Evading Classifiers by Morphing in the Dark

22 May 2017

Papers citing "Evading Classifiers by Morphing in the Dark"

20 / 20 papers shown

Title
Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples Giovanni Apruzzese Rodion Vladimirov A.T. Tastemirova Pavel Laskov AAML 40 15 0 04 Jul 2022
MaMaDroid2.0 -- The Holes of Control Flow Graphs Harel Berger Chen Hajaj Enrico Mariconti A. Dvir 36 4 0 28 Feb 2022
Poisoning Attacks and Defenses on Artificial Intelligence: A Survey M. A. Ramírez Song-Kyoo Kim H. A. Hamadi Ernesto Damiani Young-Ji Byon Tae-Yeon Kim C. Cho C. Yeun AAML 25 37 0 21 Feb 2022
WebGraph: Capturing Advertising and Tracking Information Flows for Robust Blocking S. Siby Umar Iqbal Steven Englehardt Zubair Shafiq Carmela Troncoso AAML 25 28 0 23 Jul 2021
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack Rui Shu Tianpei Xia Laurie A. Williams Tim Menzies AAML 32 15 0 23 Nov 2020
Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers Giorgio Severi J. Meyer Scott E. Coull Alina Oprea AAML SILM 29 18 0 02 Mar 2020
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes Keane Lucas Mahmood Sharif Lujo Bauer Michael K. Reiter S. Shintre AAML 31 66 0 19 Dec 2019
Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems Alessandro Erba Riccardo Taormina S. Galelli Marcello Pogliani Michele Carminati S. Zanero Nils Ole Tippenhauer AAML 28 22 0 17 Jul 2019
Effectiveness of Distillation Attack and Countermeasure on Neural Network Watermarking Ziqi Yang Hung Dang E. Chang AAML 27 34 0 14 Jun 2019
On Training Robust PDF Malware Classifiers Yizheng Chen Shiqi Wang Dongdong She Suman Jana AAML 50 68 0 06 Apr 2019
Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment Ziqi Yang E. Chang Zhenkai Liang MLAU 33 60 0 22 Feb 2019
Easy to Fool? Testing the Anti-evasion Capabilities of PDF Malware Scanners Saeed Ehteshamifar Antonio Barresi T. Gross Michael Pradel 14 9 0 17 Jan 2019
Evading classifiers in discrete domains with provable optimality guarantees B. Kulynych Jamie Hayes N. Samarin Carmela Troncoso AAML 21 19 0 25 Oct 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
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks Ambra Demontis Marco Melis Maura Pintor Matthew Jagielski Battista Biggio Alina Oprea Cristina Nita-Rotaru Fabio Roli SILM AAML 19 11 0 08 Sep 2018
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning Tribhuvanesh Orekondy Seong Joon Oh Yang Zhang Bernt Schiele Mario Fritz PICV FedML 359 37 0 15 May 2018
Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning Hyrum S. Anderson Anant Kharkar Bobby Filar David Evans P. Roth AAML 30 207 0 26 Jan 2018
Adversarial Deep Learning for Robust Detection of Binary Encoded Malware Abdullah Al-Dujaili Alex Huang Erik Hemberg Una-May O’Reilly AAML 25 186 0 09 Jan 2018
Differentially Private Federated Learning: A Client Level Perspective Robin C. Geyer T. Klein Moin Nabi FedML 42 1,280 0 20 Dec 2017
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 40 1,390 0 08 Dec 2017