{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:10:17Z","timestamp":1758265817527},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030349790"},{"type":"electronic","value":"9783030349806"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-34980-6_30","type":"book-chapter","created":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T06:03:07Z","timestamp":1573452187000},"page":"268-281","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Subgraph-Based Adversarial Examples Against Graph-Based IoT Malware Detection Systems"],"prefix":"10.1007","author":[{"given":"Ahmed","family":"Abusnaina","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hisham","family":"Alasmary","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"Abuhamad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saeed","family":"Salem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"DaeHun","family":"Nyang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aziz","family":"Mohaisen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,11,11]]},"reference":[{"key":"30_CR1","unstructured":"Antonakakis, M., et al.: Understanding the Mirai Botnet. In: Proceedings of the 26th USENIX Security Symposium, pp. 1093\u20131110 (2017)"},{"key":"30_CR2","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/TSUSC.2018.2809665","volume":"4","author":"A Azmoodeh","year":"2019","unstructured":"Azmoodeh, A., Dehghantanha, A., Choo, K.-K.R.: Robust malware detection for Internet of (Battlefield) Things devices using deep eigenspace learning. IEEE Trans. Sustain. Comput. 4, 88\u201395 (2019)","journal-title":"IEEE Trans. Sustain. Comput."},{"key":"30_CR3","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.cose.2015.04.001","volume":"52","author":"A Mohaisen","year":"2015","unstructured":"Mohaisen, A., Alrawi, O., Mohaisen, M.: AMAL: high-fidelity, behavior-based automated malware analysis and classification. Comput. Secur. 52, 251\u2013266 (2015)","journal-title":"Comput. Secur."},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Alasmary, H., et al.: Analyzing and detecting emerging Internet of Things malware: a graph-based approach. IEEE Internet Things J. (2019)","DOI":"10.1109\/JIOT.2019.2925929"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Mohaisen, A., Yun, A., Kim, Y.: Measuring the mixing time of social graphs. In: ACM IMC, pp. 383\u2013389 (2010)","DOI":"10.1145\/1879141.1879191"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Mohaisen, A., Hopper, N., Kim, Y.: Keep your friends close: incorporating trust into social network-based Sybil defenses. In: Proceedings of the 30th IEEE International Conference on Computer Communications, INFOCOM, pp. 1943\u20131951 (2011)","DOI":"10.1109\/INFCOM.2011.5934998"},{"key":"30_CR7","unstructured":"Ian, C.S., Goodfellow, J., Shlens, J.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations, pp. 1\u201311 (2015)"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2574\u20132582 (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P.D., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: Proceedings of the IEEE European Symposium on Security and Privacy (EuroS&P), pp. 372\u2013387 (2016)","DOI":"10.1109\/EuroSP.2016.36"},{"key":"30_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1007\/978-3-319-66399-9_4","volume-title":"Computer Security \u2013 ESORICS 2017","author":"K Grosse","year":"2017","unstructured":"Grosse, K., Papernot, N., Manoharan, P., Backes, M., McDaniel, P.: Adversarial examples for malware detection. In: Foley, S.N., Gollmann, D., Snekkenes, E. (eds.) ESORICS 2017. LNCS, vol. 10493, pp. 62\u201379. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66399-9_4"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Abusnaina, A., Khormali, A., Alasmary, H., Park, J., Anwar, A., Mohaisen, A.: Adversarial learning attacks on graph-based IoT malware detection systems. In: 39th IEEE International Conference on Distributed Computing Systems, ICDCS (2019)","DOI":"10.1109\/ICDCS.2019.00130"},{"key":"30_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/978-3-319-20550-2_6","volume-title":"Detection of Intrusions and Malware, and Vulnerability Assessment","author":"T W\u00fcchner","year":"2015","unstructured":"W\u00fcchner, T., Ochoa, M., Pretschner, A.: Robust and effective malware detection through quantitative data flow graph metrics. In: Almgren, M., Gulisano, V., Maggi, F. (eds.) DIMVA 2015. LNCS, vol. 9148, pp. 98\u2013118. