A Deep Learning Approach to Network Intrusion Detection

By Nathan Shone, Tran Nguyen Ngoc, Vu Dinh Phai, Qi Shi. 2018

1,484 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tao Huang
2. Ala Al-Fuqaha
3. Farhan Ahmad
4. Renchao Xie
5. Johari Abdullah
6. Mohsen Guizani
7. Jun-feng Xie
8. Sameh Sorour

In Computer Networks and Communications

1. Mohsen Guizani
2. Ala Al-Fuqaha
3. M. Mohammadi
4. Sameh Sorour
5. Tao Huang
6. Renchao Xie
7. Jun-feng Xie
8. F. Yu

In Signal Processing

1. Ala Al-Fuqaha
2. Mohsen Guizani
3. Sameh Sorour
4. F. Yu
5. Helge Janicke
6. Xiaokang Zhou
7. Bo Lang
8. Ke He

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “[166] have used conditional VAE and deep AEs, respectively, for network intrusion detection.”
• Network intrusion detection system: A systematic study of machine learning and deep learning approaches (2020, methodology): “96 Shone et al 97 proposed an IDS based on deep AE and ML technique RF.”
• Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection (2018, methodology): “Compared with other previous models and shallow classificationmethods, such as J48, naive Bayesian, RF, and SVM, our approach achieved a higher accuracy rate particularly under five-category classification, on the NSL-KDD dataset.”
• Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey (2019, methodology): “Parallel computing [66,67] approaches using GPUs [48,68,69] are common solutions.”
• A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, background): “In [209], a novel deep learning-based intrusion detection”
• Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study (2020, methodology): “The deep auto-encoder was used by Shone et al. (2018) [59] for cyber security intrusion detection.”
• Variational LSTM Enhanced Anomaly Detection for Industrial Big Data (2021, background): “environment, which may be reflected in the volume, accuracy, diversity, dynamics, low-frequency attacks, and adaptability issues [11].”
• Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, methodology): “Multiple DL-based NIDS have used AEs in the feature reduction step to produce a compact representation of traf-fic data for highly accurate detection of attacks, as shown in Table II [44], [45], [46].”
• Deep Learning for Time Series Anomaly Detection: A Survey (2022, neutral): “Fan et al. [59] propose an autoencoder-based ensemble method for the analysis of energy time series in buildings and the detection of unexpected consumption patterns and excessive waste.”
• An Adaptive Ensemble Machine Learning Model for Intrusion Detection (2019, background): “All these evaluation metrics [5] are basically derived from the four basic attributes of the confusion matrix depicting the actual and predicted classes in Table 4.”