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Found 12 papers, 3 papers with code
OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences
Our offline learning model is an adaptation of behavioural cloning with a transformer policy network, where we modify the training process to learn a Q function and a state value function from normal trajectories.
Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks
When such failures arise in wireless communications networks, important services that they use/provide (like anomaly detection) can be left inoperable and can result in a cascade of security problems.
Artificial Intelligence Techniques for Next-Generation Mega Satellite Networks
This article introduces the application of AI techniques for integrated terrestrial satellite networks, particularly massive satellite network communications.
A Game-Theoretic Approach for AI-based Botnet Attack Defence
The new generation of botnets leverages Artificial Intelligent (AI) techniques to conceal the identity of botmasters and the attack intention to avoid detection.
Local Intrinsic Dimensionality Signals Adversarial Perturbations
In this paper, we derive a lower-bound and an upper-bound for the LID value of a perturbed data point and demonstrate that the bounds, in particular the lower-bound, has a positive correlation with the magnitude of the perturbation.
Defending Regression Learners Against Poisoning Attacks
Regression models, which are widely used from engineering applications to financial forecasting, are vulnerable to targeted malicious attacks such as training data poisoning, through which adversaries can manipulate their predictions.
Defending Distributed Classifiers Against Data Poisoning Attacks
We introduce a weighted SVM against such attacks using K-LID as a distinguishing characteristic that de-emphasizes the effect of suspicious data samples on the SVM decision boundary.
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence
Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised learning setting.
Reinforcement Learning for Autonomous Defence in Software-Defined Networking
Despite the successful application of machine learning (ML) in a wide range of domains, adaptability---the very property that makes machine learning desirable---can be exploited by adversaries to contaminate training and evade classification.
Unsupervised Adversarial Anomaly Detection using One-Class Support Vector Machines
Anomaly detection discovers regular patterns in unlabeled data and identifies the non-conforming data points, which in some cases are the result of malicious attacks by adversaries.
Toward the Starting Line: A Systems Engineering Approach to Strong AI
After many hype cycles and lessons from AI history, it is clear that a big conceptual leap is needed for crossing the starting line to kick-start mainstream AGI research.
Large-Scale Strategic Games and Adversarial Machine Learning
Such high-dimensional decision spaces and big data sets lead to computational challenges, relating to efforts in non-linear optimization scaling up to large systems of variables.
