................................................... 28

3.6.3 Menghitung Resource Controller ............................................................ 29

3.6.4 Menghitung Packet in .............................................................................. 29

BAB IV ................................................................................................................. 31

4.1 Implementasi ........................................................................................... 31

4.1.1 Implementasi data ................................................................................... 31

4.1.2 Implementasi Perancangan Deep learning .............................................. 33

4.1.3 Implementasi Pengujian .......................................................................... 37

4.2 Hasil Pengujian ....................................................................................... 41

4.2.1 Evaluasi klasifikasi model ANN dan CNN ............................................. 41

4.2.2 Hasil Pengujian Jumlah Packet in .......................................................... 43

4.2.3 Hasil Pengujian Waktu install flowrule .................................................. 44

4.2.4 Hasil Pengujian CPU usage ..................................................................... 45

BAB V ................................................................................................................... 47

5.1 Kesimpulan ................................................................................................ 47

5.2 Saran .......................................................................................................... 47

DAFTAR PUSTAKA ............................................................................................ 49

LAMPIRAN .......................................................................................................... 52

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DAFTAR GAMBAR

Gambar 2.1 Perbedaan jaringan tradisional dan jaringan SDN .............................. 8

Gambar 2.2 Arsitektur SDN. ................................................................................... 9

Gambar 2.3 Confusion matrix .............................................................................. 13

Gambar 2.4 Struktur Artificial Neural Network .. ................................................ 14

Gambar 2.5 Struktur Convolutional Neural Network ........................................... 15

Gambar 3.1 Alur Penelitian ................................................................................... 16

Gambar 3.2 Rancangan Topologi Tree .................................................................. 19

Gambar 3.3 Proses ekstraksi dataset ..................................................................... 23

Gambar 3.4 Alur proses mitigasi ........................................................................... 26

Gambar 4.1 Perintah untuk membuat paket DDoS UDP ...................................... 31

Gambar 4.2 Perintah untuk membuat paket Normal UDP .................................... 31

Gambar 4.3 Perintah untuk membuat topologi tree .............................................. 32

Gambar 4.4 Perintah untuk menjalankan ryu ........................................................ 32

Gambar 4.5 Perintah untuk kirim paket menggunakan tcpreplay ......................... 33

Gambar 4.6 Source code untuk menjalankan ryu controller ................................. 38

Gambar 4.7 Perintah untuk menemukan PID pada ryu-manager ......................... 39

Gambar 4.8 Perintah untuk memonitoring penggunaan CPU ............................... 39

Gambar 4.9 Capture paket serangan menggunakan wireshark ............................ 40

Gambar 4.10 Contoh perintah menjalankan tcpreplay menggunakan 100pps ...... 40

Gambar 4.11 Hasil Output, waktu deteksi dan mitigasi serangan ........................ 41

Gambar 4.12 Flow mitigasi sudah terinstall pada switch ..................................... 41

Gambar 4.13 Diagram batang perbandingan hasil penelitian ............................... 43

Gambar 4.14 Diagram Batang Hasil Pengujian Packet In .................................... 44

Gambar 4.14 Diagram Batang Hasil Pengujian Waktu Instalasi Flowrule ........... 45

Gambar 4.15 Diagram Batang Hasil Pengujian CPU Usage ................................ 46

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DAFTAR TABEL

Tabel 3.1 Kebutuhan Perangkat Keras .................................................................. 18

Tabel 3.2 Kebutuhan Perangkat Lunak ................................................................. 18

Tabel 3.3 List Fitur flow statistic .......................................................................... 21

Tabel 3.4 Keseluruhan List Fitur yang digunakan ................................................ 22

Tabel 3.5 Contoh Dataset yang digunakan ............................................................ 24

Tabel 3.6 Struktur Flowrule .................................................................................. 27

Tabel 3.7 Perhitungan confusion matrix ............................................................... 28

Tabel 3.8 Perbandingan hasil accuracy ................................................................. 28

Tabel 3.9 Perhitungan waktu install flowrule ....................................................... 29

Tabel 3.10 Tabel perhitungan CPU usage ............................................................. 29

Tabel 3.11 Perhitungan paket in ............................................................................ 30

Tabel 4.1 Source code untuk load data .................................................................. 33

Tabel 4.2 Source code preprocessing data ............................................................ 34

Tabel 4.3 Source code untuk to_categorical ......................................................... 34

Tabel 4.4 Source code untuk membangun model ANN ........................................ 35

Tabel 4.5 Source code untuk membangun model CNN ........................................ 35

Tabel 4.6 Source code untuk proses training ........................................................ 36

Tabel 4.7 Source code untuk save model ANN ..................................................... 37

Tabel 4.8 Source code untuk save model CNN ..................................................... 37

Tabel 4.9 Source code untuk drop paket ............................................................... 37

Tabel 4.10 Hasil evaluasi model ANN dan CNN .................................................. 42

Tabel 4.11 Perbandingan hasil accuracy dengan penelitian terdahulu .................. 43

Tabel 4.12 Hasil pengujian packet in .................................................................... 44

Tabel 4.13 Hasil pengujian waktu install flowrule ................................................ 45

Tabel 4.14 Hasil pengujian rata-rata CPU usage .................................................. 46

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DAFTAR LAMPIRAN

Lampiran 1. Script simple_switch_13_flowmitigasi.py…………………………50

Lampiran 2. Script simple_switch_13_flowstatistic.py………………………….53

Lampiran 3. Script cpu_usage.py………………………………………………...61

Lampiran 4. Script generatorcsv.py………………………...….…………………61

Lampiran 5. Script compareresult.py…………………………………………….63

Lampiran 6. Script calculateres.py………………………….……………………64

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