{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:43:01Z","timestamp":1784302981565,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["IIS-1563816, CNS-1704701, DGE-1650044"],"award-info":[{"award-number":["IIS-1563816, CNS-1704701, DGE-1650044"]}]},{"DOI":"10.13039\/100004726","name":"Raytheon Company","doi-asserted-by":"publisher","award":["PhD Research Fellowship"],"award-info":[{"award-number":["PhD Research Fellowship"]}],"id":[{"id":"10.13039\/100004726","id-type":"DOI","asserted-by":"publisher"}]},{"name":"IBM","award":["PhD Research Fellowship"],"award-info":[{"award-number":["PhD Research Fellowship"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557533","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:22:22Z","timestamp":1665883342000},"page":"3948-3952","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":44,"title":["MalNet: A Large-Scale Image Database of Malicious Software"],"prefix":"10.1145","author":[{"given":"Scott","family":"Freitas","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahul","family":"Duggal","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duen Horng","family":"Chau","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Nokia Threat Intelligence Report. Network Security","year":"2018","unstructured":"2018. Nokia Threat Intelligence Report. Network Security ( 2018 ). https:\/\/doi.org\/10.1016\/S1353--4858(18)30122--3 10.1016\/S1353--4858(18)30122--3 2018. Nokia Threat Intelligence Report. Network Security (2018). https:\/\/doi.org\/10.1016\/S1353--4858(18)30122--3"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2901739.2903508"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2999320"},{"key":"e_1_3_2_1_4_1","volume-title":"Yan Lu, and Jiang Li.","author":"Burks Roland","year":"2019","unstructured":"Roland Burks , Kazi Aminul Islam , Yan Lu, and Jiang Li. 2019 . Data Augmentation with Generative Models for Improved Malware Detection: A Comparative Study. In UEMCON. IEEE , 0660--0665. Roland Burks, Kazi Aminul Islam, Yan Lu, and Jiang Li. 2019. Data Augmentation with Generative Models for Improved Malware Detection: A Comparative Study. In UEMCON. IEEE, 0660--0665."},{"key":"e_1_3_2_1_5_1","volume-title":"Data Augmentation Based Malware Detection using Convolutional Neural Networks. arXiv preprint arXiv:2010.01862","author":"Catak Ferhat Ozgur","year":"2020","unstructured":"Ferhat Ozgur Catak , Javed Ahmed , Kevser Sahinbas , and Zahid Hussain Khand . 2020. Data Augmentation Based Malware Detection using Convolutional Neural Networks. arXiv preprint arXiv:2010.01862 ( 2020 ). Ferhat Ozgur Catak, Javed Ahmed, Kevser Sahinbas, and Zahid Hussain Khand. 2020. Data Augmentation Based Malware Detection using Convolutional Neural Networks. arXiv preprint arXiv:2010.01862 (2020)."},{"key":"e_1_3_2_1_6_1","volume-title":"Deep transfer learning for static malware classification. arXiv preprint arXiv:1812.07606","author":"Chen Li","year":"2018","unstructured":"Li Chen . 2018. Deep transfer learning for static malware classification. arXiv preprint arXiv:1812.07606 ( 2018 ). Li Chen. 2018. Deep transfer learning for static malware classification. arXiv preprint arXiv:1812.07606 (2018)."},{"key":"e_1_3_2_1_7_1","unstructured":"Li Chen Ravi Sahita Jugal Parikh and Marc Marino. [n.d.]. Stamina: scalable deep learning approach for malware classification. Intel White Paper ([n. d.]).  Li Chen Ravi Sahita Jugal Parikh and Marc Marino. [n.d.]. Stamina: scalable deep learning approach for malware classification. Intel White Paper ([n. d.])."},{"key":"e_1_3_2_1_8_1","volume-title":"Malware detection using malware image and deep learning","author":"Choi Sunoh","unstructured":"Sunoh Choi , Sungwook Jang , Youngsoo Kim , and Jonghyun Kim . 2017. Malware detection using malware image and deep learning . In ICTC. IEEE , 1193--1195. Sunoh Choi, Sungwook Jang, Youngsoo Kim, and Jonghyun Kim. 2017. Malware detection using malware image and deep learning. In ICTC. IEEE, 1193--1195."