The significant growth in the use of the Internet and the rapid development of network
technologies are associated with an increased risk of network attacks. Network attacks refer …

The enormous growth of services and data transmitted over the internet, the bloodstream of
modern civilization, has caused a remarkable increase in cyber attack threats. This fact has …

Traditional machine learning models used for network intrusion detection systems rely on
vast amounts of network traffic data with expertly engineered features. The abundance of …

Malware refers to malicious software developed to penetrate or damage a computer system
without any owner's informed consent. It uses target system susceptibilities, like bugs in …

With the widespread use of IoT applications, malware has become a difficult and
sophisticated threat. Without robust security measures, a massive volume of confidential and …

Internet traffic classification plays a crucial role in Quality of Experience (QoE), Quality of
Services (QoS), intrusion detection, and traffic-trend analyses. While there is no theoretical …

INTRODUCTION: Deep Learning has significantly impacted various domains, including
medical imaging and diagnostics, by enabling accurate classification tasks. This research …

The world of malware is shifting towards using encrypted traffic. While encryption improves
the privacy of users, it brings challenges in the fields of QoS, QoE, and cybersecurity. Recent …

Malicious traffic detection is one of the most important parts of cyber security. The
approaches of using the flow as the detection object are recognized as effective. Benefiting …

Purpose This study aims to explain the state-of-the-art machine learning models that are
used in the intrusion detection problem for human-being understandable and study the …