Construct an efficient distributed denial of service attack detection system based on data mining techniques

Dhurgham Kareem Gharkan, Amer A. Abdulrahman


Distributed denial-of-service (DDoS) attack is bluster to network security that purpose at exhausted the networks with malicious traffic. Although several techniques have been designed for DDoS attack detection, intrusion detection system (IDS) It has a great role in protecting the network system and has the ability to collect and analyze data from various network sources to discover any unauthorized access. The goal of IDS is to detect malicious traffic and defend the system against any fraudulent activity or illegal traffic. Therefore, IDS monitors outgoing and incoming network traffic. This paper contains a based intrusion detection system for DDoS attack, and has the ability to detect the attack intelligently, dynamically and periodically by evaluating the set of attackers of the current node with its neighbors. We use dataset named CICDDoS2019 that contains on binary classes benign and DDoS. Performance has evaluated by applying data mining algorithms as well as applying the best features to discover potential attack classes.


CICDDoS2019 dataset; Data mining; DDoS attack; Distributed denial of service; Intrusion detection system

Full Text:




  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics