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Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine learning (ML) pipelines.
An explainable artificial intelligence system for small-unit tactical behavior
Michael Van Lent, William Fisher, and Michael Mancuso · 2004
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Adversarial learning
Daniel Lowd and Christopher Meek · 2005
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Using thematic analysis in psychology
Virginia Braun and Victoria Clarke · 2006
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Discoverer: Automatic Protocol Reverse Engineering from Network Traces
Weidong Cui, Jayanthkumar Kannan, and Helen J Wang · 2007
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Learning and classification of malware behavior
Konrad Rieck, Thorsten Holz, Carsten Willems, Patrick Düssel, and Pavel Laskov · 2008
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Prospex: Protocol specification extraction
Paolo Milani Comparetti, Gilbert Wondracek, Christopher Kruegel, and Engin Kirda · 2009
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Inference and analysis of formal models of botnet command and control protocols
Chia Yuan Cho, Domagoj Babi ć, Eui Chul Richard Shin, and Dawn Song · 2010
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Analyzing computer security: A threat/vulnerability/countermeasure approach
Charles P Pfleeger and Shari Lawrence Pfleeger · 2012
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The “Clever Hans Phenomenon” revisited
Laasya Samhita and Hans J Gross · 2013
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Drebin: Effective and explainable detection of android malware in your pocket
Daniel Arp, Michael Spreitzenbarth, Malte Hubner, Hugo Gascon, Konrad Rieck, and CERT Siemens · 2014
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An empirical comparison of botnet detection methods
S. García, M. Grill, J. Stiborek, and A. Zunino · 2014
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Dimitris Bertsimas, Arthur Delarue, Patrick Jaillet, and Sebastien Martin · 2014
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Automatic inference of search patterns for taint-style vulnerabilities
Fabian Yamaguchi, Alwin Maier, Hugo Gascon, and Konrad Rieck · 2015
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Trafficav: An effective and explainable detection of mobile malware behavior using network traffic
Shanshan Wang, Zhenxiang Chen, Lei Zhang, Qiben Yan, Bo Yang, Lizhi Peng, and Zhongtian Jia · 2016
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European Union regulations on algorithmic decision-making and a “right to explanation”
Bryce Goodman and Seth Flaxman · 2017
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The goods, the bads and the uglies: Supporting decisions in malware detection through visual analytics
Marco Angelini, Leonardo Aniello, Simone Lenti, Giuseppe Santucci, and Daniele Ucci · 2017
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Deltaphish: Detecting phishing webpages in compromised websites
Igino Corona, Battista Biggio, Matteo Contini, Luca Piras, Roberto Corda, Mauro Mereu, Guido Mureddu, Davide Ariu, and Fabio Roli · 2017
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Adversarial-Playground: A visualization suite showing how adversarial examples fool deep learning
Andrew P Norton and Yanjun Qi · 2017
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Neural nets can learn function type signatures from binaries
Zheng Leong Chua, Shiqi Shen, Prateek Saxena, and Zhenkai Liang · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
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SoK: Security and privacy in machine learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael P Wellman · 2018
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LEMNA: Explaining Deep Learning Based Security Applications
Wenbo Guo, Dongliang Mu, Jun Xu, Purui Su, Gang Wang, and Xinyu Xing · 2018
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Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)
Amina Adadi and Mohammed Berrada · 2018
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On the robustness of interpretability methods
David Alvarez-Melis and Tommi S Jaakkola · 2018
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Building a Machine Learning Model for the SOC, by the Input from the SOC, and Analyzing it for the SOC
Awalin Sopan, Matthew Berninger, Murali Mulakaluri, and Raj Katakam · 2018
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TABOR: A graphical model-based approach for anomaly detection in industrial control systems
Qin Lin, Sridha Adepu, Sicco Verwer, and Aditya Mathur · 2018
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Recurrent neural network attention mechanisms for interpretable system log anomaly detection
Andy Brown, Aaron Tuor, Brian Hutchinson, and Nicole Nichols · 2018
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Automated Vulnerability Detection in Source Code Using Deep Representation Learning
Rebecca Russell, Louis Kim, Lei Hamilton, Tomo Lazovich, Jacob Harer, Onur Ozdemir, Paul Ellingwood, and Marc McConley · 2018
