DoS/DDoS Detection for E-Healthcare in Internet of Things

Sami, Iftikhar ul; Ahmad, Maaz Bin; Asif, Muhammad; Ullah, Rafi

doi:10.14569/ijacsa.2018.090140

Cited by 5 publications

(1 citation statement)

References 6 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One such example of this was the Mirai botnet in 2016 which was able to make use of up to 400,000 devices and take down large websites such as Twitter and GitHub [ 39 ]. Due to lack of security on IoT devices, paramount research has been conducted into detecting DoS and DDoS traffic [ 40 , 41 ]. However, all such algorithms lacks the use of ML techniques.…”

Section: Background and Related Workmentioning

confidence: 99%

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

Churcher

Ullah

Ahmad

et al. 2021

Sensors

148

View full text Add to dashboard Cite

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

show abstract

Section: Background and Related Workmentioning

confidence: 99%