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Multi-Layered Filtration Framework for Efficient Detection of Network Attacks Using Machine Learning.

Muhammad Arsalan Paracha¹, Muhammad Sadiq², Junwei Liang²

¹Critical Infrastructure Protection and Malware Analysis Lab, Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)

|July 14, 2023

Summary

This summary is machine-generated.

This study introduces a multi-layered filtration framework (MLFF) to reduce detection time in intrusion detection systems (IDS) without sacrificing accuracy. The MLFF improves the efficiency of machine learning models for network security.

Keywords:

CIC-IDS2017 anomaly detection intrusion detection system machine learning network attacks network security security information and event management

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Area of Science:

Computer Science
Cybersecurity
Machine Learning

Background:

Information security is critical due to increasing digital data and internet connectivity.
Intrusion detection systems (IDS) are essential for network protection against attacks.
Existing research on anomaly-based IDS (AIDS) often overlooks detection time efficiency.

Purpose of the Study:

To propose a multi-layered filtration framework (MLFF) for feature reduction in IDS.
To enhance the efficiency of intrusion detection systems by minimizing detection time.
To evaluate the impact of feature reduction on both accuracy and speed of IDS.

Main Methods:

Developed a sequential, three-filter multi-layered filtration framework (MLFF) using a statistical approach for feature reduction.
Utilized the CIC-IDS2017 dataset for experimental validation.
Assessed performance using accuracy, precision, recall, F1 score, training time, and detection time.

Main Results:

The MLFF effectively reduces feature sets, leading to decreased detection times.
Accuracy, precision, recall, and F1 scores remain competitive after feature reduction.
Decision tree, random forest, and artificial neural network models demonstrated superior performance with minimal detection times.

Conclusions:

The proposed MLFF is an effective strategy for optimizing IDS performance by reducing detection time.
Feature reduction through statistical methods can significantly improve IDS efficiency without compromising detection accuracy.
The framework provides a valuable approach for developing faster and more effective network security solutions.