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An adversarial approach for explainable AI in intrusion detection systems
Daniel L Marino, Chathurika S Wickramasinghe, and Milos Manic · 2018
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Decision making with visualizations: a cognitive framework across disciplines
Lace M Padilla, Sarah H Creem-Regehr, Mary Hegarty, and Jeanine K Stefanucci · 2018
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The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C Lipton · 2018
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Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Andrew Ross and Finale Doshi-Velez · 2018
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Atomic habits: An easy & proven way to build good habits & break bad ones
James Clear · 2018
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Extracting automata from recurrent neural networks using queries and counterexamples
Gail Weiss, Yoav Goldberg, and Eran Yahav · 2018
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Learning finite state representations of recurrent policy networks
Anurag Koul, Sam Greydanus, and Alan Fern · 2018
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Safe reinforcement learning via shielding
Mohammed Alshiekh, Roderick Bloem, Rüdiger Ehlers, Bettina Könighofer, Scott Niekum, and Ufuk Topcu · 2018
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Verification for machine learning, autonomy, and neural networks survey
Weiming Xiang, Patrick Musau, Ayana A Wild, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Joel Rosenfeld, and Taylor T Johnson · 2018
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AI2: Safety and robustness certification of neural networks with abstract interpretation
Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, and Martin Vechev · 2018
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Learning neural representations for network anomaly detection
Miguel Nicolau, James McDermott, et al · 2018
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
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DARPA’s explainable artificial intelligence program
Gunning D Aha DW · 2019
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Towards self-explainable cyber-physical systems
Mathias Blumreiter, Joel Greenyer, Francisco Javier Chiyah Garcia, Verena Klös, Maike Schwammberger, Christoph Sommer, Andreas Vogelsang, and Andreas Wortmann · 2019
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Explainable artificial intelligence applications in NLP, biomedical, and malware classification: a literature review
Sherin Mary Mathews · 2019
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Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2019
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Explanations can be manipulated and geometry is to blame
Ann-Kathrin Dombrowski, Maximillian Alber, Christopher Anders, Marcel Ackermann, Klaus-Robert Müller, and Pan Kessel · 2019
Cited alongside, same era.
VulSniper: Focus Your Attention to Shoot Fine-Grained Vulnerabilities
Xu Duan, Jingzheng Wu, Shouling Ji, Zhiqing Rui, Tianyue Luo, Mutian Yang, and Yanjun Wu · 2019
Cited alongside, same era.
Explaining vulnerabilities of deep learning to adversarial malware binaries
Luca Demetrio, Battista Biggio, Giovanni Lagorio, Fabio Roli, and Armando Alessandro · 2019
Cited alongside, same era.
Sefi Akerman, Edan Habler, and Asaf Shabtai · 2019
Cited alongside, same era.
Explainable agents and robots: Results from a systematic literature review
Sule Anjomshoae, Amro Najjar, Davide Calvaresi, and Kary Främling · 2019
Cited alongside, same era.
Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers
Giorgio Severi, Jim Meyer, Scott Coull, and Alina Oprea · 2021
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Exploiting Explanations for Model Inversion Attacks
Xuejun Zhao, Wencan Zhang, Xiaokui Xiao, and Brian Lim · 2021
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Kim de Bie, Ana Lucic, and Hinda Haned · 2021
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Alert-driven Attack Graph Generation using S-PDFA
Azqa Nadeem, Sicco Verwer, Stephen Moskal, and Shanchieh Jay Yang · 2021
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An artificial intelligence cyberattack detection system to improve threat reaction in e-health
Carmelo Ardito, Tommaso Di Noia, Eugenio Di Sciascio, Domenico Lofù, Andrea Pazienza, and Felice Vitulano · 2021
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Interpretability in intelligent systems–a new concept?
Lars Kai Hansen and Laura Rieger · 2019
Cited alongside, same era.
Fairwashing: the risk of rationalization
Ulrich Aivodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, and Alain Tapp · 2019
Cited alongside, same era.
Robust decision trees against adversarial examples
Hongge Chen, Huan Zhang, Duane Boning, and Cho-Jui Hsieh · 2019
Cited alongside, same era.
Proposed Guidelines for the Responsible Use of Explainable Machine Learning, 2019
Patrick Hall, Navdeep Gill, and Nicholas Schmidt · 2019
Cited alongside, same era.
