Attack based DoS attack detection using multiple classifier

by   Mohamed Abushwereb, et al.

One of the most common internet attacks causing significant economic losses in recent years is the Denial of Service (DoS) flooding attack. As a countermeasure, intrusion detection systems equipped with machine learning classification algorithms were developed to detect anomalies in network traffic. These classification algorithms had varying degrees of success, depending on the type of DoS attack used. In this paper, we use an SNMP-MIB dataset from real testbed to explore the most prominent DoS attacks and the chances of their detection based on the classification algorithm used. The results show that most DOS attacks used nowadays can be detected with high accuracy using machine learning classification techniques based on features provided by SNMP-MIB. We also conclude that of all the attacks we studied, the Slowloris attack had the highest detection rate, on the other hand TCP-SYN had the lowest detection rate throughout all classification techniques, despite being one of the most used DoS attacks.


page 5

page 6


Performance Evaluation of Machine Learning Techniques for DoS Detection in Wireless Sensor Network

The nature of Wireless Sensor Networks (WSN) and the widespread of using...

An Effective Deep Learning Based Multi-Class Classification of DoS and DDoS Attack Detection

In the past few years, cybersecurity is becoming very important due to t...

Predict And Prevent DDOS Attacks Using Machine Learning and Statistical Algorithms

A malicious attempt to exhaust a victim's resources to cause it to crash...

Machine Learning-based Early Attack Detection Using Open RAN Intelligent Controller

We design and demonstrate a method for early detection of Denial-of-Serv...

Leveraging Machine Learning Techniques for Windows Ransomware Network Traffic Detection

Ransomware has become a significant global threat with the ransomware-as...

Detecting Network Anomalies using Rule-based machine learning within SNMP-MIB dataset

One of the most effective threats that targeting cybercriminals to limit...

Please sign up or login with your details

Forgot password? Click here to reset