Next Article in Journal
DSFTL: An Efficient FTL for Flash Memory Based Storage Systems
Previous Article in Journal
CoRL: Collaborative Reinforcement Learning-Based MAC Protocol for IoT Networks
Open AccessArticle

Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features

1
Department of Informatics, Kyushu University, Fukuoka 819-0395, Japan
2
Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
3
Department of Advanced Information Technology, Kyushu University, Fukuoka 819-0395, Japan
*
Authors to whom correspondence should be addressed.
Electronics 2020, 9(1), 144; https://doi.org/10.3390/electronics9010144
Received: 8 December 2019 / Revised: 27 December 2019 / Accepted: 8 January 2020 / Published: 11 January 2020
(This article belongs to the Section Networks)
The application of a large number of Internet of Things (IoT) devices makes our life more convenient and industries more efficient. However, it also makes cyber-attacks much easier to occur because so many IoT devices are deployed and most of them do not have enough resources (i.e., computation and storage capacity) to carry out ordinary intrusion detection systems (IDSs). In this study, a lightweight machine learning-based IDS using a new feature selection algorithm is designed and implemented on Raspberry Pi, and its performance is verified using a public dataset collected from an IoT environment. To make the system lightweight, we propose a new algorithm for feature selection, called the correlated-set thresholding on gain-ratio (CST-GR) algorithm, to select really necessary features. Because the feature selection is conducted on three specific kinds of cyber-attacks, the number of selected features can be significantly reduced, which makes the classifiers very small and fast. Thus, our detection system is lightweight enough to be implemented and carried out in a Raspberry Pi system. More importantly, as the really necessary features corresponding to each kind of attack are exploited, good detection performance can be expected. The performance of our proposal is examined in detail with different machine learning algorithms, in order to learn which of them is the best option for our system. The experiment results indicate that the new feature selection algorithm can select only very few features for each kind of attack. Thus, the detection system is lightweight enough to be implemented in the Raspberry Pi environment with almost no sacrifice on detection performance. View Full-Text
Keywords: IoT; DDoS attack; feature selection; IDS; machine learning; Raspberry Pi IoT; DDoS attack; feature selection; IDS; machine learning; Raspberry Pi
Show Figures

Figure 1

MDPI and ACS Style

Soe, Y.N.; Feng, Y.; Santosa, P.I.; Hartanto, R.; Sakurai, K. Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features. Electronics 2020, 9, 144.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop