Domain name system (DNS) plays a critical part in the functioning of the Internet. But since DNS queries are sent using UDP, it is vulnerable to Distributed Denial of Service (DDoS) attacks. The attacker can take advantage of this and spoof the source IP address and direct the response towards the victim network. And since the network does not keep track of the number of requests going out and responses coming in, the attacker can flood the network with these unwanted DNS responses. Along with DNS, other protocols are also exploited to perform DDoS. Usage of Network Time Protocol (NTP) is to synchronize clocks on systems. Its monlist command replies with 600 entries of previous traffic records. This response is enormous compared to the request. This functionality is used by the attacker in DDoS. Since these attacks can cause colossal congestion, it is crucial to prevent or mitigate these types of attacks. It is obligatory to discover a way to drop the spoofed packets while entering the network to mitigate this type of attack. Intelligent cybersecurity systems are designed for the detection of these attacks. An Intelligent system has AI and ML algorithms to achieve its function. This paper discusses such intelligent method to detect the attack server from legitimate traffic. This method uses an algorithm that gets activated by excess traffic in the network. The excess traffic is determined by the speed or rate of the requests and responses and their ratio. The algorithm extracts the IP addresses of servers and detects which server is sending more packets than requested or which are not requested. This server can be later blocked using a firewall or Access Control List (ACL). © 2020 - IOS Press and the authors. All rights reserved.