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HinDom: A Robust Malicious Domain Detection System based on Heterogeneous Information Network with Transductive Classification [article]

Xiaoqing Sun, Mingkai Tong, Jiahai Yang
<span title="2019-09-04">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Besides, the metapath-based transductive classification method enables HinDom to detect malicious domains with only a small fraction of labeled samples.  ...  As a solution to this problem, we propose a robust domain detection system named HinDom.  ...  Acknowledgment We thank Hui Zhang, Chenxi Li, Shize Zhang for constructive recommendations on experiments and data processing.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.01590v1">arXiv:1909.01590v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2zphegvq6bgf5ktrzqdylb3kwm">fatcat:2zphegvq6bgf5ktrzqdylb3kwm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200831031210/https://arxiv.org/pdf/1909.01590v1.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/05/39/053973b807d752b5961326e231dc6f7b8add957b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.01590v1" 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>

Less is More: Robust and Novel Features for Malicious Domain Detection [article]

Chen Hajaj, Nitay Hason, Nissim Harel, Amit Dvir
<span title="2020-06-02">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Since machine learning has become one of the most prominent methods of malware detection, A robust feature selection mechanism is proposed that results in malicious domain detection models that are resistant  ...  Based on an extensive evaluation of the different feature sets and commonly used classification models, this paper shows that models that are based on robust features are resistant to malicious perturbations  ...  [49] presented a system called Segugio, an anomaly detection system based on passive DNS traffic to identify malware-controlled domain names based on their relationship to known malicious domains.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.01449v1">arXiv:2006.01449v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lctx65q5ozcntos2kmq7qwpbi4">fatcat:lctx65q5ozcntos2kmq7qwpbi4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200605100755/https://arxiv.org/pdf/2006.01449v1.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/2006.01449v1" 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>