{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T07:43:07Z","timestamp":1772610187633,"version":"3.50.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1-2","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-21-1-2520"],"award-info":[{"award-number":["N00014-21-1-2520"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-21-1-2520"],"award-info":[{"award-number":["N00014-21-1-2520"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Hardw Syst Secur"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s41635-025-00163-z","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T06:02:20Z","timestamp":1752213740000},"page":"74-87","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Autonomous Hardware-Based Proactive Defenses with Deep Reinforcement Learning"],"prefix":"10.1007","volume":"9","author":[{"given":"Preet","family":"Derasari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guru","family":"Venkataramani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,11]]},"reference":[{"key":"163_CR1","doi-asserted-by":"crossref","unstructured":"Brumley D, Poosankam P, Song D, Zheng J (2008) Automatic patch-based exploit generation is possible: techniques and implications. In: 2008 IEEE Symposium on security and privacy (sp 2008), IEEE, pp 143\u2013157","DOI":"10.1109\/SP.2008.17"},{"key":"163_CR2","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s40012-016-0095-y","volume":"4","author":"A Malhotra","year":"2016","unstructured":"Malhotra A, Bajaj K (2016) A hybrid pattern based text mining approach for malware detection using DBScan. CSI Trans ICT 4:141\u2013149","journal-title":"CSI Trans ICT"},{"key":"163_CR3","doi-asserted-by":"crossref","unstructured":"Valea O, Opri\u015fa C (2020) Towards pentesting automation using the metasploit framework. In: 2020 IEEE 16th International conference on intelligent computer communication and processing (ICCP), IEEE, pp 171\u2013178","DOI":"10.1109\/ICCP51029.2020.9266234"},{"key":"163_CR4","doi-asserted-by":"crossref","unstructured":"Bilge L, Dumitra\u015f T (2012) Before we knew it: an empirical study of zero-day attacks in the real world. In: Proceedings of the 2012 ACM conference on computer and communications security, pp 833\u2013844","DOI":"10.1145\/2382196.2382284"},{"key":"163_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-02110-8","volume-title":"Autonomous cyber deception","author":"E Al-Shaer","year":"2019","unstructured":"Al-Shaer E, Wei J, Kevin W, Wang C (2019) Autonomous cyber deception. Springer"},{"issue":"1","key":"163_CR6","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1109\/COMST.2019.2963791","volume":"22","author":"J-H Cho","year":"2020","unstructured":"Cho J-H, Sharma DP, Alavizadeh H, Yoon S, Ben-Asher N, Moore TJ, Kim DS, Lim H, Nelson FF (2020) Toward proactive, adaptive defense: a survey on moving target defense. IEEE Commun Surv Tutorials 22(1):709\u2013745","journal-title":"IEEE Commun Surv Tutorials"},{"key":"163_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102288","volume":"106","author":"L Zhang","year":"2021","unstructured":"Zhang L, Thing VL (2021) Three decades of deception techniques in active cyber defense-retrospect and outlook. Elsevier Comput Sec 106:102288","journal-title":"Elsevier Comput Sec"},{"key":"163_CR8","doi-asserted-by":"crossref","unstructured":"Gallagher M, Biernacki L, Chen S, Aweke ZB, Yitbarek SF, Aga MT, Harris A, Xu Z, Kasikci B, Bertacco V, Malik S, Tiwari M, Austin T (2019) Morpheus: a vulnerability-tolerant secure architecture based on ensembles of moving target defenses with churn. In: ACM ASPLOS, pp 