[HTML][HTML] A systematic literature review of methods and datasets for anomaly-based network intrusion detection
As network techniques rapidly evolve, attacks are becoming increasingly sophisticated and
threatening. Network intrusion detection has been widely accepted as an effective method to …
threatening. Network intrusion detection has been widely accepted as an effective method to …
Anomaly-based intrusion detection systems in iot using deep learning: A systematic literature review
The Internet of Things (IoT) concept has emerged to improve people's lives by providing a
wide range of smart and connected devices and applications in several domains, such as …
wide range of smart and connected devices and applications in several domains, such as …
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
In this paper, we present a survey of deep learning approaches for cyber security intrusion
detection, the datasets used, and a comparative study. Specifically, we provide a review of …
detection, the datasets used, and a comparative study. Specifically, we provide a review of …
InSDN: A novel SDN intrusion dataset
MS Elsayed, NA Le-Khac, AD Jurcut - IEEE access, 2020 - ieeexplore.ieee.org
Software-Defined Network (SDN) has been developed to reduce network complexity
through control and manage the whole network from a centralized location. Today, SDN is …
through control and manage the whole network from a centralized location. Today, SDN is …
LUCID: A practical, lightweight deep learning solution for DDoS attack detection
R Doriguzzi-Corin, S Millar… - … on Network and …, 2020 - ieeexplore.ieee.org
Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …
HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection
W Wang, Y Sheng, J Wang, X Zeng, X Ye… - IEEE …, 2017 - ieeexplore.ieee.org
The development of an anomaly-based intrusion detection system (IDS) is a primary
research direction in the field of intrusion detection. An IDS learns normal and anomalous …
research direction in the field of intrusion detection. An IDS learns normal and anomalous …
Ddosnet: A deep-learning model for detecting network attacks
MS Elsayed, NA Le-Khac, S Dev… - 2020 IEEE 21st …, 2020 - ieeexplore.ieee.org
Software-Defined Networking (SDN) is an emerging paradigm, which evolved in recent
years to address the weaknesses in traditional networks. The significant feature of the SDN …
years to address the weaknesses in traditional networks. The significant feature of the SDN …
Detecting Internet of Things attacks using distributed deep learning
The reliability of Internet of Things (IoT) connected devices is heavily dependent on the
security model employed to protect user data and prevent devices from engaging in …
security model employed to protect user data and prevent devices from engaging in …
The rise of traffic classification in IoT networks: A survey
With the proliferation of the Internet of Things (IoT), the integration and communication of
various objects have become a prevalent practice. The huge growth of IoT devices and …
various objects have become a prevalent practice. The huge growth of IoT devices and …
A survey on machine learning-based misbehavior detection systems for 5g and beyond vehicular networks
A Boualouache, T Engel - IEEE Communications Surveys & …, 2023 - ieeexplore.ieee.org
Advances in Vehicle-to-Everything (V2X) technology and onboard sensors have significantly
accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G …
accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G …