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Found 9 papers, 3 papers with code
HoneyIoT: Adaptive High-Interaction Honeypot for IoT Devices Through Reinforcement Learning
One effective approach to improving IoT security is to deploy IoT honeypot systems, which can collect attack information and reveal the methods and strategies used by attackers.
Learning to Backdoor Federated Learning
In particular, we propose a general reinforcement learning-based backdoor attack framework where the attacker first trains a (non-myopic) attack policy using a simulator built upon its local data and common knowledge on the FL system, which is then applied during actual FL training.
Federated Unlearning with Knowledge Distillation
Federated Learning (FL) is designed to protect the data privacy of each client during the training process by transmitting only models instead of the original data.
AppQ: Warm-starting App Recommendation Based on View Graphs
In this work, we propose AppQ, a novel app quality grading and recommendation system that extracts inborn features of apps based on app source code.
Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On Autoencoder
One of the most challenging problems in the field of intrusion detection is anomaly detection for discrete event logs.
Time-Window Group-Correlation Support vs. Individual Features: A Detection of Abnormal Users
Autoencoder-based anomaly detection methods have been used in identifying anomalous users from large-scale enterprise logs with the assumption that adversarial activities do not follow past habitual patterns.
Mitigating Backdoor Attacks in Federated Learning
To minimize the pruning influence on test accuracy, we can fine-tune after pruning, and the attack success rate drops to 6. 4%, with only a 1. 7% loss of test accuracy.
No Peeking through My Windows: Conserving Privacy in Personal Drones
Hence, this mechanism detects window objects in every image or frame of a real-time video and masks them chaotically to protect the privacy of people.
Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation
Such popularity, however, may attract attackers to exploit the vulnerabilities of the deployed deep learning models and launch attacks against security-sensitive applications.