},{"key":"e_1_3_2_1_9_1","volume-title":"A visual study of primitive binary fragment types. Black Hat USA","author":"Conti Gregory","year":"2010","unstructured":"Gregory Conti , Sergey Bratus , Anna Shubina , Andrew Lichtenberg , Roy Ragsdale , Robert Perez-Alemany , Benjamin Sangster , and Matthew Supan . 2010. A visual study of primitive binary fragment types. Black Hat USA ( 2010 ). Gregory Conti, Sergey Bratus, Anna Shubina, Andrew Lichtenberg, Roy Ragsdale, Robert Perez-Alemany, Benjamin Sangster, and Matthew Supan. 2010. A visual study of primitive binary fragment types. Black Hat USA (2010)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Yin Cui Menglin Jia Tsung-Yi Lin Yang Song and Serge Belongie. 2019. Classbalanced loss based on effective number of samples. In CVPR. 9268--9277.  Yin Cui Menglin Jia Tsung-Yi Lin Yang Song and Serge Belongie. 2019. Classbalanced loss based on effective number of samples. In CVPR. 9268--9277.","DOI":"10.1109\/CVPR.2019.00949"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2822680"},{"key":"e_1_3_2_1_12_1","volume-title":"HAR: Hardness Aware Reweighting for Imbalanced Datasets. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 735--745","author":"Duggal Rahul","year":"2021","unstructured":"Rahul Duggal , Scott Freitas , Sunny Dhamnani , Duen Horng Chau , and Jimeng Sun . 2021 . HAR: Hardness Aware Reweighting for Imbalanced Datasets. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 735--745 . Rahul Duggal, Scott Freitas, Sunny Dhamnani, Duen Horng Chau, and Jimeng Sun. 2021. HAR: Hardness Aware Reweighting for Imbalanced Datasets. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 735--745."},{"key":"e_1_3_2_1_13_1","volume-title":"ELF: An Early-Exiting Framework for Long-Tailed Classification. arXiv:2006.11979","author":"Duggal Rahul","year":"2020","unstructured":"Rahul Duggal , Scott Freitas , Sunny Dhamnani , Duen Horng , Jimeng Sun , 2020 . ELF: An Early-Exiting Framework for Long-Tailed Classification. arXiv:2006.11979 (2020). Rahul Duggal, Scott Freitas, Sunny Dhamnani, Duen Horng, Jimeng Sun, et al. 2020. ELF: An Early-Exiting Framework for Long-Tailed Classification. arXiv:2006.11979 (2020)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380241"},{"key":"e_1_3_2_1_15_1","volume-title":"Graph-based comparison of executable objects. SSTIC","author":"Dullien Thomas","year":"2005","unstructured":"Thomas Dullien and Rolf Rolles . 2005. Graph-based comparison of executable objects. SSTIC ( 2005 ). Thomas Dullien and Rolf Rolles. 2005. Graph-based comparison of executable objects. SSTIC (2005)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2965646"},{"key":"e_1_3_2_1_17_1","volume-title":"A Large-Scale Database for Graph Representation Learning. arXiv preprint arXiv:2011.07682","author":"Freitas Scott","year":"2020","unstructured":"Scott Freitas , Yuxiao Dong , Joshua Neil , and Duen Horng Chau . 2020. A Large-Scale Database for Graph Representation Learning. arXiv preprint arXiv:2011.07682 ( 2020 ). Scott Freitas, Yuxiao Dong, Joshua Neil, and Duen Horng Chau. 2020. A Large-Scale Database for Graph Representation Learning. arXiv preprint arXiv:2011.07682 (2020)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976236.61"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2805301"},{"key":"e_1_3_2_1_20_1","volume-title":"Gamut: Sifting through Images to Detect Android Malware.","author":"Gennissen Jordy","year":"2017","unstructured":"Jordy Gennissen , Lorenzo Cavallaro , Veelasha Moonsamy , and Lejla Batina . 2017 . Gamut: Sifting through Images to Detect Android Malware. Jordy Gennissen, Lorenzo Cavallaro, Veelasha Moonsamy, and Lejla Batina. 2017. Gamut: Sifting through Images to Detect Android Malware."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10207-014-0242-0"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_23_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861","author":"Howard Andrew G","year":"2017","unstructured":"Andrew G Howard , Menglong Zhu , Bo Chen , Dmitry Kalenichenko , Weijun Wang , Tobias Weyand , Marco Andreetto , and Hartwig Adam . 