Detection of dos attacks using naive bayes method based on internet of things (iot)

Internet of Things (IoT) is a technology that is currently on a trend. Interconnecting networks on IoT are useful in the automation process, but it has vulnerabilities to network-based disruptions and attacks; such as Denial of Service (DoS). This study aimed at implementing the Naive Bayes algorith...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2021-03, Vol.1810 (1), p.12013
Main Authors: Setiadi, F F, Kesiman, M W A, Aryanto, K Y E
Format: Article
Language:English
Subjects:
Algorithms
Data logging
Datasets
Denial of service attacks
Format
Internet of Things
Parameter estimation
Physics
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Internet of Things (IoT) is a technology that is currently on a trend. Interconnecting networks on IoT are useful in the automation process, but it has vulnerabilities to network-based disruptions and attacks; such as Denial of Service (DoS). This study aimed at implementing the Naive Bayes algorithm to predict attribute classes using training data-sets from NSLKDD with the KDD99 format and obtained testing-data from the logging process of DoS attacks on IoT-based devices. The advantage of using Naive Bayes is that this method only requires a small amount of training data to determine the estimated parameters needed in the classification process. The results of the conducted research could detect attacks on IoT devices by using the help of snort tools to capture traffic logs. The results from the log were then converted into KDD99 format and processed by the Naive Bayes method. This research uses a training dataset from NSLKDD with KDD99 format which is widely used in various studies and testing data obtained from the IDS log process on the Raspberry Pi 3. The attributes used are 9 attributes namely; service, flag, src_bytes, dst_bytes, srv_serror_rate, same_srv_rate, diff_srv_rate, dst_host_srv_diff_host_rate and dst_host_srv_serror_rate. The results of the research analysis showed an accuracy of 64.02%. These results were smaller than previous results, but some aspects were still different from the actual results because the testing data and training data were taken from two different data-sets, thereby they had different characteristics.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1810/1/012013