Identification of Mirai Botnet in IoT Environment through Denial-of-Service Attacks for Early Warning System
The development of computing technology in increasing the accessibility and agility of daily activities currently uses the Internet of Things (IoT). Over time, the increasing number of IoT device users impacts access and delivery of valuable data. This is the primary goal of cybercriminals to operat...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Politeknik Negeri Padang
2022-09-01
|
| Series: | JOIV: International Journal on Informatics Visualization |
| Subjects: | |
| Online Access: | https://joiv.org/index.php/joiv/article/view/1262 |
| _version_ | 1811159853308051456 |
|---|---|
| author | Alam Rahmatulloh Galih Muhammad Ramadhan Irfan Darmawan Nur Widiyasono Dita Pramesti |
| author_facet | Alam Rahmatulloh Galih Muhammad Ramadhan Irfan Darmawan Nur Widiyasono Dita Pramesti |
| author_sort | Alam Rahmatulloh |
| collection | DOAJ |
| description | The development of computing technology in increasing the accessibility and agility of daily activities currently uses the Internet of Things (IoT). Over time, the increasing number of IoT device users impacts access and delivery of valuable data. This is the primary goal of cybercriminals to operate malicious software. In addition to the positive impact of using technology, it is also a negative impact that creates new problems in security attacks and cybercrimes. One of the most dangerous cyberattacks in the IoT environment is the Mirai botnet malware. The malware turns the user's device into a botnet to carry out Distributed Denial of Service (DDoS) attacks on other devices, which is undoubtedly very dangerous. Therefore, this study proposes a k-nearest neighbor algorithm to classify Mirai malware-type DDOS attacks on IoT device environments. The malware classification process was carried out using rapid miner machine learning by conducting four experiments using SYN, ACK, UDP, and UDPlain attack types. The classification results from selecting five parameters with the highest activity when the device is attacked. In order for these five parameters to be a reference in the event of a malware attack starting in the IoT environment, the results of the classification have implications for further research. In the future, it can be used as a reference in making an early warning innovative system as an early warning in the event of a Mirai botnet attack. |
| first_indexed | 2024-04-10T05:47:54Z |
| format | Article |
| id | doaj.art-bd2a2a514b104255b8fd288dfeb6d186 |
| institution | Directory Open Access Journal |
| issn | 2549-9610 2549-9904 |
| language | English |
| last_indexed | 2024-04-10T05:47:54Z |
| publishDate | 2022-09-01 |
| publisher | Politeknik Negeri Padang |
| record_format | Article |
| series | JOIV: International Journal on Informatics Visualization |
| spelling | doaj.art-bd2a2a514b104255b8fd288dfeb6d1862023-03-05T10:28:41ZengPoliteknik Negeri PadangJOIV: International Journal on Informatics Visualization2549-96102549-99042022-09-016362362810.30630/joiv.6.3.1262409Identification of Mirai Botnet in IoT Environment through Denial-of-Service Attacks for Early Warning SystemAlam Rahmatulloh0Galih Muhammad Ramadhan1Irfan Darmawan2Nur Widiyasono3Dita Pramesti4Siliwangi University, Tasikmalaya, IndonesiaSiliwangi University, Tasikmalaya, IndonesiaTelkom University, Bandung, IndonesiaSiliwangi University, Tasikmalaya, IndonesiaTelkom University, Bandung, IndonesiaThe development of computing technology in increasing the accessibility and agility of daily activities currently uses the Internet of Things (IoT). Over time, the increasing number of IoT device users impacts access and delivery of valuable data. This is the primary goal of cybercriminals to operate malicious software. In addition to the positive impact of using technology, it is also a negative impact that creates new problems in security attacks and cybercrimes. One of the most dangerous cyberattacks in the IoT environment is the Mirai botnet malware. The malware turns the user's device into a botnet to carry out Distributed Denial of Service (DDoS) attacks on other devices, which is undoubtedly very dangerous. Therefore, this study proposes a k-nearest neighbor algorithm to classify Mirai malware-type DDOS attacks on IoT device environments. The malware classification process was carried out using rapid miner machine learning by conducting four experiments using SYN, ACK, UDP, and UDPlain attack types. The classification results from selecting five parameters with the highest activity when the device is attacked. In order for these five parameters to be a reference in the event of a malware attack starting in the IoT environment, the results of the classification have implications for further research. In the future, it can be used as a reference in making an early warning innovative system as an early warning in the event of a Mirai botnet attack.https://joiv.org/index.php/joiv/article/view/1262classificationddosinternet of thingsk-nearest neighbormirai botnet. |
| spellingShingle | Alam Rahmatulloh Galih Muhammad Ramadhan Irfan Darmawan Nur Widiyasono Dita Pramesti Identification of Mirai Botnet in IoT Environment through Denial-of-Service Attacks for Early Warning System JOIV: International Journal on Informatics Visualization classification ddos internet of things k-nearest neighbor mirai botnet. |
| title | Identification of Mirai Botnet in IoT Environment through Denial-of-Service Attacks for Early Warning System |
| title_full | Identification of Mirai Botnet in IoT Environment through Denial-of-Service Attacks for Early Warning System |
| title_fullStr | Identification of Mirai Botnet in IoT Environment through Denial-of-Service Attacks for Early Warning System |
| title_full_unstemmed | Identification of Mirai Botnet in IoT Environment through Denial-of-Service Attacks for Early Warning System |
| title_short | Identification of Mirai Botnet in IoT Environment through Denial-of-Service Attacks for Early Warning System |
| title_sort | identification of mirai botnet in iot environment through denial of service attacks for early warning system |
| topic | classification ddos internet of things k-nearest neighbor mirai botnet. |
| url | https://joiv.org/index.php/joiv/article/view/1262 |
| work_keys_str_mv | AT alamrahmatulloh identificationofmiraibotnetiniotenvironmentthroughdenialofserviceattacksforearlywarningsystem AT galihmuhammadramadhan identificationofmiraibotnetiniotenvironmentthroughdenialofserviceattacksforearlywarningsystem AT irfandarmawan identificationofmiraibotnetiniotenvironmentthroughdenialofserviceattacksforearlywarningsystem AT nurwidiyasono identificationofmiraibotnetiniotenvironmentthroughdenialofserviceattacksforearlywarningsystem AT ditapramesti identificationofmiraibotnetiniotenvironmentthroughdenialofserviceattacksforearlywarningsystem |