Security is one of the most challenging conditions for dispersed networks because exclusive threats can damage output overall and can be classified in several ways. At this time, distributed denial-of-service (DDoS) assaults pose the greatest threat to internet security. Rapid identification of communication records for messages referencing DDoS occurrences enables organizations to take preventative action by instantly identifying both positive and negative attitudes in cyberspace. This research suggests a method for locating such assaults. The method includes the use of deep learning models that had been trained on the present dataset using Bi Long Short-Term Memory (Bi LSTM). Our model beats more established machine learning techniques, according to the experimental data.The method includes the use of deep learning models that had been trained on the present dataset using Bi Long Short-Term Memory (Bi LSTM). Our model beats more established machine learning techniques, according to the experimental data. Experimental results showed that the proposed technique could achieve an accuracy of 96.7%, making it the best option for use in the detection of breaches applications.
Keywords
Long Short Term Memory, DDoS attack, SVM, Random Forest.
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Author(s) thanks to Dr. Prashanthkumar Shukla for this research completion and Data validation support.
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Jeevan Pradeep K
Jeevan Pradeep K
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India.
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Cite this article
Jeevan Pradeep K and Prashanthkumar Shukla, “DDOS Attack Packet Detection and Prevention On a Large-Scale Network Utilising the Bi-Directional Long Short Term Memory Network”, Journal of Machine and Computing, pp. 105-113, January 2024. doi: 10.53759/7669/jmc202404011.
