[Retracted] Lightweight Statistical Approach towards TCP SYN Flood DDoS Attack Detection and Mitigation in SDN Environment

Table 3

Comparative study of various DDoS detection strategies.

Ref. no.	Approach	Method	Benefits	Limitations
[40]	Anomaly-based detection	Entropy, source IP index, and packet rate	Low computational time, low false-positive/negative rate, and high detection throughput	Low accuracy and low adaptability
[41]	Machine learning	DNN	High accuracy	High computational time
[42]	Statistical	Used FGPA	Nine types of DDoS attacks can be detected based on incoming traffic	High detection time, computational complexity, and low flexibility
[43]	Rate limiting	FlowSec	Low computational time and high detection throughput	Low accuracy and high false-positive rate
[44]	Statistical	Switch statistics	Low computational time, High accuracy, and flexibility	High false- positive/negative rate and complexity
[45]	Machine learning	LSTM + CNN	High detection accuracy and minimal false-positive rate	Memory consumption and computational complexity are high
[46]	Hybrid	Long short-term memory + fuzzy logic	High accuracy, flexibility, and minimum damage	Computational complexity