An adaptive multi-layer botnet detection technique using machine learning classifiers
Riaz Ullah Khan, Xiaosong Zhang, Rajesh Kumar, Abubakar Sharif, Noorbakhsh Amiri Golilarz, and Mamoun Alazab · 2019
Cited alongside, same era.
A deep learning based intelligent framework to mitigate DDoS attack in fog environment
Rojalina Priyadarshini and Rabindra Kumar Barik · 2019
Cited alongside, same era.
Trailblazing the artificial intelligence for cybersecurity discipline: a multi-disciplinary research roadmap, 2020
Sagar Samtani, Murat Kantarcioglu, and Hsinchun Chen · 2020
Cited alongside, same era.
Revisiting security threat on smart grids: accurate and interpretable fault location prediction and type classification
Carmelo Ardito, Yashar Deldjoo, Eugenio Di Sciascio, and Fatemeh Nazary · 2021
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E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System
Chanwoong Hwang and Taejin Lee · 2021
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Explainable Intelligence-Driven Defense Mechanism Against Advanced Persistent Threats: A Joint Edge Game and AI Approach
Huiling Li, Jun Wu, Hansong Xu, Gaolei Li, and Mohsen Guizani · 2021
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Explainable artificial intelligence (XAI) interactively working with humans as a junior cyber analyst
Eric Holder and Ning Wang · 2021
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Explainable artificial intelligence (XAI) to enhance trust management in intrusion detection systems using decision tree model
Basim Mahbooba, Mohan Timilsina, Radhya Sahal, and Martin Serrano · 2021
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FAIXID: a framework for enhancing AI explainability of intrusion detection results using data cleaning techniques
Hong Liu, Chen Zhong, Awny Alnusair, and Sheikh Rabiul Islam · 2021
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Towards an interpretable deep learning model for mobile malware detection and family identification
Giacomo Iadarola, Fabio Martinelli, Francesco Mercaldo, and Antonella Santone · 2021
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Why an android app is classified as malware: Toward malware classification interpretation
Bozhi Wu, Sen Chen, Cuiyun Gao, Lingling Fan, Yang Liu, Weiping Wen, and Michael R Lyu · 2021
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IoT Botnet Detection using Black-box Machine Learning Models: the Trade-off between Performance and Interpretability
Nourhène Ben Rabah, Bénédicte Le Grand, and Manuele Kirsch Pinheiro · 2021
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Towards explainable CNNs for Android malware detection
Martin Kinkead, Stuart Millar, Niall McLaughlin, and Philip O’Kane · 2021
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An explainable multi-modal hierarchical attention model for developing phishing threat intelligence
Yidong Chai, Yonghang Zhou, Weifeng Li, and Yuanchun Jiang · 2021
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Phishpedia: a hybrid deep learning based approach to visually identify phishing webpages
Yun Lin, Ruofan Liu, Dinil Mon Divakaran, Jun Yang Ng, Qing Zhou Chan, Yiwen Lu, Yuxuan Si, Fan Zhang, and Jin Song Dong · 2021
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XAI-based Microarchitectural Side-Channel Analysis for Website Fingerprinting Attacks and Defenses
Berk Gulmezoglu · 2021
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Vulnerability detection with fine-grained interpretations
Yi Li, Shaohua Wang, and Nguyen Tien · 2021
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Reasoning with counterfactual explanations for code vulnerability detection and correction
Anjana Wijekoon and Nirmalie Wiratunga · 2021
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FakeWake: Understanding and Mitigating Fake Wake-up Words of Voice Assistants
Yanjiao Chen, Yijie Bai, Richard Mitev, Kaibo Wang, Ahmad-Reza Sadeghi, and Wenyuan Xu · 2021
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Explainable unsupervised machine learning for cyber-physical systems
Chathurika S Wickramasinghe, Kasun Amarasinghe, Daniel L Marino, Craig Rieger, and Milos Manic · 2021
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Reliability of explainable artificial intelligence in adversarial perturbation scenarios
Antonio Galli, Stefano Marrone, Vincenzo Moscato, and Carlo Sansone · 2021
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The false hope of current approaches to explainable artificial intelligence in health care
Marzyeh Ghassemi, Luke Oakden-Rayner, and Andrew L Beam · 2021
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Explainability-based backdoor attacks against graph neural networks
Jing Xu, Minhui Xue, and Stjepan Picek · 2021
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On the privacy risks of model explanations
Reza Shokri, Martin Strobel, and Yair Zick · 2021
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Adversarial policy training against deep reinforcement learning
Xian Wu, Wenbo Guo, Hua Wei, and Xinyu Xing · 2021
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A Survey on Data-driven Software Vulnerability Assessment and Prioritization
Triet Le, Huaming Chen, and Muhammad Ali Babar · 2021
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Unleashing the power of machine learning models in banking through Explainable Artificial Intelligence (XAI), Jun 2022
Alexey Surkov, Val Srinivas, and Jill Gregorie · 2022
Closest in time.
