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| Paper Title: | DDOS Attack Detection in Networks Using LSTM And Bi-LSTM Approach |
| Authors Name: | Rakshitha S , Rukshithagowda KB , Smitha Manjunath Naik , Suhana , Shammi L |
| Unique Id: | IJSDR2305027 |
| Published In: | Volume 8 Issue 5, May-2023 |
| Abstract: | As the world is becoming increasingly digitized, the need for protective measures against the attacks becomes more and more efficient. Distributed Denial of Service (DDoS) is one of the attack that is turned into serious threat to the Internet. The automatic detection of DDoS attack packets is one of the key defence tactics. In this paper, we propose DeepLSTMDefense and DeepBiLSTMDefense models using LSTM and Bi-LSTM approach for detecting DDoS attacks based on deep learning. Deep learning approaches enable the automatic separation of high-level features from the low level features, producing effective representation and interface. We create a recurrent deep neural network to recognize the patterns in sequences of network traffic and monitor network assault activities. Experimental results demonstrate better performance of DeepBiLSTMDefense model compared with DeepLSTMDefense model and other conventional machine learning approaches. |
| Keywords: | Deep Learning, DDoS, RNN, LSTM, Bi-LSTM, DeepLSTMDefense, DeepBiLSTMDefense |
| Cite Article: | "DDOS Attack Detection in Networks Using LSTM And Bi-LSTM Approach", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 5, page no.182 - 185, May-2023, Available :http://www.ijsdr.org/papers/IJSDR2305027.pdf |
| Downloads: | 000222060 |
| Publication Details: | Published Paper ID: IJSDR2305027 Registration ID:206065 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: 182 - 185 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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