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Found 9 papers, 1 papers with code
Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space
Domain generalization focuses on leveraging knowledge from multiple related domains with ample training data and labels to enhance inference on unseen in-distribution (IN) and out-of-distribution (OOD) domains.
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks
Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data.
MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning
In this paper, we propose an enhanced Membership Inference Attack with the Batch-wise generated Attack Dataset (MIA-BAD), a modification to the MIA approach.
A Novel Approach To User Agent String Parsing For Vulnerability Analysis Using Mutli-Headed Attention
The increasing reliance on the internet has led to the proliferation of a diverse set of web-browsers and operating systems (OSs) capable of browsing the web.
Zero Day Threat Detection Using Metric Learning Autoencoders
The proliferation of zero-day threats (ZDTs) to companies' networks has been immensely costly and requires novel methods to scan traffic for malicious behavior at massive scale.
Anomaly Detection via Federated Learning
Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behavior.
Lateral Movement Detection Using User Behavioral Analysis
Lateral Movement refers to methods by which threat actors gain initial access to a network and then progressively move through said network collecting key data about assets until they reach the ultimate target of their attack.
Zero Day Threat Detection Using Graph and Flow Based Security Telemetry
With this paper, the authors' overarching goal is to provide a novel architecture and training methodology for cyber anomaly detectors that can generalize to multiple IT networks with minimal to no retraining while still maintaining strong performance.
