Open Access
ARTICLE
A Novel Framework for DDoS Attacks Detection Using Hybrid LSTM Techniques
Anitha Thangasamy*, Bose Sundan, Logeswari Govindaraj
Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai-600025, Tamilndau, India
* Corresponding Author: Anitha Thangasamy. Email:
Computer Systems Science and Engineering 2023, 45(3), 2553-2567. https://doi.org/10.32604/csse.2023.032078
Received 06 May 2022; Accepted 10 June 2022; Issue published 21 December 2022
Abstract
The recent development of cloud computing offers various services on
demand for organization and individual users, such as storage, shared computing
space, networking, etc. Although Cloud Computing provides various advantages
for users, it remains vulnerable to many types of attacks that attract cyber criminals. Distributed Denial of Service (DDoS) is the most common type of attack
on cloud computing. Consequently, Cloud computing professionals and security
experts have focused on the growth of preventive processes towards DDoS
attacks. Since DDoS attacks have become increasingly widespread, it becomes
difficult for some DDoS attack methods based on individual network flow features to distinguish various types of DDoS attacks. Further, the monitoring pattern
of traffic changes and accurate detection of DDoS attacks are most important and
urgent. In this research work, DDoS attack detection methods based on deep
belief network feature extraction and Hybrid Long Short-Term Memory (LSTM)
model have been proposed with NSL-KDD dataset. In Hybrid LSTM method, the
Particle Swarm Optimization (PSO) technique, which is combined to optimize the
weights of the LSTM neural network, reduces the prediction error. This deep
belief network method is used to extract the features of IP packets, and it identifies
DDoS attacks based on PSO-LSTM model. Moreover, it accurately predicts normal network traffic and detects anomalies resulting from DDoS attacks. The proposed PSO-LSTM architecture outperforms the classification techniques
including standard Support Vector Machine (SVM) and LSTM in terms of attack
detection performance along with the results of the measurement of accuracy,
recall, f-measure, precision.
Keywords
Cite This Article
A. Thangasamy, B. Sundan and L. Govindaraj, "A novel framework for ddos attacks detection using hybrid lstm techniques,"
Computer Systems Science and Engineering, vol. 45, no.3, pp. 2553–2567, 2023.