Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection

By Jihyun Kim, Jaehyun Kim, Huong Le Thi Thu, Howon Kim. International Conference on Platform Technology and Service, 2016

584 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/96cb6755a23ac96087a0f0ebe74b8355534c31ed

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

In Computer Networks and Communications

In Signal Processing

Citations in the corpus

Deep Learning Approach for Intelligent Intrusion Detection System (2019, methodology): “The performance of various RNN types were evaluated by [34].”
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends (2018, methodology): “[66] developed a network IntrusionDetection System (IDS) based on an LSTM recursive neural network.”
Machine Learning and Deep Learning Methods for Cybersecurity (2018, methodology): “[66] also use LSTM as their model and the 10% KDD Cup 99 as their dataset.”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “Cycles provide a structure for internal states or memory allowing RNNs to process arbitrary sequences of inputs.”
A Deep Learning Approach to Network Intrusion Detection (2018, background): “[37] claim they have achieved an accuracy of accuracy of 96.”
An Intrusion Detection Model Based on Feature Reduction and Convolutional Neural Networks (2019, methodology): “[24] used long short-term memory structures combined with recurrent neural networks to effectively train the intrusion detection model.”
Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network (2020, methodology): “Kim applied Long Short-TermMemory (LSTM) architecture to RNN and they confirmed that the deep learning approach was effective for IDS through the performance test [22].”
Object detection using YOLO: challenges, architectural successors, datasets and applications (2022, background): “The success stories of deep learning in various domains includes weather forecasting [80], stock market prediction [53], speech recognition [59], object detection [37], character recognition [83], intrusion detection [32], automatic landslide detection [48], time”
A survey of deep learning-based network anomaly detection (2017, methodology): “[40] pointed out that long short term memory (LSTM) was applied to RNN, and they used this concept for modeling the IDS.”
Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, methodology): “[170] to train the IDS model which is based on Long Short Term Memory (LSTM) architecture.”
A Survey of Deep Learning Methods for Cyber Security (2019, unknown): “[139] 47 Intrusion Detection KDDCUP 1999 [57]”
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study (2020, methodology): “(2016) [31] KDD Cup 1999 dataset Detection Rate, FAR, Efficiency 91”
Building an efficient intrusion detection system based on feature selection and ensemble classifier (2019, background): “On the other hand, based on known patterns, misuse detection can effectively distinguish legitimate instances from the malicious ones [46].”