## 1 Introduction  
The Internet and its applications are constantly developing and are an essential element of every person's daily life. Due to this overwhelming need, research has expanded beyond just connecting computers to the Internet. Indeed, the Internet of Things (IoT) allows Machine-to-Machine (M2M) interactions to coexist with Human-to-Human (H2H) interactions over the same communication network. The IoT is a disruptive technology that has the potential to alter both the physical and digital aspects of our lives. This technology describes a specific kind of network that links M2M objects and gadgets to the Internet in order to facilitate information sharing and smart recognition [1]. The total number of IoT connections has grown from 6 billion in 2015 and is expected to reach 27 billion in 2025 [2]. GSMA Intelligence forecasts IoT connections to reach more than 38 billion by 2030, with the enterprise segment accounting for more than 60% of the total [3]. After a slowdown in enterprise progress caused by the pandemic and chip shortages, growth is returning to previous levels. In 2030, smart buildings and smart homes will be the largest verticals for IoT connections, while smart manufacturing is forecast to grow at a Compound Annual Growth Rate (CAGR) of 20% between 2023 and 2030 [3]. Long-Term Evolution for Machines (LTE-M) is a type of cellular network specifically designed for IoT and M2M devices with a limited bandwidth of 1.4 MHz. M2M devices transmit compact data packets at varying intervals. However, these devices differ from traditional Human-to-Human (H2H) communications in terms of their distinctiveness and functionality. M2M devices transmit their data payloads in synchronized bursts, creating a phenomenon like coordinated storms [4]. The synchronization behavior described above has led to various issues, particularly in light of the increasing prevalence of M2M devices. These issues encompass network saturation, access baring, resource depletion, and the inefficient utilization of the bandwidth. Consequently, these challenges have prompted extensive research efforts within the academic community to develop potential solutions. In addition to natural and human-induced catastrophes such as tsunamis, acts of terrorism, and wars, there is a significant challenge posed by the simultaneous transmission of alerts from various devices. This situation has a detrimental impact on both H2H and M2M communication traffic. Distributed Denial of Service (DDoS) attacks have been one of the security gaps that most threaten services, applications, and information access. According to Forbes [5], there are about 1.09 billion websites on the Internet in 2024 [6]. In an attempt to stay undetected while carrying out attacks, the attacker precisely mimics the actions of users. The Malicious user divides their attack methods according to rate, admission pattern, etc., in order to initiate a HTTP-GET flood DDoS attack. The detection of HTTP-GET flood DDoS attacks has gained more attention because of a number of important issues and difficulties that have emerged from recent study. Working near conflicting HTTP-GET flood DDoS attacks presents a number of difficulties that are either unresolved or only partially handled [7]. DDoS assaults are organized, dispersed, and remotely managed networks that employ deployed computers, sometimes known as bots, to deliver a massive volume of synchronous, continuous requests to the target server. The frequency, intensity, and sophistication of DDoS attacks are all rising. In order to conduct different DDoS attacks, malicious users are continuously changing their experience, modifying their tactics, and utilizing cutting edge technologies. Even though there are a number of ways to identify, stop, or lessen DDoS attacks, malicious people may always come up with new ways to get around existing defenses [8]. DDoS attacks continue to rank among the network's greatest dangers.