{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T07:55:27Z","timestamp":1768031727146,"version":"3.49.0"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031565793","type":"print"},{"value":"9783031565809","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-56580-9_12","type":"book-chapter","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T05:02:23Z","timestamp":1712034143000},"page":"193-211","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Decoding HDF5: Machine Learning File Forensics and\u00a0Data Injection"],"prefix":"10.1007","author":[{"given":"Clinton","family":"Walker","sequence":"first","affiliation":[]},{"given":"Ibrahim","family":"Baggili","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,3]]},"reference":[{"key":"12_CR1","unstructured":"CVE-2016-4330. Available from MITRE, CVE-ID CVE-2016-4330. (2016). https:\/\/cve.mitre.org\/cgi-bin\/cvename.cgi?name=CVE-2016-4330"},{"key":"12_CR2","unstructured":"CVE-2016-4331. Available from MITRE, CVE-ID CVE-2016-4331. (2016). https:\/\/cve.mitre.org\/cgi-bin\/cvename.cgi?name=CVE-2016-4331"},{"key":"12_CR3","unstructured":"CVE-2016-4332. Available from MITRE, CVE-ID CVE-2016-4332. (2016). https:\/\/cve.mitre.org\/cgi-bin\/cvename.cgi?name=CVE-2016-4332"},{"key":"12_CR4","unstructured":"CVE-2016-4333. Available from MITRE, CVE-ID CVE-2016-4333. (2016). https:\/\/cve.mitre.org\/cgi-bin\/cvename.cgi?name=CVE-2016-4333"},{"key":"12_CR5","unstructured":"CVE-2022-25942. Available from MITRE, CVE-ID CVE-2022-25942. (2022). https:\/\/cve.mitre.org\/cgi-bin\/cvename.cgi?name=CVE-2022-25942"},{"key":"12_CR6","unstructured":"CVE-2022-25972. Available from MITRE, CVE-ID CVE-2022-25972 (2022). https:\/\/cve.mitre.org\/cgi-bin\/cvename.cgi?name=CVE-2022-25972"},{"key":"12_CR7","unstructured":"CVE-2022-26061. Available from MITRE, CVE-ID CVE-2022-26061. (2022). https:\/\/cve.mitre.org\/cgi-bin\/cvename.cgi?name=CVE-2022-26061"},{"key":"12_CR8","unstructured":"Researchers weaponize machine learning models with ransomware, December 2022. https:\/\/www.technewsworld.com\/story\/researchers-weaponize-machine-learning-models-with-ransomware-177489.html"},{"key":"12_CR9","unstructured":"Hdf5 cves. Available from MITRE (2023). https:\/\/cve.mitre.org\/cgi-bin\/cvekey.cgi?keyword=HDF5"},{"key":"12_CR10","unstructured":"Hdf5 for python (2023). https:\/\/docs.h5py.org\/en\/stable\/"},{"key":"12_CR11","unstructured":"Using tensorflow securely (2023). https:\/\/github.com\/tensorflow\/tensorflow\/blob\/master\/SECURITY.md"},{"issue":"3","key":"12_CR12","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1109\/COMST.2020.2988293","volume":"22","author":"MA Al-Garadi","year":"2020","unstructured":"Al-Garadi, M.A., Mohamed, A., Al-Ali, A.K., Du, X., Ali, I., Guizani, M.: A survey of machine and deep learning methods for internet of things (iot) security. IEEE Commun. Surv. Tutor. 22(3), 1646\u20131685 (2020). https:\/\/doi.org\/10.1109\/COMST.2020.2988293","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Apruzzese, G., Colajanni, M., Ferretti, L., Guido, A., Marchetti, M.: On the effectiveness of machine and deep learning for cyber security. In: 2018 10th International Conference on Cyber Conflict (CyCon), pp. 371\u2013390 (2018). https:\/\/doi.org\/10.23919\/CYCON.2018.8405026","DOI":"10.23919\/CYCON.2018.8405026"},{"key":"12_CR14","unstructured":"Arp, D., et al.: Dos and don\u2019ts of machine learning in computer security. In: 31st USENIX Security Symposium (USENIX Security 22), pp. 3971\u20133988. USENIX Association, Boston, MA, August 2022. https:\/\/www.usenix.org\/conference\/usenixsecurity22\/presentation\/arp"},{"key":"12_CR15","doi-asserted-by":"publisher","unstructured":"Berman, D.S., Buczak, A.L., Chavis, J.S., Corbett, C.L.: A survey of deep learning methods for cyber security. Information 10(4) (2019). https:\/\/doi.org\/10.3390\/info10040122, https:\/\/www.mdpi.com\/2078-2489\/10\/4\/122","DOI":"10.3390\/info10040122"},{"key":"12_CR16","doi-asserted-by":"publisher","unstructured":"Cui, L., Yang, S., Chen, F., Ming, Z., Lu, N., Qin, J.: A survey on application of machine learning for internet of things. Int. J. Mach. Learn. Cybern. 