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Cost-Aware Robust Tree Ensembles for Security Applications [article]

Yizheng Chen, Shiqi Wang, Weifan Jiang, Asaf Cidon, Suman Jana
<span title="2021-02-23">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
There are various costs for attackers to manipulate the features of security classifiers.  ...  Our cost-aware training method can be applied to different types of tree ensembles, including gradient boosted decision trees and random forest models.  ...  Acknowledgements We thank Huan Zhang and the anonymous reviewers for their constructive and valuable feedback. This work is supported in part by NSF grants CNS  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.01149v5">arXiv:1912.01149v5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xkckv3cg2bgxbiy2dhhlotmg3q">fatcat:xkckv3cg2bgxbiy2dhhlotmg3q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210227092444/https://arxiv.org/pdf/1912.01149v5.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/74/f6/74f61086d15cb4b3f8f90fe9909a920a290f1a25.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.01149v5" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Beyond Robustness: Resilience Verification of Tree-Based Classifiers [article]

Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese, Federico Marcuzzi, Salvatore Orlando
<span title="2021-12-05">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We then introduce a formally sound data-independent stability analysis for decision trees and decision tree ensembles, which we experimentally assess on public datasets and we leverage for resilience verification  ...  Our results show that resilience verification is useful and feasible in practice, yielding a more reliable security assessment of both standard and robust decision tree models.  ...  However, observe that global rithms for training decision trees and decision tree ensembles robustness as defined in [13] cannot be used to reason about that are robust to evasion attacks [5], [  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.02705v1">arXiv:2112.02705v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ahw6lbkf7fbnnlubo7z5zuq4wy">fatcat:ahw6lbkf7fbnnlubo7z5zuq4wy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211208102538/https://arxiv.org/pdf/2112.02705v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3d/71/3d71651c9a3ebf0f73844084dfc73a2202687ac6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.02705v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Learning Security Classifiers with Verified Global Robustness Properties [article]

Yizheng Chen, Shiqi Wang, Yue Qin, Xiaojing Liao, Suman Jana, David Wagner
<span title="2021-05-24">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We define new notions of global robustness that are more suitable for security classifiers. We design a novel booster-fixer training framework to enforce global robustness properties.  ...  In comparison, we show that we can train classifiers to satisfy different global robustness properties for three security datasets, and even multiple properties at the same time, with modest impact on  ...  Any decision tree (or ensemble of trees) can be expressed as a logic ensemble, with one clause per leaf in the tree, but logic ensembles are more expressive (for a fixed number of clauses) because they  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.11363v1">arXiv:2105.11363v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/apeyqnltarbxzhjgb57kbui4pe">fatcat:apeyqnltarbxzhjgb57kbui4pe</a> </span>
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Feature partitioning for robust tree ensembles and their certification in adversarial scenarios

Stefano Calzavara, Claudio Lucchese, Federico Marcuzzi, Salvatore Orlando
<span title="">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sjaucqv5czbyxdt75q6vkociju" style="color: black;">EURASIP Journal on Information Security</a> </i> &nbsp;
We apply the proposed strategy to decision tree ensembles, and we also propose an approximate certification method for tree ensembles that efficiently provides a lower bound of the accuracy of a forest  ...  The attacker aims at finding a perturbation of an instance that changes the model outcome.We propose a model-agnostic strategy that builds a robust ensemble by training its basic models on feature-based  ...  Along the lines of ensemble training, we use multiple feature partitions to train a distinct T P for each partition P, and join together the resulting robust forests T P in a single decision tree ensemble  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s13635-021-00127-0">doi:10.1186/s13635-021-00127-0</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/r45gjmumtfeoncchjnb6f3f3mi">fatcat:r45gjmumtfeoncchjnb6f3f3mi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211222134117/https://jis-eurasipjournals.springeropen.com/track/pdf/10.1186/s13635-021-00127-0.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/91/56/91561400cee9f944cb0dc079e0c061193a92c060.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s13635-021-00127-0"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [article]

Maksym Andriushchenko, Matthias Hein
<span title="2019-10-30">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
For boosted trees we show how to efficiently calculate and optimize an upper bound on the robust loss, which leads to state-of-the-art robust test error for boosted trees on MNIST (12.5 FMNIST (23.2 ϵ_  ...  However, for boosted decision trees and decision stumps there are almost no results, even though they are widely used in practice (e.g.  ...  Acknowledgements We thank the anonymous reviewers for very helpful and thoughtful comments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1906.03526v2">arXiv:1906.03526v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/icppxp5j3ja4vcsoqnlxfm2st4">fatcat:icppxp5j3ja4vcsoqnlxfm2st4</a> </span>
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An ensemble based approach for effective intrusion detection using majority voting

