Universiti Teknologi Malaysia Institutional Repository

Ping flood attack pattern recognition using a K-means algorithm in an Internet of Things (IoT) network

Stiawan, Deris and Meilinda Eka Suryani, Meilinda Eka Suryani and Susanto, Susanto and Idris, Mohd. Yazid and Aldalaien, Muawya N. and Alsharif, Nizar and Budiarto, Rahmat (2021) Ping flood attack pattern recognition using a K-means algorithm in an Internet of Things (IoT) network. IEEE Access, 9 (NA). pp. 116475-116484. ISSN 2169-3536

[img]
Preview
PDF
1MB

Official URL: http://dx.doi.org/10.1109/ACCESS.2021.3105517

Abstract

Security is the main challenge in Internet of Things (IoT) systems. The devices on the IoT networks are very heterogeneous, many of them have limited resources, and they are connected globally, which makes the IoT much more challenging to secure than other types of networks. Denial of service (DoS) is the most popular method used to attack IoT networks, either by flooding services or crashing services. Intrusion detection system (IDS) is one of countermeasures for DoS attack. Unfortunately, the existing IDSs are still suffering from detection accuracy problem due to difficulty of recognizing features of the DoS attacks. Thus, we need to determine specific features that representing well the traffic attacks, so the IDS will be able to distinguish normal traffic from the attacks. In this work, we investigate ping flood attack pattern recognition on IoT networks. Experiments were conducted using wireless communication with three different scenarios: normal traffic, attack traffic, and combined normal-attack traffic. Each scenario created an associated dataset. The datasets were then grouped into two clusters: normal and attack. The K-Means algorithm was used to produce the clustering results. The average number of packets in the attack cluster was 95 931 packets, and the average in the normal cluster was 4,068 packets. The accuracy level of the clustering results was calculated using a confusion matrix. The accuracy of the clustering using the implemented K-Means algorithm was 99.94%. The rates from the confusion matrix were true negative (98.62%), true positive (100.00%), false negative (0.00%), and false positive (1.38%).

Item Type:Article
Uncontrolled Keywords:Internet, Internet of Things, Internet of Things (IoT), K-Means
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:97434
Deposited By: Widya Wahid
Deposited On:10 Oct 2022 07:54
Last Modified:10 Oct 2022 07:54

Repository Staff Only: item control page