A survey on deep learning for cybersecurity: Progress, challenges, and opportunities
As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …
A survey on encrypted network traffic analysis applications, techniques, and countermeasures
E Papadogiannaki, S Ioannidis - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
The adoption of network traffic encryption is continually growing. Popular applications use
encryption protocols to secure communications and protect the privacy of users. In addition …
encryption protocols to secure communications and protect the privacy of users. In addition …
Federated-learning-based anomaly detection for IoT security attacks
V Mothukuri, P Khare, RM Parizi… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
The Internet of Things (IoT) is made up of billions of physical devices connected to the
Internet via networks that perform tasks independently with less human intervention. Such …
Internet via networks that perform tasks independently with less human intervention. Such …
Et-bert: A contextualized datagram representation with pre-training transformers for encrypted traffic classification
Encrypted traffic classification requires discriminative and robust traffic representation
captured from content-invisible and imbalanced traffic data for accurate classification, which …
captured from content-invisible and imbalanced traffic data for accurate classification, which …
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 …
[HTML][HTML] A novel hybrid model for intrusion detection systems in SDNs based on CNN and a new regularization technique
MS ElSayed, NA Le-Khac, MA Albahar… - Journal of Network and …, 2021 - Elsevier
Software-defined networking (SDN) is a new networking paradigm that separates the
controller from the network devices ie routers and switches. The centralized architecture of …
controller from the network devices ie routers and switches. The centralized architecture of …
Mobile encrypted traffic classification using deep learning: Experimental evaluation, lessons learned, and challenges
The massive adoption of hand-held devices has led to the explosion of mobile traffic
volumes traversing home and enterprise networks, as well as the Internet. Traffic …
volumes traversing home and enterprise networks, as well as the Internet. Traffic …
Machine-learning-based darknet traffic detection system for IoT applications
The massive modern technical revolution in electronics, cognitive computing, and sensing
has provided critical infrastructure for the development of today's Internet of Things (IoT) for a …
has provided critical infrastructure for the development of today's Internet of Things (IoT) for a …
End-to-end encrypted traffic classification with one-dimensional convolution neural networks
W Wang, M Zhu, J Wang, X Zeng… - 2017 IEEE international …, 2017 - ieeexplore.ieee.org
Traffic classification plays an important and basic role in network management and
cyberspace security. With the widespread use of encryption techniques in network …
cyberspace security. With the widespread use of encryption techniques in network …