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Found 9 papers, 1 papers with code
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks
In this paper, we propose a framework for deep learning-based IDSes addressing new attacks.
Intelligent Reflecting Surfaces for Compute-and-Forward
Compute-and-forward is a promising strategy to tackle interference and obtain high rates between the transmitting users in a wireless network.
Information Theory Networking and Internet Architecture Information Theory
Machine Learning Interpretability Meets TLS Fingerprinting
Protecting users' privacy over the Internet is of great importance; however, it becomes harder and harder to maintain due to the increasing complexity of network protocols and components.
A Variational Auto-Encoder Approach for Image Transmission in Wireless Channel
Recent advancements in information technology and the widespread use of the Internet have led to easier access to data worldwide.
On Multi-Session Website Fingerprinting over TLS Handshake
In this paper, we propose a deep learning approach to generate a multi-label classifier that can predict the websites visited by a user in a certain period.
A Content-Based Deep Intrusion Detection System
In this work, we propose a framework, called deep intrusion detection (DID) system, that uses the pure content of traffic flows in addition to traffic metadata in the learning and detection phases of a passive DNN IDS.
Adaptive Online Learning for Gradient-Based Optimizers
Finally, as a representative application of our proposed algorithm, we study the problem of learning the best regularizer from a family of regularizers for Online Mirror Descent.
Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning
Our proposed scheme, called "Deep Packet," can handle both \emph{traffic characterization} in which the network traffic is categorized into major classes (\eg, FTP and P2P) and application identification in which end-user applications (\eg, BitTorrent and Skype) identification is desired.
Low-Complexity Stochastic Generalized Belief Propagation
However, this reduction can be larger than only one order of magnitude in alphabet size.
