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Found 11 papers, 1 papers with code
Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness
Adversarial training was found to increase the robustness of ML models against these attacks.
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning
Machine Learning (ML) has become ubiquitous, and its deployment in Network Intrusion Detection Systems (NIDS) is inevitable due to its automated nature and high accuracy compared to traditional models in processing and classifying large volumes of data.
Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks
Machine learning and deep learning algorithms can be used to classify encrypted Internet traffic.
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning
The need for robust, secure and private machine learning is an important goal for realizing the full potential of the Internet of Things (IoT).
A GAN-based Approach for Mitigating Inference Attacks in Smart Home Environment
Beyond the notable risk of eavesdropping, intruders can adopt machine learning techniques to infer sensitive information from audio recordings on these devices, resulting in a new dimension of privacy concerns and attack variables to smart home users.
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs
Network security applications, including intrusion detection systems of deep neural networks, are increasing rapidly to make detection task of anomaly activities more accurate and robust.
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey
We then introduce a classification of machine learning in network security applications.
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization
In this paper, we study the resilience of deep learning-based intrusion detection systems against adversarial attacks.
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks
In this paper, we consider a variant of the FNN known as the Self-normalizing Neural Network (SNN) and compare its performance with the FNN for classifying intrusion attacks in an IoT network.
Optimal Slice Allocation in 5G Core Networks
5G network slicing is essential to providing flexible, scalable and on-demand solutions for the vast array of applications in 5G networks.
Networking and Internet Architecture
A Clustering-based Consistency Adaptation Strategy for Distributed SDN Controllers
The consistency and the availability of the distributed state information are governed by an underlying consistency model.
