scholarly journals Detecting Distributed Denial of Service Attacks using Machine Learning Models

2021 ◽  
Vol 12 (12) ◽  
Author(s):  
Ebtihal Sameer Alghoson ◽  
Onytra Abbass
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Denial Of Service Attacks ◽  
Distributed Denial Of Service ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Towards a Universal Features Set for IoT Botnet Attacks Detection

2020 ◽  
Author(s):  
Faisal Hussain ◽  
Syed Ghazanfar Abbas ◽  
Ubaid U. Fayyaz ◽  
Ghalib A. Shah ◽  
Abdullah Toqeer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Learning Models ◽  
Botnet Detection ◽  
Detection Techniques ◽  
Machine Learning Model ◽  
Attack Patterns ◽  
Iot Devices ◽  
Machine Learning Models

Abstract The security pitfalls of IoT devices make it easy for the attackers to exploit the IoT devices and make them a part of a botnet. Once hundreds of thousands of IoT devices are compromised and become the part of a botnet, the attackers use this botnet to launch the large and complex distributed denial of service (DDoS) attacks which take down the target websites or services and make them unable to respond the legitimate users. So far, many botnet detection techniques have been proposed but their performance is limited to a specific dataset on which they are trained. This is because the features used to train a machine learning model on one botnet dataset, do not perform well on other datasets due to the diversity of attack patterns. Therefore, in this paper, we propose a universal features set to better detect the botnet attacks regardless of the underlying dataset. The proposed features set manifest preeminent results for detecting the botnet attacks when tested the trained machine learning models over three different botnet attack datasets.

scholarly journals A simulation work for generating a novel dataset to detect distributed denial of service attacks on Vehicular Ad hoc NETwork systems

2021 ◽  
Vol 17 (3) ◽  
pp. 155014772110002
Author(s):  
Fahd A Alhaidari ◽  
Alia Mohammed Alrehan
Keyword(s):  
Machine Learning ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Denial Of Service ◽  
Vehicular Ad Hoc Network ◽  
Traffic Density ◽  
Support Vector ◽  
Denial Of Service Attacks ◽  
Distributed Denial Of Service ◽  
Network Systems

Vehicular Ad hoc NETwork is a promising technology providing important facilities for modern transportation systems. It has garnered much interest from researchers studying the mitigation of attacks including distributed denial of service attacks. Machine learning techniques, which mainly rely on the quality of the datasets used, play a role in detecting many attacks with a high level of accuracy. We conducted a comprehensive literature review and found many limitations on the datasets available for distributed denial of service attacks on Vehicular Ad hoc NETwork including the following: unavailability of online versions, an absence of distributed denial of service traffic, unrepresentative of Vehicular Ad hoc NETwork, and no information regarding the network configurations. Therefore, in this article, we proposed a novel simulation technique to generate a valid dataset called Vehicular Ad hoc NETwork distributed denial of service dataset, which is dedicated to Vehicular Ad hoc NETworks. Vehicular Ad hoc NETwork distributed denial of service dataset holds information on distributed denial of service attack traffic considering Vehicular Ad hoc NETwork architecture, traffic density, attack intensity, and nodes mobility. Well-known simulation tools such as SUMO, OMNeT++, Veins, and INET were used to ensure that all the properties of Vehicular Ad hoc NETwork have been captured. We then compared Vehicular Ad hoc NETwork distributed denial of service dataset with several studies to prove its novelty and evaluated the dataset using several machine learning models. We confirmed that studied models using this dataset achieved high accuracy above 99.5% except support-vector machine that achieved 97.3%.

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