Title

NETWORK INTRUSION DETECTION SYSTEM WITH INCREMENTAL ACTIVE LEARNING

Abstract

Abstract

While Internet usage has increased every year, it has gained momentum in recent years with the global pandemic. Increasing Internet usage has brought cyber threats. Intrusion detection systems have become more important than ever. The performance of these systems is directly proportional to their adaptiveness to rapidly change in attack types. However, desired performance cannot always be achieved due to the lack of labeled data on newly developed attacks and the difficulty of incremental learning with machine learning methods. In this study, we proposed a network intrusion detection system using active learning methods for class incremental learning, which can adapt to the dynamic environment and provide high performance with less labeled data.

Supervisor(s)

Supervisor(s)

MUNTEHA NUR BEDIR TUZUN

Date and Location

Date and Location

2022-09-14 14:30:00

Category

Category

MSc_Thesis