469\u2013484","DOI":"10.1145\/3297858.3304037"},{"key":"163_CR9","doi-asserted-by":"crossref","unstructured":"Derasari P, Gogineni K, Venkataramani G (2023) Mayavi: a cyber-deception hardware for memory load-stores. In: 33rd ACM GLSVLSI\u201923, pp 563\u2013568","DOI":"10.1145\/3583781.3590272"},{"key":"163_CR10","doi-asserted-by":"crossref","unstructured":"Derasari P, Gogineni K, Venkataramani G (2023) Mayalok: a cyber-deception hardware using runtime instruction infusion. In: 34th IEEE ASAP), pp 33\u201340","DOI":"10.1109\/ASAP57973.2023.00019"},{"key":"163_CR11","doi-asserted-by":"crossref","unstructured":"Derasari P, Venkataramani G (2024) Maya: hardware enhanced customizable defenses at the user-kernel interface. In: 2024 International symposium on secure and private execution environment design (SEED), pp 50\u201361","DOI":"10.1109\/SEED61283.2024.00016"},{"key":"163_CR12","first-page":"409","volume":"2024","author":"P Derasari","year":"2024","unstructured":"Derasari P, Venkataramani G (2024) EPIC: efficient and proactive instruction-level cyberdefense. Proceedings of the great lakes symposium on VLSI 2024:409\u2013414","journal-title":"Proceedings of the great lakes symposium on VLSI"},{"key":"163_CR13","doi-asserted-by":"crossref","unstructured":"Sajid MSI, Wei J, Abdeen B, Al-Shaer E, Islam MM, Diong W, Khan L (2021) Soda: a system for cyber deception orchestration and automation. In: ACSAC, pp 675\u2013689","DOI":"10.1145\/3485832.3485918"},{"key":"163_CR14","unstructured":"MITRE MITRE Engage. https:\/\/engage.mitre.org\/wp-content\/uploads\/2022\/04\/StarterKit-v1.0-1.pdf"},{"key":"163_CR15","unstructured":"Kharaz A, Arshad S, Mulliner C, Robertson W, Kirda E (2016) Unveil: a large-scale, automated approach to detecting ransomware. In: 25th USENIX Security Symposium, pp 757\u2013772"},{"issue":"6","key":"163_CR16","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1145\/1064978.1065034","volume":"40","author":"C-K Luk","year":"2005","unstructured":"Luk C-K, Cohn R, Muth R, Patil H, Klauser A, Lowney G, Wallace S, Reddi VJ, Hazelwood K (2005) Pin: building customized program analysis tools with dynamic instrumentation. Acm Sigplan Notices 40(6):190\u2013200","journal-title":"Acm Sigplan Notices"},{"issue":"7540","key":"163_CR17","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529\u2013533","journal-title":"Nature"},{"key":"163_CR18","doi-asserted-by":"crossref","unstructured":"Hirano M, Kobayashi R (2024) Ransmap: open dataset of ransomware storage and memory access patterns for creating deep learning based ransomware detectors. Comput Sec 104202","DOI":"10.1016\/j.cose.2024.104202"},{"key":"163_CR19","doi-asserted-by":"crossref","unstructured":"Araujo F, Hamlen KW, Biedermann S, Katzenbeisser S (2014) From patches to honey-patches: lightweight attacker misdirection, deception, and disinformation. In: ACM CCS, pp 942\u2013953","DOI":"10.1145\/2660267.2660329"},{"key":"163_CR20","first-page":"462","volume":"2","author":"MT Qassrawi","year":"2010","unstructured":"Qassrawi MT, Hongli Z (2010) Deception methodology in virtual honeypots. IEEE NSWCTC 2:462\u2013467","journal-title":"IEEE NSWCTC"},{"key":"163_CR21","doi-asserted-by":"publisher","first-page":"198285","DOI":"10.1109\/ACCESS.2020.3034443","volume":"8","author":"J Choi","year":"2020","unstructured":"Choi J, Lee H, Park Y, Kim HK, Lee J, Kim Y, Lee G, Shim S-W, Kim T (2020) PhantomFS-v2: dare you to avoid this trap. IEEE Access 8:198285\u2013198300","journal-title":"IEEE Access"},{"key":"163_CR22","doi-asserted-by":"crossref","unstructured":"Kc GS, Keromytis AD, Prevelakis V (2003) Countering code-injection attacks with instruction-set