Search Results for author:
Found 14 papers, 2 papers with code
MYCROFT: Towards Effective and Efficient External Data Augmentation
Machine learning (ML) models often require large amounts of data to perform well.
SwiftQueue: Optimizing Low-Latency Applications with Swift Packet Queuing
Instead, SwiftQueue uses a custom Transformer, which is well-studied for its expressiveness on sequential patterns, to predict the next packet's latency based on the latencies of recently received ACKs.
ServeFlow: A Fast-Slow Model Architecture for Network Traffic Analysis
ServeFlow is able to make inferences on 76. 3% of flows in under 16ms, which is a speed-up of 40. 5x on the median end-to-end serving latency while increasing the service rate and maintaining similar accuracy.
GRACE: Loss-Resilient Real-Time Video through Neural Codecs
In real-time video communication, retransmitting lost packets over high-latency networks is not viable due to strict latency requirements.
Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning
In this paper, we explore how machine learning (ML) models can (1) help streamline the detection process, (2) improve the potential of using large-scale datasets for censorship detection, and (3) discover new censorship instances and blocking signatures missed by existing heuristic methods.
LEAF: Navigating Concept Drift in Cellular Networks
We then show that frequent model retraining with newly available data is not sufficient to mitigate concept drift, and can even degrade model accuracy further.
An Efficient One-Class SVM for Anomaly Detection in the Internet of Things
Insecure Internet of things (IoT) devices pose significant threats to critical infrastructure and the Internet at large; detecting anomalous behavior from these devices remains of critical importance, but fast, efficient, accurate anomaly detection (also called "novelty detection") for these classes of devices remains elusive.
Traffic Refinery: Cost-Aware Data Representation for Machine Learning on Network Traffic
We demonstrate the benefit of exploring a range of representations of network traffic and present Traffic Refinery, a proof-of-concept implementation that both monitors network traffic at 10 Gbps and transforms traffic in real time to produce a variety of feature representations for machine learning.
Feature Extraction for Novelty Detection in Network Traffic
To facilitate such exploration, we develop a systematic framework, open-source toolkit, and public Python library that makes it both possible and easy to extract and generate features from network traffic and perform and end-to-end evaluation of these representations across most prevalent modern novelty detection models.
A Developer-Friendly Library for Smart Home IoT Privacy-Preserving Traffic Obfuscation
The number and variety of Internet-connected devices have grown enormously in the past few years, presenting new challenges to security and privacy.
Cryptography and Security
Detecting Compressed Cleartext Traffic from Consumer Internet of Things Devices
Data encryption is the primary method of protecting the privacy of consumer device Internet communications from network observers.
Machine Learning DDoS Detection for Consumer Internet of Things Devices
An increasing number of Internet of Things (IoT) devices are connecting to the Internet, yet many of these devices are fundamentally insecure, exposing the Internet to a variety of attacks.
Spying on the Smart Home: Privacy Attacks and Defenses on Encrypted IoT Traffic
The growing market for smart home IoT devices promises new conveniences for consumers while presenting new challenges for preserving privacy within the home.
Cryptography and Security
Closing the Blinds: Four Strategies for Protecting Smart Home Privacy from Network Observers
The growing market for smart home IoT devices promises new conveniences for consumers while presenting novel challenges for preserving privacy within the home.
Cryptography and Security
