IoT DOS and DDOS Attacks Detection Using an Effective Convolutional Neural Network

Ines Jemal, Omar Cheikhrouhou, Mohamed Haddar

202315 citationsDOI

Abstract

The rapid proliferation of the Internet of Things (IoT) has led to the interconnection of billions of intelligent sensors. However, this interconnection has also introduced significant security challenges. Recently, deep learning has shown promising results in several fields including attacks detection. This paper aims to improve IoT security through the application of deep learning techniques. Specifically, we chose the Convolutional Neural Network as a means to identify and counteract the most severe IoT attacks, such as denial-of-service (DOS) and distributed denial-of-service (DDoS) attacks. The experimental results demonstrate that our CNN is highly effective in identifying DDOS and DOS attacks in the real dataset Bot-IoT, achieving an accuracy rate of 99.920%.

Topics & Concepts

Denial-of-service attackComputer scienceConvolutional neural networkInternet of ThingsDeep learningInterconnectionTrinooApplication layer DDoS attackComputer networkComputer securityArtificial intelligenceThe InternetWorld Wide WebNetwork Security and Intrusion DetectionAdvanced Malware Detection TechniquesInternet Traffic Analysis and Secure E-voting