2017 . Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017). Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSR.2017.57"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2843859.2843866"},{"key":"e_1_3_2_1_27_1","volume-title":"Yang Wang, and Farkhund Iqbal.","author":"Kalash Mahmoud","year":"2018","unstructured":"Mahmoud Kalash , Mrigank Rochan , Noman Mohammed , Neil DB Bruce , Yang Wang, and Farkhund Iqbal. 2018 . Malware classification with deep convolutional neural networks. In NTMS. IEEE , 1--5. Mahmoud Kalash, Mrigank Rochan, Noman Mohammed, Neil DB Bruce, Yang Wang, and Farkhund Iqbal. 2018. Malware classification with deep convolutional neural networks. In NTMS. IEEE, 1--5."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CICYBS.2013.6597204"},{"key":"e_1_3_2_1_29_1","volume-title":"Machine learning based malware classification for Android applications using multimodal image representations","author":"Kumar Ajit","unstructured":"Ajit Kumar , K Pramod Sagar , KS Kuppusamy , and G Aghila . 2016. Machine learning based malware classification for Android applications using multimodal image representations . In ISCO. IEEE , 1--6. Ajit Kumar, K Pramod Sagar, KS Kuppusamy, and G Aghila. 2016. Machine learning based malware classification for Android applications using multimodal image representations. In ISCO. IEEE, 1--6."},{"key":"e_1_3_2_1_30_1","volume-title":"Androzoo: Collecting millions of android apps and their metadata for the research community. arXiv preprint arXiv:1709.05281","author":"Li Li","year":"2017","unstructured":"Li Li , Jun Gao , M\u00e9d\u00e9ric Hurier , Pingfan Kong , Tegawend\u00e9 F Bissyand\u00e9 , Alexandre Bartel , Jacques Klein , and Yves Le Traon . 2017 . Androzoo: Collecting millions of android apps and their metadata for the research community. arXiv preprint arXiv:1709.05281 (2017). Li Li, Jun Gao, M\u00e9d\u00e9ric Hurier, Pingfan Kong, Tegawend\u00e9 F Bissyand\u00e9, Alexandre Bartel, Jacques Klein, and Yves Le Traon. 2017. Androzoo: Collecting millions of android apps and their metadata for the research community. arXiv preprint arXiv:1709.05281 (2017)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_3_2_1_32_1","volume-title":"New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning. arXiv preprint arXiv:1907.08356","author":"Lu Shuqiang","year":"2019","unstructured":"Shuqiang Lu , Lingyun Ying , Wenjie Lin , YuWang, Meining Nie , Kaiwen Shen , Lu Liu , and Haixin Duan . 2019. New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning. arXiv preprint arXiv:1907.08356 ( 2019 ). Shuqiang Lu, Lingyun Ying,Wenjie Lin, YuWang, Meining Nie, Kaiwen Shen, Lu Liu, and Haixin Duan. 2019. New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning. arXiv preprint arXiv:1907.08356 (2019)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2017.8258512"},{"key":"e_1_3_2_1_34_1","volume-title":"BooJoong Kang, Suleiman Yerima, Paul Miller, Sakir Sezer, Yeganeh Safaei, Erik Trickel, Ziming Zhao, Adam Doup\u00e9, et al.","author":"McLaughlin Niall","year":"2017","unstructured":"Niall McLaughlin , Jesus Martinez del Rincon , BooJoong Kang, Suleiman Yerima, Paul Miller, Sakir Sezer, Yeganeh Safaei, Erik Trickel, Ziming Zhao, Adam Doup\u00e9, et al. 2017 . Deep android malware detection. In CODASPY. ACM , 301--308. Niall McLaughlin, Jesus Martinez del Rincon, BooJoong Kang, Suleiman Yerima, Paul Miller, Sakir Sezer, Yeganeh Safaei, Erik Trickel, Ziming Zhao, Adam Doup\u00e9, et al. 2017. Deep android malware detection. In CODASPY. ACM, 301--308."},{"key":"e_1_3_2_1_35_1","volume-title":"Deep learning for image-based mobile malware detection. Journal of Computer Virology and Hacking Techniques","author":"Mercaldo Francesco","year":"2020","unstructured":"Francesco Mercaldo and Antonella Santone . 2020. Deep learning for image-based mobile malware detection. Journal of Computer Virology and Hacking Techniques ( 2020 ). Francesco Mercaldo and Antonella Santone. 