Deep learning approach for Network Intrusion Detection in Software Defined Networking

By Tuan A. Tang, L. Mhamdi, D. McLernon, Syed Ali Raza Zaidi, M. Ghogho. 2016

684 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

STRIP: a defence against trojan attacks on deep neural networks (2019, background): “Machine learning (ML) models are increasingly deployed to make decisions on our behalf on various (mission-critical) tasks such as computer vision, disease diagnosis, financial fraud detection, defending against malware and cyber-attacks, access control, surveillance and so on [1]–[3].”
LEMNA: Explaining Deep Learning based Security Applications (2018, background): “So far, researchers have successfully applied deep neural networks to train classifiers for malware classification [2, 16, 21, 48, 68], binary reverse-engineering [15, 52, 71] and network intrusion detection [24, 62], which all achieved an exceptionally high accuracy.”
InSDN: A Novel SDN Intrusion Dataset (2020, result): “For example, some of the previous works [13] and [14] used”
A Deep Learning Approach to Network Intrusion Detection (2018, methodology): “[23] also propose a method to monitor network flow data.”
Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection (2019, background): “The new wave of Artificial Intelligence has been driven by the rapid progress in deep neural network (DNN) technologies, and their wide deployments in domains like self-driving [34], malware classification [43], intrusion detection [39], digital forensics [21], etc.”
BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset (2020, methodology): “In [53], the authors build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD”
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study (2020, methodology): “[21] proposed an intrusion detection system that employs a deep learning technique in software-defined networking.”
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, background): “[110] proposed a DL-based NID scheme for software defined networking (SDN).”
CNN-Based Network Intrusion Detection against Denial-of-Service Attacks (2020, methodology): “[39] suggest IDS in SDN based on Deep Neural Network (DNN) by using NSL-KDD.”
Survey on SDN based network intrusion detection system using machine learning approaches (2018, methodology): “The recent development focuses on leveraging a new network architecture, namely, the software-defined network (SDN) to implement NIDS with machine learning approaches [6].”
Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing (2018, background): “[2] T.”
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, methodology): “Reference [205] compares the deep learning algorithm with three other ML algorithms (i.”
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks (2017, background): “In [19], the authors propose a deep learning based approach using self-taught learning (STL) on the benchmark NSL-KDD dataset in a network intrusion detection system.”
A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “[143] 59 Intrusion Detection for SDN Generated from a Cooja network simulator”