A Deep Learning Approach for Network Intrusion Detection System

By Ahmad Y. Javaid, Quamar Niyaz, Weiqing Sun, Mansoor Alam. EAI Endorsed Trans. Security Safety, 2016

1,240 citations recorded by the source corpus.

A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organizations. However, many challenges arise while developing a flexible and efficient NIDS for unforeseen and unpredictable attacks. We propose a deep learning based approach for developing such an efficient and flexible NIDS. We use Self-taught Learning (STL), a deep learning based technique, on NSL-KDD - a benchmark dataset for network intrusion. We present the performance of our approach and compare it with a few previous work. Compared metrics include accuracy, precision, recall, and f-measure values.

Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/25deaf0adeb9ea455a2f2d211ee86890eb64b69b

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Citations in the corpus

Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, methodology): “[106] proposed a flow-based, Self-taught Learning (STL) [107] approach to detect network intrusion.”
Deep Learning Approach for Intelligent Intrusion Detection System (2019, methodology): “Two level approach proposed for IDS in which the ﬁrst level extracts the optimal features using sparse autoencoder in an unsupervised way and classiﬁed using softmax regression [33].”
Survey on SDN based network intrusion detection system using machine learning approaches (2018, methodology): “[15] implemented flexible NIDS using self-taught learning on NSL-KDD data for network intrusion and developed a sparse encoder for further reduction.”
Autoencoder-based feature learning for cyber security applications (2017, methodology): “The proposed models vary on the structure of the model used, the application purpose and motivation behind the chosen strategy [20, 21, 22, 23].”
HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection (2018, methodology): “[6] studied the intrusion detection method on the NSL-KDD dataset using deep belief networks.”
A Deep Learning Approach to Network Intrusion Detection (2018, background): “[20] propose a deep learning based approach to building an effective and flexible NIDS.”
CNN-Based Network Intrusion Detection against Denial-of-Service Attacks (2020, methodology): “[38] employ Self-Taught Learning (STL) algorithm to develop an IDS.”
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks (2017, methodology): “In [18], the authors utilize a deep learning approach based on a deep neural network for flow-based anomaly detection, and the experimental results show that deep learning can be applied for anomaly detection in software defined networks.”
Enhanced Network Anomaly Detection Based on Deep Neural Networks (2018, methodology): “[30] developed a ‘‘Self-taught Learning’’ classification mechanism by combining encoder layers of Sparse Autoencoder (for hidden features extraction) with Softmax regression (for probability estimates of classes) to perform classification on NSLKDD.”
A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN) (2016, methodology): “In [26], we implemented a deep learning based NIDS using NSL-KDD dataset.”
Smart Anomaly Detection in Sensor Systems (2020, methodology): “Semi-supervised [55],[56],[57],[58] CNN [68],[69]”
Deep Learning for Time Series Anomaly Detection: A Survey (2022, neutral): “Consequently, it can help operators identify targeted cyber-attacks and make better decisions to ensure grid reliability.”