2020. Deep learning for image-based mobile malware detection. Journal of Computer Virology and Hacking Techniques (2020)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Antonio Nappa M Zubair Rafique and Juan Caballero. 2013. Driving in the cloud: An analysis of drive-by download operations and abuse reporting. In DIMVA.  Antonio Nappa M Zubair Rafique and Juan Caballero. 2013. Driving in the cloud: An analysis of drive-by download operations and abuse reporting. In DIMVA.","DOI":"10.1007\/978-3-642-39235-1_1"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2016904.2016908"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2046684.2046689"},{"key":"e_1_3_2_1_39_1","volume-title":"Virus-MNIST: A Benchmark Malware Dataset. arXiv preprint arXiv:2103.00602","author":"Noever David","year":"2021","unstructured":"David Noever and Samantha E Miller Noever . 2021. Virus-MNIST: A Benchmark Malware Dataset. arXiv preprint arXiv:2103.00602 ( 2021 ). David Noever and Samantha E Miller Noever. 2021. Virus-MNIST: A Benchmark Malware Dataset. arXiv preprint arXiv:2103.00602 (2021)."},{"key":"e_1_3_2_1_40_1","unstructured":"The Council of Economic Advisers. 2018. The Cost of Malicious Cyber Activity to the U.S. Economy. (2018).  The Council of Economic Advisers. 2018. The Cost of Malicious Cyber Activity to the U.S. Economy. (2018)."},{"key":"e_1_3_2_1_41_1","unstructured":"Ben Popper. 2017. Google announces over 2 billion monthly active devices on Android. https:\/\/www.theverge.com\/2017\/5\/17\/15654454\/android-reaches-2- billion-monthly-active-users  Ben Popper. 2017. Google announces over 2 billion monthly active devices on Android. https:\/\/www.theverge.com\/2017\/5\/17\/15654454\/android-reaches-2- billion-monthly-active-users"},{"key":"e_1_3_2_1_42_1","unstructured":"Edward Raff Jon Barker Jared Sylvester Robert Brandon Bryan Catanzaro and Charles K Nicholas. 2018. Malware detection by eating a whole exe. InWorkshops at the Thirty-Second AAAI Conference on Artificial Intelligence.  Edward Raff Jon Barker Jared Sylvester Robert Brandon Bryan Catanzaro and Charles K Nicholas. 2018. Malware detection by eating a whole exe. InWorkshops at the Thirty-Second AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_43_1","volume-title":"End-to-end malware detection for android IoT devices using deep learning. Ad Hoc Networks","author":"Ren Zhongru","year":"2020","unstructured":"Zhongru Ren , Haomin Wu , Qian Ning , Iftikhar Hussain , and Bingcai Chen . 2020. End-to-end malware detection for android IoT devices using deep learning. Ad Hoc Networks ( 2020 ). Zhongru Ren, Haomin Wu, Qian Ning, Iftikhar Hussain, and Bingcai Chen. 2020. End-to-end malware detection for android IoT devices using deep learning. Ad Hoc Networks (2020)."},{"key":"e_1_3_2_1_44_1","volume-title":"Information Technology-New Generations","author":"Rezende Edmar","unstructured":"Edmar Rezende , Guilherme Ruppert , Tiago Carvalho , Antonio Theophilo , Fabio Ramos , and Paulo de Geus . 2018. Malicious software classification using VGG16 deep neural network's bottleneck features . In Information Technology-New Generations . Springer , 51--59. Edmar Rezende, Guilherme Ruppert, Tiago Carvalho, Antonio Theophilo, Fabio Ramos, and Paulo de Geus. 2018. Malicious software classification using VGG16 deep neural network's bottleneck features. In Information Technology-New Generations. Springer, 51--59."},{"key":"e_1_3_2_1_45_1","volume-title":"Microsoft malware classification challenge. arXiv preprint arXiv:1802.10135","author":"Ronen Royi","year":"2018","unstructured":"Royi Ronen , Marian Radu , Corina Feuerstein , Elad Yom-Tov , and Mansour Ahmadi . 2018. Microsoft malware classification challenge. arXiv preprint arXiv:1802.10135 ( 2018 ). Royi Ronen, Marian Radu, Corina Feuerstein, Elad Yom-Tov, and Mansour Ahmadi. 2018. Microsoft malware classification challenge. arXiv preprint arXiv:1802.10135 (2018)."},{"key":"e_1_3_2_1_46_1","volume-title":"Signature generation and detection of malware families","author":"Sathyanarayan V Sai","unstructured":"V Sai Sathyanarayan , Pankaj Kohli , and Bezawada Bruhadeshwar . 