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-20550-2_6"},{"key":"30_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1007\/978-3-642-40203-6_10","volume-title":"Computer Security \u2013 ESORICS 2013","author":"D Caselden","year":"2013","unstructured":"Caselden, D., Bazhanyuk, A., Payer, M., McCamant, S., Song, D.: HI-CFG: construction by binary analysis and application to attack polymorphism. In: Crampton, J., Jajodia, S., Mayes, K. (eds.) ESORICS 2013. LNCS, vol. 8134, pp. 164\u2013181. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40203-6_10"},{"key":"30_CR14","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.cose.2014.10.011","volume":"48","author":"S Alam","year":"2015","unstructured":"Alam, S., Horspool, R.N., Traor\u00e9, I., Sogukpinar, I.: A framework for metamorphic malware analysis and real-time detection. Comput. Secur. 48, 212\u2013233 (2015)","journal-title":"Comput. Secur."},{"key":"30_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/11790754_8","volume-title":"Detection of Intrusions and Malware & Vulnerability Assessment","author":"D Bruschi","year":"2006","unstructured":"Bruschi, D., Martignoni, L., Monga, M.: Detecting self-mutating malware using control-flow graph matching. In: B\u00fcschkes, R., Laskov, P. (eds.) DIMVA 2006. LNCS, vol. 4064, pp. 129\u2013143. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11790754_8"},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Yan, J., Jin, D., Yan, G.: Classifying malware represented as control flow graphs using deep graph convolutional neural networks. In: IEEE\/IFIP DSN, pp. 1\u201312 (2019)","DOI":"10.1109\/DSN.2019.00020"},{"key":"30_CR17","unstructured":"Antonakakis, M., et al.: From throw-away traffic to bots: Detecting the rise of DGA-based malware. In: Proceedings of the 21th USENIX Security Symposium, pp. 491\u2013506 (2012)"},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. In: Proceedings of the IEEE Symposium on Security and Privacy, pp. 39\u201357 (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"Khormali, A., Abusnaina, A., Nyang, D., Yuksel, M., Mohaisen, A.: Examining the robustness of learning-based DDoS detection in software defined networks. In: Proceedings of the IEEE Conference on Dependable and Secure Computing, IDSC (2019)","DOI":"10.1109\/DSC47296.2019.8937669"},{"key":"30_CR20","unstructured":"Developers, Radare2 (2019). http:\/\/www.radare.org\/r\/"},{"issue":"5","key":"30_CR21","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1002\/sam.10084","volume":"3","author":"M Thoma","year":"2010","unstructured":"Thoma, M., et al.: Discriminative frequent subgraph mining with optimality guarantees. Stat. Anal. Data Min. 3(5), 302\u2013318 (2010)","journal-title":"Stat. Anal. Data Min."},{"key":"30_CR22","unstructured":"Yan, X., Han, J.: gSpan: graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining, pp. 721\u2013724 (2002)"},{"key":"30_CR23","unstructured":"Developers, Cyberiocs (2019). https:\/\/freeiocs.cyberiocs.pro\/"},{"key":"30_CR24","unstructured":"Github (2019). https:\/\/github.com\/"},{"key":"30_CR25","unstructured":"VirusTotal (2019). https:\/\/www.virustotal.com"},{"key":"30_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1007\/978-3-319-45719-2_11","volume-title":"Research in Attacks, Intrusions, and Defenses","author":"M Sebasti\u00e1n","year":"2016","unstructured":"Sebasti\u00e1n, M., Rivera, R., Kotzias, P., Caballero, J.: AVclass: a tool for massive malware labeling. In: Monrose, F., Dacier, M., Blanc, G., Garcia-Alfaro, J. (eds.) RAID 2016. LNCS, vol. 9854, pp. 230\u2013253. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-45719-2_11"}],"container-title":["Lecture Notes in Computer Science","Computational Data and Social Networks"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-34980-6_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T17:50:23Z","timestamp":1612288223000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-34980-6_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030349790","9783030349806"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-34980-6_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"11 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSoNet","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Data and Social Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"csonet2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/optnetsci.cise.ufl.edu\/CSoNet\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"120","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}