Adversary Resistant Deep Neural Networks with an Application to Malware Detection

5 October 2016

Papers citing "Adversary Resistant Deep Neural Networks with an Application to Malware Detection"

21 / 21 papers shown

Title
Frontier AI's Impact on the Cybersecurity Landscape Wenbo Guo Yujin Potter Tianneng Shi Zhun Wang Andy Zhang Dawn Song 57 2 0 07 Apr 2025
A Survey on Poisoning Attacks Against Supervised Machine Learning Wenjun Qiu AAML 36 9 0 05 Feb 2022
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things Yansong Gao Minki Kim Chandra Thapa Sharif Abuadbba Zhi-Li Zhang S. Çamtepe Hyoungshick Kim Surya Nepal 30 59 0 03 Mar 2021
HaS-Nets: A Heal and Select Mechanism to Defend DNNs Against Backdoor Attacks for Data Collection Scenarios Hassan Ali Surya Nepal S. Kanhere S. Jha AAML 27 12 0 14 Dec 2020
Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review Yansong Gao Bao Gia Doan Zhi-Li Zhang Siqi Ma Jiliang Zhang Anmin Fu Surya Nepal Hyoungshick Kim AAML 36 221 0 21 Jul 2020
Odyssey: Creation, Analysis and Detection of Trojan Models Marzieh Edraki Nazmul Karim Nazanin Rahnavard Ajmal Mian M. Shah AAML 28 13 0 16 Jul 2020
Arms Race in Adversarial Malware Detection: A Survey Deqiang Li Qianmu Li Yanfang Ye Shouhuai Xu AAML 24 52 0 24 May 2020
PoisHygiene: Detecting and Mitigating Poisoning Attacks in Neural Networks Junfeng Guo Zelun Kong Cong Liu AAML 27 1 0 24 Mar 2020
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection Aminollah Khormali Ahmed A. Abusnaina Songqing Chen Daehun Nyang Aziz Mohaisen AAML 34 28 0 20 Sep 2019
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Bao Gia Doan Ehsan Abbasnejad Damith C. Ranasinghe AAML 27 66 0 09 Aug 2019
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks David J. Miller Zhen Xiang G. Kesidis AAML 19 35 0 12 Apr 2019
Deep Learning for Anomaly Detection: A Survey Raghavendra Chalapathy Sanjay Chawla AI4TS 41 1,479 0 10 Jan 2019
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach Tsui-Wei Weng Pin-Yu Chen Lam M. Nguyen M. Squillante Ivan Oseledets Luca Daniel AAML 21 30 0 18 Dec 2018
Verification of Recurrent Neural Networks Through Rule Extraction Qinglong Wang Kaixuan Zhang Xue Liu C. Lee Giles AAML 28 18 0 14 Nov 2018
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples Deqiang Li Ramesh Baral Tao Li Han Wang Qianmu Li Shouhuai Xu AAML 28 21 0 18 Sep 2018
Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction Jianguo Zhang Ji Wang Lifang He Zhao Li Philip S. Yu 29 31 0 11 Sep 2018
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 50 226 0 18 Jul 2018
Towards Fast Computation of Certified Robustness for ReLU Networks Tsui-Wei Weng Huan Zhang Hongge Chen Zhao Song Cho-Jui Hsieh Duane S. Boning Inderjit S. Dhillon Luca Daniel AAML 50 686 0 25 Apr 2018
Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction Bin Liang Hongcheng Li Miaoqiang Su Xirong Li Wenchang Shi Xiaofeng Wang AAML 14 215 0 23 May 2017
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems Hossein Hosseini Yize Chen Sreeram Kannan Baosen Zhang Radha Poovendran AAML 30 106 0 13 Mar 2017
Dense Associative Memory is Robust to Adversarial Inputs Dmitry Krotov J. Hopfield AAML 28 111 0 04 Jan 2017