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Guofei Gu, Phillip A Porras, Vinod Yegneswaran, Martin W Fong, and Wenke Lee. 2007. Bothunter: Detecting malware infection through ids-driven dialog correlation.. In USENIX Security Symposium, Vol. 7."},{"key":"e_1_3_2_1_35_1","volume-title":"A survey of methods for explaining black box models. Comput. Surveys","author":"Guidotti Riccardo","year":"2018","unstructured":"Riccardo Guidotti , Anna Monreale , Salvatore Ruggieri , Franco Turini , Fosca Giannotti , and Dino Pedreschi . 2018. A survey of methods for explaining black box models. Comput. Surveys ( 2018 ). Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018. A survey of methods for explaining black box models. Comput. 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In Proc. of the International Conference on Learning Representations (ICLR). Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. In Proc. of the International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_39_1","volume-title":"Proc. of the International Conference on Information Systems Security and Privacy (ICISSP). SciTePress.","author":"Lashkari Arash Habibi","unstructured":"Arash Habibi Lashkari , Gerard Draper-Gil , Mohammad Saiful Islam Mamun , and Ali A. Ghorbani . 2017. Characterization of Tor Traffic using Time based Features . In Proc. of the International Conference on Information Systems Security and Privacy (ICISSP). SciTePress. Arash Habibi Lashkari, Gerard Draper-Gil, Mohammad Saiful Islam Mamun, and Ali A. Ghorbani. 2017. Characterization of Tor Traffic using Time based Features. In Proc. of the International Conference on Information Systems Security and Privacy (ICISSP). 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An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Systems With Applications ( 2012 ). Yinhui Li, Jingbo Xia, Silan Zhang, Jiakai Yan, Xiaochuan Ai, and Kuobin Dai. 2012. An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Systems With Applications (2012)."},{"key":"e_1_3_2_1_42_1","volume-title":"Jo a o Gama, and Guangquan Zhang","author":"Lu Jie","year":"2019","unstructured":"Jie Lu , Anjin Liu , Fan Dong , Feng Gu , Jo a o Gama, and Guangquan Zhang . 2019 . Learning under Concept Drift : A Review. IEEE Transactions on Knowledge and Data Engineering (TKDE) ( 2019). Jie Lu, Anjin Liu, Fan Dong, Feng Gu, Jo a o Gama, and Guangquan Zhang. 2019. Learning under Concept Drift: A Review. IEEE Transactions on Knowledge and Data Engineering (TKDE) (2019)."},{"key":"e_1_3_2_1_43_1","volume-title":"Analysis of high volumes of network traffic for advanced persistent threat detection. Computer Networks","author":"Marchetti Mirco","year":"2016","unstructured":"Mirco Marchetti , Fabio Pierazzi , Michele Colajanni , and Alessandro Guido . 2016. Analysis of high volumes of network traffic for advanced persistent threat detection. Computer Networks ( 2016 ). Mirco Marchetti, Fabio Pierazzi, Michele Colajanni, and Alessandro Guido. 2016. Analysis of high volumes of network traffic for advanced persistent threat detection. Computer Networks (2016)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2017.8257930"},{"key":"e_1_3_2_1_45_1","volume-title":"December","author":"Labs McAfee","year":"2016","unstructured":"McAfee Labs . 2016. McAfee Labs Threats Report , December 2016 . https:\/\/www.mcafee.com\/enterprise\/en-us\/assets\/reports\/rp-quarterly-threats-dec-2016.pdf. McAfee Labs. 2016. McAfee Labs Threats Report, December 2016. https:\/\/www.mcafee.com\/enterprise\/en-us\/assets\/reports\/rp-quarterly-threats-dec-2016.pdf."},{"key":"e_1_3_2_1_46_1","volume-title":"Proc. of the Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA).","author":"Miller Brad","unstructured":"Brad Miller , Alex Kantchelian , Michael Carl Tschantz , Sadia Afroz , Rekha Bachwani , Riyaz Faizullabhoy , Ling Huang , Vaishaal Shankar , Tony Wu , George Yiu , Anthony D. Joseph , and J. D. Tygar . 2016. Reviewer Integration and Performance Measurement for Malware Detection . In Proc. of the Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA). Brad Miller, Alex Kantchelian, Michael Carl Tschantz, Sadia Afroz, Rekha Bachwani, Riyaz Faizullabhoy, Ling Huang, Vaishaal Shankar, Tony Wu, George Yiu, Anthony D. Joseph, and J. D. Tygar. 