Applying Long Short-Term Memory Recurrent Neural Network for Intrusion Detection

Althubiti, Sara A.; Nick, William; Mason, Janelle; Yuan, Xiaohong; Esterline, Albert

doi:10.1109/secon.2018.8478898

Cited by 34 publications

(27 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To further validate the effectiveness of our generated adversarial dataset, we applied the original dataset and the adversarial dataset to LSTM [18] and XGBoost [19]. The hyperparameters of the comparison classifiers are shown in Table 3.…”

Section: B Experimental Resultsmentioning

confidence: 99%

Adversarial Examples Detection for XSS Attacks Based on Generative Adversarial Networks

et al. 2020

View full text Add to dashboard Cite

Models based on deep learning are prone to misjudging the results when faced with adversarial examples. In this paper, we propose an MCTS-T algorithm for generating adversarial examples of cross-site scripting (XSS) attacks based on Monte Carlo tree search (MCTS) algorithm. The MCTS algorithm enables the generation model to provide a reward value that reflects the probability of generative examples bypassing the detector. To guarantee the antagonism and feasibility of the generative adversarial examples, the bypassing rules are restricted. The experimental results indicate that the missed detection rate of adversarial examples is significantly improved after the MCTS-T generation algorithm. Additionally, we construct a generative adversarial network (GAN) to optimize the detector and improve the detection rate when dealing with adversarial examples. After several epochs of adversarial training, the accuracy of detecting adversarial examples is significantly improved. INDEX TERMS Network intrusion detection, generative adversarial network, Monte Carlo tree, convolutional neural networks.

show abstract

Section: B Experimental Resultsmentioning

confidence: 99%

Adversarial Examples Detection for XSS Attacks Based on Generative Adversarial Networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Through the performance tests, they have confirmed that the method of deep learning is sufficient for the IDS [34]. Yin Chuan-long et al [35], [36] presented the design and implementation of the detection system based on recurrent NNs. In addition to that, they have investigated the model efficiency in binary and multi-class classifications, the number of neurons and various learning rate effects on the precision.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 91%

“…The number of the iterations has been predefined as 100 epochs, the initialized weights of the network were in the range of (0 -0.05) and the loss function was logarithmic loss [36]. They have discovered that the Adam optimizer is suitable for the LSTM RNN model in the detection of intrusions, and they have concluded the fact that LSTM RNN model utilizing Adam optimizer is capable of constructing a sufficient IDS binary classifier.…”

Section: Repeating Unitmentioning

confidence: 99%

“…They have discovered that the Adam optimizer is suitable for the LSTM RNN model in the detection of intrusions, and they have concluded the fact that LSTM RNN model utilizing Adam optimizer is capable of constructing a sufficient IDS binary classifier. In their future work suggestions, they have proposed applying LSTM to more recent intrusion detection data-sets and assess the efficiency of complicated LSTMs with various optimizers [36]. For more details please see [38].…”

Section: Repeating Unitmentioning

confidence: 99%

See 1 more Smart Citation

Survey on intrusion detection systems based on deep learning

Lateef

Al-Janabi

Al‐Khateeb

2019

PEN

View full text Add to dashboard Cite

Intrusion Detection Systems (IDSs) have a significant role in all networks and information systems in the world to earn the required security guarantee. IDS is one of the solutions used to reduce malicious attacks. As attackers always changing their techniques of attack and find alternative attack methods, IDS must also evolve in response by adopting more sophisticated methods of detection. The huge growth in the data and the significant advances in computer hardware technologies resulted in the new studies existence in the deep learning field, including intrusion detection. Deep learning is sub-field of Machine Learning (ML) methods that are based on learning data representations. In this paper, a detailed survey of various deep learning methods applied in IDSs is given first. Then, a deep learning classification scheme is presented and the main works that have been reported in the deep learning works is summarized. Utilizing this approach, we have provided a taxonomy survey on the available deep architectures and algorithms in these works and classify those algorithms to three classes, which are: discriminative, hybrid and generative. After that, chosen deep learning applications are reviewed in a wide range of fields of intrusion detection. Finally, popular types of datasets and frameworks are discussed.

show abstract

“…Since the early 2010s, various deep ML models have been employed for malware detection, in parallel to classical, "shallow" ML-based algorithms. Malware detection has employed various architectures of deep neural networks (DNNs): multilayer perceptrons (MLPs) [152], recurrent (RNNs) or convolutional neural networks (CNNs) [94], [153], convolutional recurrent neural networks (CRNNs) [154], autoencoders [155] and long short-term memory (LSTM) models [154], [156].…”

Section: Introducing Deep Learningmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Applying Long Short-Term Memory Recurrent Neural Network for Intrusion Detection

Cited by 34 publications

References 6 publications

Adversarial Examples Detection for XSS Attacks Based on Generative Adversarial Networks

Adversarial Examples Detection for XSS Attacks Based on Generative Adversarial Networks

Survey on intrusion detection systems based on deep learning

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Contact Info

Product

Resources

About