Analisis Serangan DDoS Menggunakan Metode Jaringan Saraf Tiruan
DDoS attack (Distribute Denial of Service) is one of the weapons of choice from hackers because it’s proven it has become threat on the internet worlds. The frequent of DDoS attacks creates a threat to internet users or servers, so that requires the introduction of several new methods that occur, on...
| Main Authors: | , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
LPPM ISB Atma Luhur
2020-10-01
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| Colecção: | Jurnal Sisfokom |
| Assuntos: | |
| Acesso em linha: | http://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/945 |
| _version_ | 1827332797463789568 |
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| author | M. Alfine Ridho Molavi Arman |
| author_facet | M. Alfine Ridho Molavi Arman |
| author_sort | M. Alfine Ridho |
| collection | DOAJ |
| description | DDoS attack (Distribute Denial of Service) is one of the weapons of choice from hackers because it’s proven it has become threat on the internet worlds. The frequent of DDoS attacks creates a threat to internet users or servers, so that requires the introduction of several new methods that occur, one of which can use the IDS (Intrusion Detection System) method. This study took advantage of Neural Network ability to detect DDoS attack or normal based on traffic log processed statistically using Fixed Moving Window. The DDOS attack scheme uses a network topology that has been designed based on the needs and objectives that are found in monitoring network traffic. In each DDoS data and normal consist of 27 traffic log with total numbers of dataset as much as 54 data along with each testing data as much as 10 DDoS data and normal. Data collection was performed using LOIC, HOIC, and DoSHTTP with 300 seconds of traffic monitoring. The result of the Fixed Moving Window processing is the extraction value that will be put in the Neural Networks have 6 input values, one hidden layer with 300 neurons and 2 outputs which consist of a normal dataset and a DDoS dataset. The results of this study showed that Neural Network can detect DDoS and Normal in a good way with accuracy value as much as 95%. |
| first_indexed | 2024-03-07T17:08:10Z |
| format | Article |
| id | doaj.art-c33d5f3b5e8d45e1b38ea6a51a787b10 |
| institution | Directory Open Access Journal |
| issn | 2301-7988 2581-0588 |
| language | English |
| last_indexed | 2024-03-07T17:08:10Z |
| publishDate | 2020-10-01 |
| publisher | LPPM ISB Atma Luhur |
| record_format | Article |
| series | Jurnal Sisfokom |
| spelling | doaj.art-c33d5f3b5e8d45e1b38ea6a51a787b102024-03-03T02:29:42ZengLPPM ISB Atma LuhurJurnal Sisfokom2301-79882581-05882020-10-019337337910.32736/sisfokom.v9i3.945573Analisis Serangan DDoS Menggunakan Metode Jaringan Saraf TiruanM. Alfine Ridho0Molavi Arman1STMIK GI MDPAMIK MDPDDoS attack (Distribute Denial of Service) is one of the weapons of choice from hackers because it’s proven it has become threat on the internet worlds. The frequent of DDoS attacks creates a threat to internet users or servers, so that requires the introduction of several new methods that occur, one of which can use the IDS (Intrusion Detection System) method. This study took advantage of Neural Network ability to detect DDoS attack or normal based on traffic log processed statistically using Fixed Moving Window. The DDOS attack scheme uses a network topology that has been designed based on the needs and objectives that are found in monitoring network traffic. In each DDoS data and normal consist of 27 traffic log with total numbers of dataset as much as 54 data along with each testing data as much as 10 DDoS data and normal. Data collection was performed using LOIC, HOIC, and DoSHTTP with 300 seconds of traffic monitoring. The result of the Fixed Moving Window processing is the extraction value that will be put in the Neural Networks have 6 input values, one hidden layer with 300 neurons and 2 outputs which consist of a normal dataset and a DDoS dataset. The results of this study showed that Neural Network can detect DDoS and Normal in a good way with accuracy value as much as 95%.http://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/945ddosneural netwokfixed moving windowtraffic logintrusion detection system |
| spellingShingle | M. Alfine Ridho Molavi Arman Analisis Serangan DDoS Menggunakan Metode Jaringan Saraf Tiruan Jurnal Sisfokom ddos neural netwok fixed moving window traffic log intrusion detection system |
| title | Analisis Serangan DDoS Menggunakan Metode Jaringan Saraf Tiruan |
| title_full | Analisis Serangan DDoS Menggunakan Metode Jaringan Saraf Tiruan |
| title_fullStr | Analisis Serangan DDoS Menggunakan Metode Jaringan Saraf Tiruan |
| title_full_unstemmed | Analisis Serangan DDoS Menggunakan Metode Jaringan Saraf Tiruan |
| title_short | Analisis Serangan DDoS Menggunakan Metode Jaringan Saraf Tiruan |
| title_sort | analisis serangan ddos menggunakan metode jaringan saraf tiruan |
| topic | ddos neural netwok fixed moving window traffic log intrusion detection system |
| url | http://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/945 |
| work_keys_str_mv | AT malfineridho analisisseranganddosmenggunakanmetodejaringansaraftiruan AT molaviarman analisisseranganddosmenggunakanmetodejaringansaraftiruan |