An efficacious method of detecting DDoS using artificial neural networks

Abstract

DDoS has evolved as most common and devastating attack that has been confronted from previous years. Since hundreds and thousands of network replies, mostly RREP work together simultaneously to accomplish DDoS attack. Thus, no information system can tolerate and survive once they confront this ruthless attack and there are many existing intrusion detection systems to prevent and protect system as well as network from DDoS but still DDoS is still complex to detect and perplexing. In this research article, we have developed an IDS based on basics of latency and delays in neural networks. In order to form a multi-layer architecture, every node is kept on surveillance once the detectors are deployed in the network topology and the activities of every single node is tracked by their close hop nodes mutually to ensure their status of survival. Only after all of the information is collected in a table is forwarded for integrated analysis by their selected expert module. The nodes covered in first and second layer of firewall experience some suspected packets or streams as that of DDoS pattern and the core expert module that started right after the 2 nd firewall will take some effective action and invoke the defense module to ensure the safety of the information system. And the nodes which didn't stood against defense module will be isolated first and rebooted later to ensure the normal functionality of the network.

Notice of Retraction: Enhancing Cyber Security in Modern Internet of things (IoT) Using Intrusion Prevention Algorithm for IoT (IPAI)

“…In anomaly based IDS the system behavior is monitored, if it deviates from the normal behavior by a certain threshold, the anomaly is detected. In specification based IDS, certain constraints are set for the operations or protocols [17]. The IDS monitors the functioning according to the constraints.…”

AI-Based Techniques for DDoS Attack Detection in WSN: A Systematic Literature Review

“…From this systematic literature review, it is found that the accuracy rate of DDoS detection using the ANN technique was 99.98% in multiple research (Ahanger, 2018;Khan et al, 2016). In addition, 98.0% and 95.0% accuracy were founded by Saied et al (2016;Aljumah and Ahamad, 2016;Saied et al, 2016). Apart from ANN, the performance of SVM is remarkable which is also more than 95.0% (Al-Issa et al, 2019;Mohd and Singh, 2019;Sharma and Parihar, 2013;Wang and Lin, 2016).…”

“…Many types of research were conducted using the support vector machine, and the accuracy rate was remarkable to detect DDoS attack. Artificial neural network on simulated data has been applied in multiple research (Ahanger, 2018;Aljumah and Ahamad, 2016;Alrajeh and Lloret, 2013;Saied et al, 2016). ANN was capable of detecting DDoS attack with 99.98% accuracy rate.…”

DDoS Attack Preventing and Detection with the Artificial Intelligence Approach

“…Wireshark, Burp Suite, and Nessus. Abdullah Al Jumuah et al [20] they developed a six-stage algorithm and used chaos theory to efficiently detect DDoS attacks. A mirror image of an actual network environment is used to initiate the learning process.…”