9(8), 1399\u20131417 (2018). https:\/\/doi.org\/10.1007\/s13042-018-0834-5","DOI":"10.1007\/s13042-018-0834-5"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Ferrag, M.A., Maglaras, L., Moschoyiannis, S., Janicke, H.: Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J. Inf. Secur. Appl. 50, 102419 (2020)","DOI":"10.1016\/j.jisa.2019.102419"},{"key":"12_CR18","doi-asserted-by":"publisher","unstructured":"Folk, M., Heber, G., Koziol, Q., Pourmal, E., Robinson, D.: An overview of the hdf5 technology suite and its applications. In: Proceedings of the EDBT\/ICDT 2011 Workshop on Array Databases, pp. 36\u201347. AD \u201911, Association for Computing Machinery, New York, NY, USA (2011). https:\/\/doi.org\/10.1145\/1966895.1966900","DOI":"10.1145\/1966895.1966900"},{"key":"12_CR19","doi-asserted-by":"publisher","unstructured":"Goldblum, M., et al.: Dataset security for machine learning: data poisoning, backdoor attacks, and defenses. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2022.3162397","DOI":"10.1109\/TPAMI.2022.3162397"},{"key":"12_CR20","doi-asserted-by":"publisher","first-page":"24411","DOI":"10.1109\/ACCESS.2018.2830661","volume":"6","author":"WG Hatcher","year":"2018","unstructured":"Hatcher, W.G., Yu, W.: A survey of deep learning: platforms, applications and emerging research trends. IEEE Access 6, 24411\u201324432 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2830661","journal-title":"IEEE Access"},{"issue":"5","key":"12_CR21","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1109\/TSE.2020.3034721","volume":"48","author":"Y He","year":"2022","unstructured":"He, Y., Meng, G., Chen, K., Hu, X., He, J.: Towards security threats of deep learning systems: a survey. IEEE Trans. Softw. Eng. 48(5), 1743\u20131770 (2022). https:\/\/doi.org\/10.1109\/TSE.2020.3034721","journal-title":"IEEE Trans. Softw. Eng."},{"key":"12_CR22","doi-asserted-by":"publisher","unstructured":"Huang, S., Papernot, N., Goodfellow, I., Duan, Y., Abbeel, P.: Adversarial attacks on neural network policies (2017). https:\/\/doi.org\/10.48550\/ARXIV.1702.02284, https:\/\/arxiv.org\/abs\/1702.02284","DOI":"10.48550\/ARXIV.1702.02284"},{"key":"12_CR23","doi-asserted-by":"publisher","unstructured":"Karatas, G., Demir, O., Koray Sahingoz, O.: Deep learning in intrusion detection systems. In: 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), pp. 113\u2013116 (2018). https:\/\/doi.org\/10.1109\/IBIGDELFT.2018.8625278","DOI":"10.1109\/IBIGDELFT.2018.8625278"},{"key":"12_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-030-25540-4_26","volume-title":"Computer Aided Verification","author":"G Katz","year":"2019","unstructured":"Katz, G., et al.: The marabou framework for verification and analysis of deep neural networks. In: Dillig, I., Tasiran, S. (eds.) CAV 2019. LNCS, vol. 11561, pp. 443\u2013452. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-25540-4_26"},{"key":"12_CR25","unstructured":"Koziol, Q., Robinson, D., of Science, U.O.: HDF5, March 2018. https:\/\/doi.org\/10.11578\/dc.20180330.1, https:\/\/www.osti.gov\/\/servlets\/purl\/1631295"},{"key":"12_CR26","doi-asserted-by":"publisher","unstructured":"Li, Y., Li, Y., Wu, B., Li, L., He, R., Lyu, S.: Invisible backdoor attack with sample-specific triggers (2020). https:\/\/doi.org\/10.48550\/ARXIV.2012.03816, https:\/\/arxiv.org\/abs\/2012.03816","DOI":"10.48550\/ARXIV.2012.03816"},{"key":"12_CR27","doi-asserted-by":"publisher","unstructured":"Ling, X., et al.: Deepsec: a uniform platform for security analysis of deep learning model. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 673\u2013690 (2019). https:\/\/doi.org\/10.1109\/SP.2019.00023","DOI":"10.1109\/SP.2019.00023"},{"key":"12_CR28","doi-asserted-by":"publisher","unstructured":"Liu, H., Lang, B.: Machine learning and deep learning methods for intrusion detection systems: a survey. Appl. Sci. 9(20) (2019). https:\/\/doi.org\/10.3390\/app9204396, https:\/\/www.mdpi.com\/2076-3417\/9\/20\/4396","DOI":"10.3390\/app9204396"},{"key":"12_CR29","doi-asserted-by":"publisher","first-page":"20717","DOI":"10.1109\/ACCESS.2021.3054129","volume":"9","author":"AB Nassif","year":"2021","unstructured":"Nassif, A.B., Talib, M.A., Nasir, Q., Albadani, H., Dakalbab, F.M.: Machine learning for cloud security: a systematic review. IEEE Access 9, 20717\u201320735 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3054129","journal-title":"IEEE