Search Results for author:
Found 7 papers, 2 papers with code
Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies
Advancements in reinforcement learning (RL) have inspired new directions in intelligent automation of network defense.
Machine Learning Algorithms for Active Monitoring of High Performance Computing as a Service (HPCaaS) Cloud Environments
This paper explores the viability identifying types of engineering applications running on a cloud infrastructure configured as an HPC platform using privacy preserving features as input to statistical models.
Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers
First, the success rate of backdoor poisoning attacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique.
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection.
Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection
By treating system logs as threads of interleaved "sentences" (event log lines) to train online unsupervised neural network language models, our approach provides an adaptive model of normal network behavior.
Faster Fuzzing: Reinitialization with Deep Neural Models
We improve the performance of the American Fuzzy Lop (AFL) fuzz testing framework by using Generative Adversarial Network (GAN) models to reinitialize the system with novel seed files.
Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams
As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time.