The role of machine learning in cybersecurity
Giovanni Apruzzese, Pavel Laskov, Edgardo Montes de Oca, Wissam Mallouli, Luis Burdalo Rapa, Athanasios Vasileios Grammatopoulos, and Fabio Di Franco · 2022
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Research Trends, Challenges, and Emerging Topics in Digital Forensics: A Review of Reviews
Fran Casino, Thomas K. Dasaklis, Georgios P. Spathoulas, Marios Anagnostopoulos, Amrita Ghosal, István Bor̈oc̈z, Agusti Solanas, Mauro Conti, and Constantinos Patsakis · 2022
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Learning State Machines to Monitor and Detect Anomalies on a Kubernetes Cluster
Clinton Cao, Agathe Blaise, Sicco Verwer, and Filippo Rebecchi · 2022
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Interpretability of Machine Learning-Based Results of Malware Detection Using a Set of Rules
Jan Dolejš and Martin Jureček · 2022
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Interpretable Federated Transformer Log Learning for Cloud Threat Forensics
Gonzalo De La Torre Parra, Luis Selvera, Joseph Khoury, Hector Irizarry, Elias Bou-Harb, and Paul Rad · 2022
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DeepCASE: Semi-Supervised Contextual Analysis of Security Events”
Thijs van Ede, Hojjat Aghakhani, Noah Spahn, Riccardo Bortolameotti, Marco Cova, Andrea Continella, Maarten van Steen, Andreas Peter, Christopher Kruegel, and Giovanni Vigna · 2022
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Explainable AI in Cybersecurity Operations: Lessons Learned from XAI Tool Deployment
Megan Nyre-Yu, Elizabeth Susan Morris, Blake Cameron Moss, Charles Smutz, and Michael Smith · 2022
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Invoice #31415 attached: Automated analysis of malicious Microsoft Office documents
Vasilios Koutsokostas, Nikolaos Lykousas, Theodoros Apostolopoulos, Gabriele Orazi, Amrita Ghosal, Fran Casino, Mauro Conti, and Constantinos Patsakis · 2022
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Understanding the impact of explanations on advice-taking: a user study for AI-based clinical Decision Support Systems
Cecilia Panigutti, Andrea Beretta, Fosca Giannotti, and Dino Pedreschi · 2022
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Intelligent Malware Defenses
Azqa Nadeem, Vera Rimmer, Wouter Joosen, and Sicco Verwer · 2022
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Dos and don’ts of machine learning in computer security
Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, and Konrad Rieck · 2022
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The Disagreement Problem in Explainable Machine Learning: A Practitioner’s Perspective
Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju · 2022
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Attribution-based Explanations that Provide Recourse Cannot be Robust
Hidde Fokkema, Rianne de Heide, and Tim van Erven · 2022
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Learning to be adversarially robust and differentially private
Jamie Hayes, Borja Balle, and M Pawan Kumar · 2022
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System Preserving Explainability and Confidentiality (SPEC)
Sumitra Biswal · 2022
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Establishing the contaminating effect of metadata feature inclusion in machine-learned network intrusion detection models
Laurens D’hooge, Miel Verkerken, Bruno Volckaert, Tim Wauters, and Filip De Turck · 2022
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Encoding NetFlows for State-Machine Learning
Clinton Cao, Annibale Panichella, Sicco Verwer, Agathe Blaise, and Filippo Rebecchi · 2022
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Learning about the adversary
Azqa Nadeem, Shanchieh Jay Yang, and Sicco Verwer · 2023
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