Comparing Computational Intelligent Techniques for DDOS Attacks detection

Abstract

The Internet is often targeted by the Distributed Denial of Service (DDOS) Attacks that deliberately utilize resources and bandwidth to prohibit access to potential users. The attack possibility is that the packets are filled massively. A DOS attack is launched by a single source, while a DDOS attack is originated from numerous resources. DDoS attacks are not capable of stealing website user’s information. The prime motive of the DDoS attacks is to devastate the website resources. Distributed Denial of Service (DDoS) attacks are disruptive to internet access on the Network. The attitude of the customer to get fast and reliable services can be seriously influenced by DDoS attackers. In the digital era of today, cases of DDoS attacks have also been exceeded in the wireless, smartphone, and IoT attacks with catastrophic implications. We will soon be experiencing the 5G smartphone rebellion, but there are indications that 5G networks too are becoming victim to DDoS attacks but the existing DDoS detection and protection strategies are not able to handle DDOS attacks successfully therefore, thorough research on implementing computational intelligent strategies in the detection and defense techniques has been performed to recognize, mitigate, and avoid these attacks. But the most suitable and efficient defense strategy for these attacks remains an issue to be addressed in the future. This review article concentrates on the most prevalent methods of detection and defense against DDoS attacks that incorporate computational intelligence. The analysis describes attacks and explains them. The key factors relevant to the detection of DDOS attacks are included in this research like methods, tools, and detection accuracy. Finally, various challenges attached to the detection of DDOS attacks and research gaps are depicted.