DDoS Detection on Internet of Things using Unsupervised Algorithms

Hailyie Tekleselassie,S Krit

doi:10.1051/e3sconf/202129701005

Abstract

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either “abnormal” or “normal” using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

Highlights

The increase of Internet of Things (IoT) devices and computation devices have completed living relaxed and suitable for us due to the debauched and correct computation of our information
For the Autoencoder and Restricted Boltzmann Machine, their subsections consist of plots showing the training and test loss, a table summarizing the performance across the datasets and a detailed discussion of the results
The basic nature of DDoS attacker is to flood the network with a large number of packets and exhaust the network. 4.1 Comparison Result and Analysis The efficiency of planned attack detection system is assessed against five existing works of [Bellingerite al., (2020)], [Almsgiving et al, (2017)], [Yan Naing Soe,2020] (Naeem Firdous Syed,2020) and [Ashrafi et al, (2013)] who have addressed intrusion detection against DDoS attack using KDD dataset. compares the detection accuracy of the proposed work against the existing works

Summary

Introduction

The increase of IoT devices and computation devices have completed living relaxed and suitable for us due to the debauched and correct computation of our information. With the increase of the technologies elaborate, the number of cyberattacks is increasing, using more sophisticated means to incorrectly access sensitive information and to extort money or the already mentioned interruption of services. One such technology is the Internet of Things (IoT) [2]. While traditional DDoS defenses are applied to the target server and are fundamentally homogeneous, IoT-specific DDoS defenses are applied to IoT devices and are more complex, reflecting the heterogeneity of IoT devices In both cases, detection techniques are used to detect abnormal activities in the network or host [4]

Methods

Results

Conclusion