Access"},{"key":"12_CR30","doi-asserted-by":"publisher","first-page":"19696","DOI":"10.1109\/ACCESS.2020.2968718","volume":"8","author":"G Nguyen","year":"2020","unstructured":"Nguyen, G., Dlugolinsky, S., Tran, V., Lopez Garcia, A.: Deep learning for proactive network monitoring and security protection. IEEE Access 8, 19696\u201319716 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2968718","journal-title":"IEEE Access"},{"key":"12_CR31","doi-asserted-by":"publisher","unstructured":"Papernot, N., McDaniel, P., Sinha, A., Wellman, M.P.: Sok: security and privacy in machine learning. In: 2018 IEEE European Symposium on Security and Privacy (EuroS &P), pp. 399\u2013414 (2018). https:\/\/doi.org\/10.1109\/EuroSP.2018.00035","DOI":"10.1109\/EuroSP.2018.00035"},{"key":"12_CR32","doi-asserted-by":"publisher","unstructured":"Poisel, R., Tjoa, S.: A comprehensive literature review of file carving. In: 2013 International Conference on Availability, Reliability and Security, pp. 475\u2013484 (2013). https:\/\/doi.org\/10.1109\/ARES.2013.62","DOI":"10.1109\/ARES.2013.62"},{"key":"12_CR33","unstructured":"Rojas, E., Kahira, A.N., Meneses, E., Bautista-Gomez, L., Badia, R.M.: A study of checkpointing in large scale training of deep neural networks. CoRR abs\/2012.00825 (2020). https:\/\/arxiv.org\/abs\/2012.00825"},{"key":"12_CR34","doi-asserted-by":"publisher","unstructured":"Salem, A., Wen, R., Backes, M., Ma, S., Zhang, Y.: Dynamic backdoor attacks against machine learning models. In: 2022 IEEE 7th European Symposium on Security and Privacy (EuroS &P), pp. 703\u2013718 (2022). https:\/\/doi.org\/10.1109\/EuroSP53844.2022.00049","DOI":"10.1109\/EuroSP53844.2022.00049"},{"key":"12_CR35","doi-asserted-by":"publisher","unstructured":"Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., Rellermeyer, J.S.: A survey on distributed machine learning. ACM Comput. Surv. 53(2) (2020). https:\/\/doi.org\/10.1145\/3377454","DOI":"10.1145\/3377454"},{"key":"12_CR36","doi-asserted-by":"publisher","unstructured":"Wang, J., Hassan, G.M., Akhtar, N.: A survey of neural trojan attacks and defenses in deep learning (2022). https:\/\/doi.org\/10.48550\/ARXIV.2202.07183, https:\/\/arxiv.org\/abs\/2202.07183","DOI":"10.48550\/ARXIV.2202.07183"},{"key":"12_CR37","doi-asserted-by":"publisher","unstructured":"Wei, Y., Zheng, N., Xu, M.: An automatic carving method for RAR file based on content and structure. In: 2010 Second International Conference on Information Technology and Computer Science, pp. 68\u201372 (2010). https:\/\/doi.org\/10.1109\/ITCS.2010.23","DOI":"10.1109\/ITCS.2010.23"},{"key":"12_CR38","doi-asserted-by":"publisher","unstructured":"Xiao, Q., Li, K., Zhang, D., Xu, W.: Security risks in deep learning implementations. In: 2018 IEEE Security and Privacy Workshops (SPW), pp. 123\u2013128 (2018). https:\/\/doi.org\/10.1109\/SPW.2018.00027","DOI":"10.1109\/SPW.2018.00027"},{"key":"12_CR39","doi-asserted-by":"publisher","first-page":"74720","DOI":"10.1109\/ACCESS.2020.2987435","volume":"8","author":"M Xue","year":"2020","unstructured":"Xue, M., Yuan, C., Wu, H., Zhang, Y., Liu, W.: Machine learning security: threats, countermeasures, and evaluations. IEEE Access 8, 74720\u201374742 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2987435","journal-title":"IEEE Access"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Digital Forensics and Cyber Crime"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56580-9_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T05:08:36Z","timestamp":1712034516000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56580-9_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031565793","9783031565809"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56580-9_12","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDF2C","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Digital Forensics and Cyber Crime","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New York, NY","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdf2c2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"105","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":"41","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":"0","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":"39% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}