{
  "id": 74671,
  "title": "Related papers",
  "url": "/competitions/malware-classification/discussion/74671",
  "author_name": "QnKhuat",
  "post_date": "2018-12-14T10:27:16.077000",
  "votes": 2,
  "comment_count": 0,
  "views": 0,
  "content": "<p>There are all the free papers cited in the challenge paper mentioned in <a href=\"https://www.kaggle.com/c/microsoft-malware-prediction/discussion/74593\">welcome thread</a> </p>\n\n<p><a href=\"https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?referer=https://www.google.com/&amp;httpsredir=1&amp;article=1566&amp;context=etd_projects\">Measuring the Effectiveness of Generic Malware Models</a></p>\n\n<p><a href=\"http://arxiv.org/abs/1609.02404\">ITect: Scalable Information Theoretic Similarity for Malware Detection</a></p>\n\n<p><a href=\"https://arxiv.org/abs/1603.09638\">Detection under Privileged Information</a></p>\n\n<p><a href=\"https://link.springer.com/article/10.1007%2Fs11416-015-0260-0\">On normalized compression distance and large malware</a></p>\n\n<p><a href=\"https://ieeexplore.ieee.org/document/7723750/\">Malware Sequence Alignment</a></p>\n\n<p><a href=\"https://ieeexplore.ieee.org/document/7527757\">Polymorphic Malware Detection Using Sequence Classification Methods</a></p>\n\n<p><a href=\"https://jis-eurasipjournals.springeropen.com/articles/10.1186/s13635-017-0055-6\">Polymorphic malware detection using sequence classification methods and ensembles</a></p>\n\n<p><a href=\"https://arxiv.org/abs/1802.04365\">Learning a Neural-network-based Representation for Open Set Recognition</a></p>\n\n<p><a href=\"https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/jordaney\">Transcend: Detecting Concept Drift in Malware Classification Models</a></p>\n\n<p><a href=\"https://arxiv.org/abs/1802.04528\">Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples</a></p>\n\n<p><a href=\"https://www.researchgate.net/publication/317423852_Discovering_Malware_with_Time_Series_Shapelets\">Discovering Malware with Time Series Shapelets</a></p>\n\n<hr>\n\n<p>If you found any related papers, Please share with us</p>",
  "messages": [
    {
      "id": 438880,
      "postDate": "2018-12-14T10:27:16.077Z",
      "content": "<p>There are all the free papers cited in the challenge paper mentioned in <a href=\"https://www.kaggle.com/c/microsoft-malware-prediction/discussion/74593\">welcome thread</a> </p>\n\n<p><a href=\"https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?referer=https://www.google.com/&amp;httpsredir=1&amp;article=1566&amp;context=etd_projects\">Measuring the Effectiveness of Generic Malware Models</a></p>\n\n<p><a href=\"http://arxiv.org/abs/1609.02404\">ITect: Scalable Information Theoretic Similarity for Malware Detection</a></p>\n\n<p><a href=\"https://arxiv.org/abs/1603.09638\">Detection under Privileged Information</a></p>\n\n<p><a href=\"https://link.springer.com/article/10.1007%2Fs11416-015-0260-0\">On normalized compression distance and large malware</a></p>\n\n<p><a href=\"https://ieeexplore.ieee.org/document/7723750/\">Malware Sequence Alignment</a></p>\n\n<p><a href=\"https://ieeexplore.ieee.org/document/7527757\">Polymorphic Malware Detection Using Sequence Classification Methods</a></p>\n\n<p><a href=\"https://jis-eurasipjournals.springeropen.com/articles/10.1186/s13635-017-0055-6\">Polymorphic malware detection using sequence classification methods and ensembles</a></p>\n\n<p><a href=\"https://arxiv.org/abs/1802.04365\">Learning a Neural-network-based Representation for Open Set Recognition</a></p>\n\n<p><a href=\"https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/jordaney\">Transcend: Detecting Concept Drift in Malware Classification Models</a></p>\n\n<p><a href=\"https://arxiv.org/abs/1802.04528\">Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples</a></p>\n\n<p><a href=\"https://www.researchgate.net/publication/317423852_Discovering_Malware_with_Time_Series_Shapelets\">Discovering Malware with Time Series Shapelets</a></p>\n\n<hr>\n\n<p>If you found any related papers, Please share with us</p>",
