A Novel Hybrid Method for Network Anomaly Detection Based on Traffic Prediction and Change Point Detection

by   Mouhammd Alkasassbeh, et al.

In recent years, computer networks have become more and more advanced in terms of size, applications, complexity and level of heterogeneity. Moreover, availability and performance are important issues for end users. New types of cyber-attacks that can affect and damage network performance and availability are constantly emerging and some threats, such as Distributed Denial of Service (DDoS) attacks, can be very dangerous and cannot be easily prevented. In this study, we present a novel hybrid approach to detecting a DDoS attack by means of monitoring abnormal traffic in the network. This approach reads traffic data and from that it is possible to build a model, by means of which future data may be predicted and compared with observed data, in order to detect any abnormal traffic. This approach combines two methods: traffic prediction and changing detection. To the best of our knowledge, such a combination has never been used in this area before. The approach achieved a highly significant accuracy rate of 98.3 attacks are detected and prevented from penetrating the network system.



There are no comments yet.


page 3

page 5


A Proactive Design to Detect Denial of Service Attacks Using SNMP-MIB ICMP Variables

Denial of Service (DOS) attack is one of the most attack that attract th...

Seek and Push: Detecting Large Traffic Aggregates in the Dataplane

High level goals such as bandwidth provisioning, accounting and network ...

Methodology proposal for proactive detection of network anomalies in e-learning system during the COVID-19 scenario

In specific conditions and crisis situations such as the pandemic of cor...

RCNF: Real-time Collaborative Network Forensic Scheme for Evidence Analysis

Network forensic techniques help in tracking different types of cyber at...

Network Phenotyping for Network Traffic Classification and Anomaly Detection

This paper proposes to develop a network phenotyping mechanism based on ...

Detection of Abnormal Vessel Behaviours from AIS data using GeoTrackNet: from the Laboratory to the Ocean

The constant growth of maritime traffic leads to the need of automatic a...

Detecting Network Disruptions At Colocation Facilities

Colocation facilities and Internet eXchange Points (IXPs) provide neutra...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.