A review of detection approaches for distributed denial of service attacks

Distributed Denial of Service (DDoS) attacks are the intimidation trials on the Internet that depletes the network bandwidth or exhausts the victim’s resources. Researchers have introduced various defense mechanisms (such as attack prevention, traceback, reaction, detection, and characterization) against DDoS attacks, but such attacks are still growing year by year, and the ideal solutions of this problem are eluded so far. In the past, various signature-based and anomaly-based approaches were introduced for the detection of DDoS attacks, but only a few of them have focused on the nature of anomalies. Most of the detection approaches do not provide efficient real-time detection with high detection rate and low faux pas. In this paper, a classification of detection approaches against DDoS attacks has been presented with an aim to go deep insight into the DDoS problem for the beginners in this research area. The detection approaches have been explained along with their pluses and minuses. Further, this review paper includes the different functional classes to which the detection approaches belong to. In the end, a comparison of signature-based, anomaly-based and hybrid detection approaches is depicted in tabular form