| Title | DDoS Attack Detection Using Greedy Algorithm and Frequency Modulation |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Ateş, Çağatay, Özdel, Süleyman, Yıldırım, Metehan, Anarım, Emin |
| Conference Name | 2019 27th Signal Processing and Communications Applications Conference (SIU) |
| Date Published | apr |
| Keywords | anomaly detection, composability, Computer crime, computer network security, DDoS, DDoS attack detection, DDoS attack detection algorithm, destination IP, destination IP addresses, detection phase, distributed denial of service attack, divergence, Dogs, Entropy, Frequency modulation, greedy, greedy algorithm, greedy algorithms, Human Behavior, IP networks, Metrics, network services, probability, Probability distribution, probability distributions, pubcrawl, Resiliency, source IP, source IP addresses |
| Abstract | Distributed Denial of Service (DDoS) attack is one of the major threats to the network services. In this paper, we propose a DDoS attack detection algorithm based on the probability distributions of source IP addresses and destination IP addresses. According to the behavior of source and destination IP addresses during DDoS attack, the distance between these features is calculated and used.It is calculated with using the Greedy algorithm which eliminates some requirements associated with Kullback-Leibler divergence such as having the same rank of the probability distributions. Then frequency modulation is proposed in the detection phase to reduce false alarm rates and to avoid using static threshold. This algorithm is tested on the real data collected from Bogazici University network. |
| DOI | 10.1109/SIU.2019.8806266 |
| Citation Key | ates_ddos_2019 |
DDoS Attack Detection Using Greedy Algorithm and Frequency Modulation