Deep Learning Based Game-Theoretical Approach to Evade Jamming Attacks. - Sandamal Weerasinghe; Tansu Alpcan; Sarah M. Erfani; Christopher Leckie; Peyam Pourbeik; Jack Riddle (GameSec, 2018)

Detection of Anomalous Communications with SDRs and Unsupervised Adversarial Learning. - Sandamal Weerasinghe; Sarah M. Erfani; Tansu Alpcan; Christopher Leckie; Jack Riddle (LCN, 2018)

Support vector machines resilient against training data integrity attacks. - Sandamal Weerasinghe; Sarah M. Erfani; Tansu Alpcan; Christopher Leckie (Pattern Recognit., 2019)

Novel Defenses Against Data Poisoning in Adversarial Machine Learning. - Sandamal Weerasinghe (2019)

Defending Regression Learners Against Poisoning Attacks. - Sandamal Weerasinghe; Sarah M. Erfani; Tansu Alpcan; Christopher Leckie; Justin Kopacz (CoRR, 2020)

Defending Distributed Classifiers Against Data Poisoning Attacks. - Sandamal Weerasinghe; Tansu Alpcan; Sarah M. Erfani; Christopher Leckie (CoRR, 2020)

Closing the BIG-LID: An Effective Local Intrinsic Dimensionality Defense for Nonlinear Regression Poisoning. - Sandamal Weerasinghe; Tamas Abraham; Tansu Alpcan; Sarah M. Erfani; Christopher Leckie; Benjamin I. P. Rubinstein (IJCAI, 2021)

Local Intrinsic Dimensionality Signals Adversarial Perturbations. - Sandamal Weerasinghe; Tansu Alpcan; Sarah M. Erfani; Christopher Leckie; Benjamin I. P. Rubinstein (CoRR, 2021)

Defending Support Vector Machines Against Data Poisoning Attacks. - Sandamal Weerasinghe; Tansu Alpcan; Sarah M. Erfani; Christopher Leckie (IEEE Trans. Inf. Forensics Secur., 2021)

Local Intrinsic Dimensionality Signals Adversarial Perturbations. - Sandamal Weerasinghe; Tamas Abraham; Tansu Alpcan; Sarah M. Erfani; Christopher Leckie; Benjamin I. P. Rubinstein (CDC, 2022)