2016. Reviewer Integration and Performance Measurement for Malware Detection. In Proc. of the Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA)."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23204"},{"key":"e_1_3_2_1_48_1","volume-title":"Abdulmageed","author":"Mohamed Marwa R.","year":"2019","unstructured":"Marwa R. Mohamed , Abdurrahman A. Nasr , Ibrahim F. Tarrad , and Mohamed Z . Abdulmageed . 2019 . Exploiting Incremental Classifiers for the Training of an Adaptive Intrusion Detection Model. Int. Journal of Network Security ( 2019). Marwa R. Mohamed, Abdurrahman A. Nasr, Ibrahim F. Tarrad, and Mohamed Z. Abdulmageed. 2019. Exploiting Incremental Classifiers for the Training of an Adaptive Intrusion Detection Model. Int. Journal of Network Security (2019)."},{"key":"e_1_3_2_1_49_1","volume-title":"A unifying view on dataset shift in classification. Pattern Recognition","author":"Moreno-Torres Jose G.","year":"2012","unstructured":"Jose G. Moreno-Torres , Troy Raeder , Roc\u00edo Ala\u00edz-Rodr\u00edguez , Nitesh V. Chawla , and Francisco Herrera . 2012. A unifying view on dataset shift in classification. Pattern Recognition ( 2012 ). Jose G. Moreno-Torres, Troy Raeder, Roc\u00edo Ala\u00edz-Rodr\u00edguez, Nitesh V. Chawla, and Francisco Herrera. 2012. A unifying view on dataset shift in classification. Pattern Recognition (2012)."},{"key":"e_1_3_2_1_50_1","volume-title":"Sayyed Rasoul Mousavi, and Homa Hasannejad","author":"Noorbehbahani Fakhroddin","year":"2017","unstructured":"Fakhroddin Noorbehbahani , Ali Fanian , Sayyed Rasoul Mousavi, and Homa Hasannejad . 2017 . An incremental intrusion detection system using a new semi-supervised stream classification method. International Journal of Communication Systems ( 2017). Fakhroddin Noorbehbahani, Ali Fanian, Sayyed Rasoul Mousavi, and Homa Hasannejad. 2017. An incremental intrusion detection system using a new semi-supervised stream classification method. International Journal of Communication Systems (2017)."},{"key":"e_1_3_2_1_51_1","unstructured":"S. J. Pan and Q. Yang. 2010. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE) (2010).  S. J. Pan and Q. Yang. 2010. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE) (2010)."},{"key":"e_1_3_2_1_52_1","volume-title":"Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Edouard Duchesnay.","author":"Pedregosa Fabian","year":"2011","unstructured":"Fabian Pedregosa , Ga\u00ebl Varoquaux , Alexandre Gramfort , Vincent Michel , Bertrand Thirion , Olivier Grisel , Mathieu Blondel , Peter Prettenhofer , Ron Weiss , Vincent Dubourg , Jake Vander Plas , Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Edouard Duchesnay. 2011 . Scikit-learn : Machine Learning in Python. Journal of Machine Learning Research (JMLR) ( 2011). Fabian Pedregosa, Ga\u00ebl Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vander Plas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Edouard Duchesnay. 2011. Scikit-learn: Machine Learning in Python. 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IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) (2018)."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813654"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2010.25"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10207-019-00478-3"},{"key":"e_1_3_2_1_64_1","volume-title":"Rosli Salleh, and Lorenzo Cavallaro.","author":"Tam Kimberly","year":"2017","unstructured":"Kimberly Tam , Ali Feizollah , Nor Badrul Anuar , Rosli Salleh, and Lorenzo Cavallaro. 2017 . The Evolution of Android Malware and Android Analysis Techniques. Comput. Surveys ( 2017). Kimberly Tam, Ali Feizollah, Nor Badrul Anuar, Rosli Salleh, and Lorenzo Cavallaro. 2017. The Evolution of Android Malware and Android Analysis Techniques. Comput. 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Pattern Recognition Letters ( 2005). Chunlin Zhang, Ju Jiang, and Mohamed S. Kamel. 2005. Intrusion detection using hierarchical neural networks. 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