      "rawMarkdown": "There are all the free papers cited in the challenge paper mentioned in [welcome thread][1] \n\n[Measuring the Effectiveness of Generic Malware Models][2]\n\n[ITect: Scalable Information Theoretic Similarity for Malware Detection][3]\n\n[Detection under Privileged Information][4]\n\n[On normalized compression distance and large malware][5]\n\n[Malware Sequence Alignment][6]\n\n[Polymorphic Malware Detection Using Sequence Classification Methods][7]\n\n[Polymorphic malware detection using sequence classification methods and ensembles][8]\n\n[Learning a Neural-network-based Representation for Open Set Recognition][9]\n\n[Transcend: Detecting Concept Drift in Malware Classification Models][10]\n\n[Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples][11]\n\n[Discovering Malware with Time Series Shapelets][12]\n\n-----------------------------------------------------------------------------\nIf you found any related papers, Please share with us\n\n\n  [1]: https://www.kaggle.com/c/microsoft-malware-prediction/discussion/74593\n  [2]: https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?referer=https://www.google.com/&amp;httpsredir=1&amp;article=1566&amp;context=etd_projects\n  [3]: http://arxiv.org/abs/1609.02404\n  [4]: https://arxiv.org/abs/1603.09638\n  [5]: https://link.springer.com/article/10.1007%2Fs11416-015-0260-0\n  [6]: https://ieeexplore.ieee.org/document/7723750/\n  [7]: https://ieeexplore.ieee.org/document/7527757\n  [8]: https://jis-eurasipjournals.springeropen.com/articles/10.1186/s13635-017-0055-6\n  [9]: https://arxiv.org/abs/1802.04365\n  [10]: https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/jordaney\n  [11]: https://arxiv.org/abs/1802.04528\n  [12]: https://www.researchgate.net/publication/317423852_Discovering_Malware_with_Time_Series_Shapelets",
      "votes": 2
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "438880": "There are all the free papers cited in the challenge paper mentioned in [welcome thread][1] \n\n[Measuring the Effectiveness of Generic Malware Models][2]\n\n[ITect: Scalable Information Theoretic Similarity for Malware Detection][3]\n\n[Detection under Privileged Information][4]\n\n[On normalized compression distance and large malware][5]\n\n[Malware Sequence Alignment][6]\n\n[Polymorphic Malware Detection Using Sequence Classification Methods][7]\n\n[Polymorphic malware detection using sequence classification methods and ensembles][8]\n\n[Learning a Neural-network-based Representation for Open Set Recognition][9]\n\n[Transcend: Detecting Concept Drift in Malware Classification Models][10]\n\n[Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples][11]\n\n[Discovering Malware with Time Series Shapelets][12]\n\n-----------------------------------------------------------------------------\nIf you found any related papers, Please share with us\n\n\n  [1]: https://www.kaggle.com/c/microsoft-malware-prediction/discussion/74593\n  [2]: https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?referer=https://www.google.com/&amp;httpsredir=1&amp;article=1566&amp;context=etd_projects\n  [3]: http://arxiv.org/abs/1609.02404\n  [4]: https://arxiv.org/abs/1603.09638\n  [5]: https://link.springer.com/article/10.1007%2Fs11416-015-0260-0\n  [6]: https://ieeexplore.ieee.org/document/7723750/\n  [7]: https://ieeexplore.ieee.org/document/7527757\n  [8]: https://jis-eurasipjournals.springeropen.com/articles/10.1186/s13635-017-0055-6\n  [9]: https://arxiv.org/abs/1802.04365\n  [10]: https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/jordaney\n  [11]: https://arxiv.org/abs/1802.04528\n  [12]: https://www.researchgate.net/publication/317423852_Discovering_Malware_with_Time_Series_Shapelets"
  }
}