# Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron
## 1 Introduction  
In today's fast-paced world, where the number of internet-connected devices is increasing and online applications are growing at a rapid pace, information security is becoming an absolute necessity. Since the beginning of the World Wide Web, 1.2 billion websites have been developed [1], and a huge number and variety of online applications are integrated with various web services, such as e-commerce, online banking, online shopping, online education, e-healthcare, and industrial control systems (ICS) for critical infrastructure, etc. Nowadays, cyber attackers are highly skilled and well-equipped to carry out successful attacks on businesses and governments [2]. Cybercrime is big business today, and the volume of stolen information is enormous. There are many different categories of malware [3]. This poses a huge risk to governments, businesses, and consumers around the world. We do not have to go far back in time to remember the massive attack on a bank in Bangladesh, where USD 81 million was reportedly stolen. This is a constant reminderof how effective these attacks can be; the bank's own computers were used to transfer large sums of money. No business is safe, no matter how large. Statistics show that 20% of affected businesses fall into the small business category, 33% into the SME category, and 41% into the large business category. The more widespread the threat, the more important it becomes to be aware of the issues and protect the important information. Eighty-two percent of organizations have been exposed to at least one or more attacks in which data are stolen and used to cripple the victim's services. The organizations that were affected by DDoS attacks reported a 26% drop in performance of their services and 41% reported an outage of the affected services [3]. Figure 1 shows an environment of DDoS attacks. The attacker makes precise imitations of human users' behaviors in an effort to avoid being noticed while conducting the assault. To launch an HTTP-GET flood DDoS assault, the malicious user separates his attack techniques based on rate, admission pattern, etc. Several significant concerns and challenges that have surfaced from recent research have drawn increasing attention in the area of HTTP-GET flood DDoS assault detection. When working in the vicinity of conflicting HTTP-GET flood DDoS attacks [1], numerous challenges arise, that are also only partially addressed or unresolved. DDoS attacks are disciplined, distributed, and remotely organized networks that use deployed computers (also called Bots or Zombies) to send an immense number of uninterrupted and synchronous requests to the victim system(s). DDoS attacks are increasing in strength, regularity, and complexity. Malicious users are constantly evolving their experience, adapting their techniques, and using advanced technologies to launch various DDoS attacks. While there are various solutions to detect, defend against, or mitigate DDoS attacks, malicious users continue to develop new approaches and means to circumvent these countermeasures [3]. DDoS events are still among the biggest threats to the network. Recently, DDoS outbreaks at the application layer of internet servers have become widespread, resulting in huge revenue losses for targets [4]. In TCP/IP layer attacks, the online server is crushed and the number of requests per second is limited. Slowloris, zero-day attacks, and DDoS assaults that take advantage of Apache or Windows vulnerabilities fall under this category [5]. Figure 1: DDoS attack environment. The solutions offered to understand DDoS attacks at the TCP/IP layer capture only a subset of DDoS incidents at the application layer. The resolutions that detect entire types of application-layer attacks are very complicated in formula. One set of tasks in detecting a DDoS outbreak at the TCP/IP layer is the unavailability of landscapes to detect such incidents [6].