DeepAI AI Chat
Log In Sign Up

A Dependable Hybrid Machine Learning Model for Network Intrusion Detection

12/08/2022
by   Md. Alamin Talukder, et al.
Jahangirnagar University
qut
The University of Queensland
0

Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others significantly. Amid the sophistication and growing number of attacks, dealing with large amounts of data is a recognized issue in the development of anomaly-based NIDS. However, do current models meet the needs of today's networks in terms of required accuracy and dependability? In this research, we propose a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability. Our proposed method ensures efficient pre-processing by combining SMOTE for data balancing and XGBoost for feature selection. We compared our developed method to various machine learning and deep learning algorithms to find a more efficient algorithm to implement in the pipeline. Furthermore, we chose the most effective model for network intrusion based on a set of benchmarked performance analysis criteria. Our method produces excellent results when tested on two datasets, KDDCUP'99 and CIC-MalMem-2022, with an accuracy of 99.99 CIC-MalMem-2022, respectively, and no overfitting or Type-1 and Type-2 issues.

READ FULL TEXT

page 17

page 24

page 25

page 28

page 29

page 31

page 32

page 33

03/19/2020

Hybrid Model For Intrusion Detection Systems

With the increasing number of new attacks on ever growing network traffi...
10/16/2019

A new method for flow-based network intrusion detection using inverse statistical physics

Network Intrusion Detection Systems (NIDS) play an important role as too...
10/21/2020

Deep Q-Network-based Adaptive Alert Threshold Selection Policy for Payment Fraud Systems in Retail Banking

Machine learning models have widely been used in fraud detection systems...
03/31/2021

Anomaly-Based Intrusion Detection by Machine Learning: A Case Study on Probing Attacks to an Institutional Network

Cyber attacks constitute a significant threat to organizations with impl...
05/26/2021

Performance Analysis of a Foreground Segmentation Neural Network Model

In recent years the interest in segmentation has been growing, being use...
03/13/2018

Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection

Deep learning has recently demonstrated state-of-the art performance on ...
11/29/2018

A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics

In this paper we propose a novel machine-learning method for anomaly det...