@inproceedings{a838f6ecb648418eb84425fde8e7ffd6,
title = "An effective approach of detecting DDoS using Artificial Neural Networks",
abstract = "One of the major problems with internet is its security and DDoS is one of the main and devastating security problems that decline the network and resource performance by sending simultaneous request traffic to the target network or system. The attackers even use the legitimate traffic and sometimes even change the traffic information to pass through the detection systems. In this research article, a novel DDoS detection mechanism is proposed based on Artificial Neural Networks principles. This method analyzes the system resources and the network data in order to train the ANN DDoS detection system to detect normal and abnormal traffic. The legitimate traffic is allowed to pass through and abnormal traffic is flagged suspicious to go through detection and defense system. This ANN based DDoS detection method provided good results in detecting DDoS attacks.",
keywords = "ANN, DDoS, Security, Smurf",
author = "Ahanger, \{Tariq Ahamed\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd IEEE International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017 ; Conference date: 22-03-2017 Through 24-03-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/WiSPNET.2017.8299853",
language = "English",
series = "Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "707--711",
booktitle = "Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017",
address = "United States",
}