Machine Learning-Based Mechanism for Mitigating DDOS Attacks from Smart Home IoT Networks

EasyChair Preprint 9187

Abstract

In recent decades, Internet of things (IoT) has increasingly become a ubiquitous widespread intelligent technology worldwide. It provides different advanced applications such as business, health, communications, smart home, industry, smart cities, and agriculture. the IoT is considered the main target for internet pirates and hackers looking for sensitive information or/and exploit it to destroy service provider's networks. Most recently, a denial of service (DoS) or distributed denial of service (DDoS) attack is the most common security concern allows attackers to make online systems unavailable to legitimate users. This paper aim to investigate the current architecture and mechanism used in IoT, also we propose a novel DDoS detection and mitigation cybersecurity model by using Machine learning (ML) approach through a set of modern IoT datasets. Finally, it could evaluate the develop model in term of security evolution metrics and could shed light on some future research directions that need further investigation.
Key words: Internet of things (IoT), denial of service (DoS), distributed denial of service (DDoS), and Machine learning (ML).

Keyphrases: DDoS, DoS, IoT, and Machine learning (ML).

@booklet{EasyChair:9187,
  author    = {Izzadeen Alfaqih and Mohammed Ibrahim},
  title     = {Machine Learning-Based Mechanism for Mitigating DDOS Attacks from Smart Home IoT Networks},
  howpublished = {EasyChair Preprint 9187},
  year      = {EasyChair, 2022}}