Alwi M. Bamhdi, Iram Abrar, Faheem Masoodi
<span title="2021-04-01">2021</span> <i title="Universitas Ahmad Dahlan"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/avuzjspx3nh5lboz3nsmpd3ba4" style="color: black;">TELKOMNIKA (Telecommunication Computing Electronics and Control)</a> </i> &nbsp;
On the network security canvas, Intrusion detection system (IDS) is an essential tool used for timely detection of cyber-attacks.  ...  In this backdrop, an ensemble approach has been proposed in current work to tackle the issues of single classifiers and accordingly, a highly scalable and constructive majority voting-based ensemble model  ...  The rationale of current work is to offer a robust IDS and for the same ensemble-based technique was employed.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12928/telkomnika.v19i2.18325">doi:10.12928/telkomnika.v19i2.18325</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/obt4f5mptvatnh5plplejysfli">fatcat:obt4f5mptvatnh5plplejysfli</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428062800/http://journal.uad.ac.id/index.php/TELKOMNIKA/article/download/18325/9900" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3d/52/3d522f63edbcaa63811b74fda316134ec0ba9610.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12928/telkomnika.v19i2.18325"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

An Intrusion Detection System for Identifying Attacks using Classification Technique

Aanchal Tiwari
<span title="2017-04-30">2017</span> <i title="International Journal for Research in Applied Science and Engineering Technology (IJRASET)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hsp44774azcezeyiq4kuzpfh5a" style="color: black;">International Journal for Research in Applied Science and Engineering Technology</a> </i> &nbsp;
Data mining based decision tree algorithm play very important role to develop the robust IDS to classify the attacks which is harmful for our system.  ...  We have also develop the robust ensemble model which is combination of C4.5, Simple CART and decision tree that gives better accuracy.  ...  In this research work, focus on the decision tree like C4.5, SimpleCART and Random tree to develop the robust IDS. The proposed IDS is robust and efficient classifier for classification of attacks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22214/ijraset.2017.4270">doi:10.22214/ijraset.2017.4270</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mlb6euflwvdhxe55lersab7nty">fatcat:mlb6euflwvdhxe55lersab7nty</a> </span>
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A STUDY OF THE MODIFIED KDD 99 DATASET BY USING CLASSIFIER ENSEMBLES

Mohammed J. Alhaddad
<span title="">2012</span> <i title="IOSR Journals"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xkfue5ht4jb3xjr2swegjpni24" style="color: black;">IOSR Journal of Engineering</a> </i> &nbsp;
Naïve Bayes and decision trees and their ensemble methods are used for this study. We used different performance measures in our study.  ...  Results also suggest that single decision tree is a good classifier for this data as it has reasonable classification accuracy and less training and testing time.  ...  We suggest that a single decision tree is a useful classifier for network security data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.9790/3021-0205961965">doi:10.9790/3021-0205961965</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bbgvu3izwbfmxb32tropsd5b4u">fatcat:bbgvu3izwbfmxb32tropsd5b4u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180602123438/http://www.iosrjen.org/Papers/vol2_issue5/E025961965.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/46/6a/466a7ed9b139e9c5888cc4cbb0422757dcc36694.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.9790/3021-0205961965"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Feature Partitioning for Robust Tree Ensembles and their Certification in Adversarial Scenarios [article]

Stefano Calzavara, Claudio Lucchese, Federico Marcuzzi, Salvatore Orlando
<span title="2020-04-07">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We experimented the proposed strategy on decision tree ensembles, and we also propose an approximate certification method for tree ensembles that efficiently assess the minimal accuracy of a forest on  ...  We propose a model-agnostic strategy that builds a robust ensemble by training its basic models on feature-based partitions of the given dataset.  ...  Along the lines of ensemble training, we use multiple feature partitionings and join together the resulting robust forests in a single decision tree ensemble.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.03295v1">arXiv:2004.03295v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oxdk6ngofjck3n6ekk5y6khecm">fatcat:oxdk6ngofjck3n6ekk5y6khecm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200410223734/https://arxiv.org/pdf/2004.03295v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.03295v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Formal Verification of Input-Output Mappings of Tree Ensembles [article]

John Törnblom, Simin Nadjm-Tehrani
<span title="2019-05-10">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We demonstrate that our method is practical for tree ensembles trained on low-dimensional data with up to 25 decision trees and tree depths of up to 20.  ...  In this paper, we present an efficient method to extract equivalence classes from decision trees and tree ensembles, and to formally verify that their input-output mappings comply with requirements.  ...  Robustness We verified the robustness against noise for all trained models by defining input regions surrounding each sample in the test set with the robustness margin = 0.05, which amounts to a 5% change  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.04194v1">arXiv:1905.04194v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vwfcqjyswvc3peth6g4svdlzcq">fatcat:vwfcqjyswvc3peth6g4svdlzcq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200831092841/https://arxiv.org/pdf/1905.04194v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d3/39/d3392f9e98716fe110d18024d472e881649f2a31.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.04194v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning

Neha Sharma, Narendra Yadav, Saurabh Sharma
<span title="2021-10-13">2021</span> <i title="European Alliance for Innovation n.o."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/l7xphlcxlzcopemlfe46zdv2ui" style="color: black;">EAI Endorsed Transactions on Industrial Networks and Intelligent Systems</a> </i> &nbsp;
We will also implement several ensemble algorithms like Random Forest, Extra trees, AdaBoost, and XGBoost to derive insights from the data and make useful predictions.  ...  This paper can give a basic understanding of data analytics in terms of security using Machine Learning techniques.  ...  Increasing network security concerns encourages a robust intrusion detection system that can learn from data and apply it to real-time scenarios.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4108/eai.13-10-2021.171319">doi:10.4108/eai.13-10-2021.171319</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vb27kzvk4jg67f4jmvizb726gq">fatcat:vb27kzvk4jg67f4jmvizb726gq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211025204326/https://eudl.eu/pdf/10.4108/eai.13-10-2021.171319" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8c/e8/8ce8b313322cb10dc45d312d1a4f90970d5cf67e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4108/eai.13-10-2021.171319"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

On Training Robust PDF Malware Classifiers [article]

Yizheng Chen, Shiqi Wang, Dongdong She, Suman Jana
<span title="2019-12-03">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We utilize state-of-the-art verifiably robust training method to build robust PDF malware classifiers.  ...  In this paper, we take the first steps towards training robust PDF malware classifiers with verifiable robustness properties.  ...  Acknowledgements We thank our shepherd Nicolas Papernot and the anonymous reviewers for their constructive and valuable feedback.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.03542v2">arXiv:1904.03542v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p36c6kaxwzavbedcbbkaqybqyy">fatcat:p36c6kaxwzavbedcbbkaqybqyy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200905035528/https://arxiv.org/pdf/1904.03542v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c2/98/c298f274649f83f8c17f7a859c35fa1980f8f0f6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.03542v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

An Ensemble Model for Identification of Phishing Website

Jaspreet Kaur Virdi
<span title="2017-04-27">2017</span> <i title="International Journal for Research in Applied Science and Engineering Technology (IJRASET)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hsp44774azcezeyiq4kuzpfh5a" style="color: black;">International Journal for Research in Applied Science and Engineering Technology</a> </i> &nbsp;
To develop a robust model , we have ensemble the models with different combinations.  ...  We have achieved better accuracy with ensemble of C4.5, SimpleCart , MLP and Random tree with all data partitions, but it achieved best accuracy as 97.16% in case of 85-15% data partition.  ...  In this research work, proposed ensemble (C4.5+SimpleCart+MLP+Random tree) is robust and efficient model and recommended for classification of phishing websites with 80-15% training-testing data partition  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22214/ijraset.2017.4205">doi:10.22214/ijraset.2017.4205</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2wmoczrdsrdntlyhcaolqyf5yi">fatcat:2wmoczrdsrdntlyhcaolqyf5yi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200709173643/https://www.ijraset.com/fileserve.php?FID=7278" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4f/f1/4ff1df56a183df90ea50b351a68858aaff59d0c4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22214/ijraset.2017.4205"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Resource Efficient Boosting Method for IoT Security Monitoring

Idris Zakariyya, M. Omar Al-Kadri, Harsha Kalutarage
<span title="2021-01-09">2021</span> <i title="IEEE"> 2021 IEEE 18th Annual Consumer Communications &amp; Networking Conference (CCNC) </i> &nbsp;
The need for robust security techniques in response to their resource limitation escalates.  ...  Bagging is an ensemble decision tree algorithm that manipulates the training data instances to improve classification model performance [6] .  ... 
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Noise Flooding for Detecting Audio Adversarial Examples Against Automatic Speech Recognition

Krishan Rajaratnam, Jugal Kalita
<span title="">2018</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/aejfybxtvbeabjuep6xilinche" style="color: black;">2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)</a> </i> &nbsp;
This technique of flooding, which does not require retraining or modifying the model, is inspired by work done in computer vision and builds on the idea that speech classifiers are relatively robust to  ...  A combined defense incorporating 5 different frequency bands for flooding the signal with noise outperformed other existing defenses in the audio space, detecting adversarial examples with 91.8% precision  ...  Acknowledgments We are thankful to the reviewers for helpful criticism, and the UCCS LINC and VAST labs for general support. This work is supported by the National Science  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/isspit.2018.8642623">doi:10.1109/isspit.2018.8642623</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/isspit/RajaratnamK18.html">dblp:conf/isspit/RajaratnamK18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bfytvhnyvvczjattw6y3jei7iu">fatcat:bfytvhnyvvczjattw6y3jei7iu</a> </span>
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