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Found 10 papers, 6 papers with code
A Survey on XAI for Beyond 5G Security: Technical Aspects, Use Cases, Challenges and Research Directions
With the advent of 5G commercialization, the need for more reliable, faster, and intelligent telecommunication systems are envisaged for the next generation beyond 5G (B5G) radio access technologies.
Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and Defenses
Via an extensive evaluation using three Atari 2600 games, we show that our attacks are effective, as they fully degrade the performance of three different DRL agents (up to 100%, even when the $l_\infty$ bound on the perturbation is as small as 0. 01).
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS
Recently, federated learning (FL) has been subject to both security and privacy attacks posing a dilemmatic challenge on the underlying algorithmic designs: On the one hand, FL is shown to be vulnerable to backdoor attacks that stealthily manipulate the global model output using malicious model updates, and on the other hand, FL is shown vulnerable to inference attacks by a malicious aggregator inferring information about clients’ data from their model updates.
WAFFLE: Watermarking in Federated Learning
In this paper, we present WAFFLE, the first approach to watermark DNN models trained using federated learning.
Extraction of Complex DNN Models: Real Threat or Boogeyman?
However, model extraction attacks can steal the functionality of ML models using the information leaked to clients through the results returned via the API.
Detecting organized eCommerce fraud using scalable categorical clustering
Our approach is based on clustering and aims to group together fraudulent orders placed by the same group of fraudsters.
DAWN: Dynamic Adversarial Watermarking of Neural Networks
Existing watermarking schemes are ineffective against IP theft via model extraction since it is the adversary who trains the surrogate model.
PRADA: Protecting against DNN Model Stealing Attacks
Access to the model can be restricted to be only via well-defined prediction APIs.
Cryptography and Security
DIoT: A Self-learning System for Detecting Compromised IoT Devices
Consequently, DIoT can cope with the emergence of new device types as well as new attacks.
Cryptography and Security
IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT
In this paper, we present IOT SENTINEL, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise.
Cryptography and Security