2008. Signature generation and detection of malware families . In ACISP. Springer , 336--349. V Sai Sathyanarayan, Pankaj Kohli, and Bezawada Bruhadeshwar. 2008. Signature generation and detection of malware families. In ACISP. Springer, 336--349."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC.2018.10315"},{"key":"e_1_3_2_1_48_1","volume-title":"Virustotal-free online virus, malware and url scanner. Online: https:\/\/www. virustotal. com\/en","author":"Total Virus","year":"2012","unstructured":"Virus Total . 2012. Virustotal-free online virus, malware and url scanner. Online: https:\/\/www. virustotal. com\/en ( 2012 ). Virus Total. 2012. Virustotal-free online virus, malware and url scanner. Online: https:\/\/www. virustotal. com\/en (2012)."},{"key":"e_1_3_2_1_49_1","unstructured":"Fengguo Wei Yuping Li Sankardas Roy Xinming Ou and Wu Zhou. 2017. Deep ground truth analysis of current android malware. In DIMVA.  Fengguo Wei Yuping Li Sankardas Roy Xinming Ou and Wu Zhou. 2017. Deep ground truth analysis of current android malware. In DIMVA."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"crossref","unstructured":"Hiromu Yakura Shinnosuke Shinozaki Reon Nishimura Yoshihiro Oyama and Jun Sakuma. 2018. Malware analysis of imaged binary samples by convolutional neural network with attention mechanism. In CODASPY. 127--134.  Hiromu Yakura Shinnosuke Shinozaki Reon Nishimura Yoshihiro Oyama and Jun Sakuma. 2018. Malware analysis of imaged binary samples by convolutional neural network with attention mechanism. In CODASPY. 127--134.","DOI":"10.1145\/3176258.3176335"},{"key":"e_1_3_2_1_51_1","volume-title":"Neural malware analysis with attention mechanism. Computers & Security","author":"Yakura Hiromu","year":"2019","unstructured":"Hiromu Yakura , Shinnosuke Shinozaki , Reon Nishimura , Yoshihiro Oyama , and Jun Sakuma . 2019. Neural malware analysis with attention mechanism. Computers & Security ( 2019 ). Hiromu Yakura, Shinnosuke Shinozaki, Reon Nishimura, Yoshihiro Oyama, and Jun Sakuma. 2019. Neural malware analysis with attention mechanism. Computers & Security (2019)."},{"key":"e_1_3_2_1_52_1","volume-title":"Malware obfuscation techniques: A brief survey","author":"You Ilsun","unstructured":"Ilsun You and Kangbin Yim . 2010. Malware obfuscation techniques: A brief survey . In BWCCA. IEEE , 297--300. Ilsun You and Kangbin Yim. 2010. Malware obfuscation techniques: A brief survey. In BWCCA. IEEE, 297--300."},{"key":"e_1_3_2_1_53_1","volume-title":"Imbalanced malware images classification: a CNN based approach. arXiv:1708.08042","author":"Yue Songqing","year":"2017","unstructured":"Songqing Yue . 2017. Imbalanced malware images classification: a CNN based approach. arXiv:1708.08042 ( 2017 ). Songqing Yue. 2017. Imbalanced malware images classification: a CNN based approach. arXiv:1708.08042 (2017)."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"crossref","unstructured":"Rui Zhao Wanli Ouyang Hongsheng Li and Xiaogang Wang. 2015. Saliency detection by multi-context deep learning. In CVPR. 1265--1274.  Rui Zhao Wanli Ouyang Hongsheng Li and Xiaogang Wang. 2015. Saliency detection by multi-context deep learning. In CVPR. 1265--1274.","DOI":"10.1109\/CVPR.2015.7298731"}],"event":{"name":"CIKM '22: The 31st ACM International Conference on Information and Knowledge Management","location":"Atlanta GA USA","acronym":"CIKM '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557533","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557533","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557533","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:08Z","timestamp":1750182668000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557533"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":54,"alternative-id":["10.1145\/3511808.3557533","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557533","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}