randomization. In: 10th ACM CCS, pp 272\u2013280","DOI":"10.1145\/948109.948146"},{"key":"163_CR23","volume-title":"Neuro-dynamic programming","author":"D Bertsekas","year":"1996","unstructured":"Bertsekas D (1996) Neuro-dynamic programming. Athena Scientific"},{"key":"163_CR24","first-page":"279","volume":"8","author":"CJ Watkins","year":"1992","unstructured":"Watkins CJ, Dayan P (1992) Q-learning. Mach Learn 8:279\u2013292","journal-title":"Mach Learn"},{"key":"163_CR25","unstructured":"Li Y (2017) Deep reinforcement learning: an overview. arXiv:1701.07274"},{"issue":"8","key":"163_CR26","doi-asserted-by":"publisher","first-page":"3779","DOI":"10.1109\/TNNLS.2021.3121870","volume":"34","author":"TT Nguyen","year":"2021","unstructured":"Nguyen TT, Reddi VJ (2021) Deep reinforcement learning for cyber security. IEEE Trans Neural Netw Learn Syst 34(8):3779\u20133795","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"163_CR27","doi-asserted-by":"crossref","unstructured":"Wang Y, Stokes JW, Marinescu M (2019) Neural malware control with deep reinforcement learning. In: MILCOM 2019-2019 IEEE military communications conference (MILCOM), IEEE, pp 1\u20138","DOI":"10.1109\/MILCOM47813.2019.9020862"},{"key":"163_CR28","doi-asserted-by":"crossref","unstructured":"Cavallaro L, Saxena P, Sekar R (2008) On the limits of information flow techniques for malware analysis and containment. In: International conference on detection of intrusions and malware, and vulnerability assessment, Springer, pp 143\u2013163","DOI":"10.1007\/978-3-540-70542-0_8"},{"key":"163_CR29","unstructured":"Costan V, Devadas S (2016) Intel SGX explained. Cryptology ePrint Archive"},{"key":"163_CR30","doi-asserted-by":"crossref","unstructured":"Gassend B, Clarke D, Van\u00a0Dijk M, Devadas S (2002) Silicon physical random functions. In: Proceedings of the 9th ACM conference on computer and communications security, pp 148\u2013160","DOI":"10.1145\/586110.586132"},{"issue":"2","key":"163_CR31","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/1353535.1346295","volume":"42","author":"J Devietti","year":"2008","unstructured":"Devietti J, Blundell C, Martin MM, Zdancewic S (2008) Hardbound: architectural support for spatial safety of the C programming language. ACM SIGOPS Operat Syst Rev 42(2):103\u2013114","journal-title":"ACM SIGOPS Operat Syst Rev"},{"issue":"2","key":"163_CR32","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1145\/1273440.1250722","volume":"35","author":"M Dalton","year":"2007","unstructured":"Dalton M, Kannan H, Kozyrakis C (2007) Raksha: a flexible information flow architecture for software security. ACM SIGARCH Comput Architecture News 35(2):482\u2013493","journal-title":"ACM SIGARCH Comput Architecture News"},{"key":"163_CR33","unstructured":"Simoiu C, Bonneau J, Gates C, Goel S (2019) \u201dI was told to buy a software or lose my computer. I ignored it\u201d: a study of ransomware. In: 15th SOUPS, pp 155\u2013174"},{"key":"163_CR34","doi-asserted-by":"crossref","unstructured":"Miramirkhani N, Appini MP, Nikiforakis N, Polychronakis M (2017) Spotless sandboxes: evading malware analysis systems using wear-and-tear artifacts. In: IEEE S &P, pp 1009\u20131024","DOI":"10.1109\/SP.2017.42"},{"key":"163_CR35","doi-asserted-by":"crossref","unstructured":"Bernaschi M, Gabrielli E, Mancini LV (2000) Operating system enhancements to prevent the misuse of system calls. In: Proceedings of the 7th ACM conference on computer and communications security, pp 174\u2013183","DOI":"10.1145\/352600.352624"},{"key":"163_CR36","doi-asserted-by":"crossref","unstructured":"Schultz MG, Eskin E, Zadok F, Stolfo SJ (2000) Data mining methods for detection of new malicious executables. In: Proceedings 2001 IEEE symposium on security and privacy. S &P 2001, IEEE, pp 38\u201349","DOI":"10.1109\/SECPRI.2001.924286"},{"key":"163_CR37","unstructured":"Kerrisk M Linux Programmer\u2019s Manual - Strace(1). https:\/\/man7.org\/linux\/man-pages\/man1\/strace.1.html"},{"key":"163_CR38","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1007\/s41635-017-0013-2","volume":"1","author":"J Lopez","year":"2017","unstructured":"Lopez J, Babun L, Aksu H, Uluagac AS (2017) A survey on function and system call hooking approaches. J Hardware Syst Sec 1:114\u2013136","journal-title":"J Hardware Syst Sec"},{"key":"163_CR39","unstructured":"Kerrisk M Linux Programmer\u2019s Manual - Syscall(2). https:\/\/man7.org\/linux\/man-pages\/man2\/syscall.2.html"},{"key":"163_CR40","doi-asserted-by":"crossref","unstructured":"Cox G, Dike C, Johnston D (2011) Intel\u2019s digital random number generator (DRNG). In: 23rd IEEE HCS, pp 1\u201313","DOI":"10.1109\/HOTCHIPS.2011.7477490"},{"key":"163_CR41","volume-title":"Platform firmware resiliency guidelines","author":"A Regenscheid","year":"2017","unstructured":"Regenscheid A (2017) Platform firmware resiliency guidelines. Technical report, National Institute of Standards and Technology"},{"key":"163_CR42","doi-asserted-by":"crossref","unstructured":"Alsaleh MN, Wei J, Al-Shaer E, Ahmed M (2019) Gextractor: automated extraction of malware deception parameters for autonomous cyber deception. In: Autonomous Cyber Deception, Springer, ???, pp 185\u2013207","DOI":"10.1007\/978-3-030-02110-8_10"},{"key":"163_CR43","doi-asserted-by":"crossref","unstructured":"Ozsoy M, Donovick C, Gorelik I, Abu-Ghazaleh N, Ponomarev D (2015) Malware-aware processors: a framework for efficient online malware detection. In: 21st IEEE HPCA, pp 651\u2013661","DOI":"10.1109\/HPCA.2015.7056070"},{"key":"163_CR44","doi-asserted-by":"crossref","unstructured":"Kazdagli M, Reddi VJ, Tiwari M (2016) Quantifying and improving the efficiency of hardware-based mobile malware detectors. In: 49th IEEE\/ACM MICRO, pp 1\u201313","DOI":"10.1109\/MICRO.2016.7783740"},{"issue":"5","key":"163_CR45","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/MSP.2011.98","volume":"9","author":"P O\u2019Kane","year":"2011","unstructured":"O\u2019Kane P, Sezer S, McLaughlin K (2011) Obfuscation: the hidden malware. IEEE Sec Privacy 9(5):41\u201347","journal-title":"IEEE Sec Privacy"},{"key":"163_CR46","unstructured":"VirusTotal VirusTotal. https:\/\/www.virustotal.com\/gui\/home\/upload"},{"issue":"2","key":"163_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2024716.2024718","volume":"39","author":"N Binkert","year":"2011","unstructured":"Binkert N, Beckmann B, Black G, Reinhardt SK, Saidi A, Basu A, Hestness J, Hower DR, Krishna T, Sardashti S, Sen R, Sewell K, Shoaib M, Vaish N, Hill MD, Wood DA (2011) The gem5 simulator. ACM SIGARCH Comput Architect News 39(2):1\u20137","journal-title":"ACM SIGARCH Comput Architect News"},{"key":"163_CR48","doi-asserted-by":"crossref","unstructured":"Aidan JS, Verma HK, Awasthi LK (2017) Comprehensive survey on petya ransomware attack. In: IEEE ICNGCIS, pp 122\u2013125","DOI":"10.1109\/ICNGCIS.2017.30"},{"key":"163_CR49","unstructured":"Arntz P (2019) CrySiS. https:\/\/www.malwarebytes.com\/blog\/news\/2019\/05\/threat-spotlight-crysis-aka-dharma-ransomware-causing-a-crisis-for-\/businesses"},{"key":"163_CR50","doi-asserted-by":"crossref","unstructured":"Hassan NA (2019) Ransomware families. In: Ransomware Revealed, Springer, ???, pp 47\u201368","DOI":"10.1007\/978-1-4842-4255-1_3"},{"key":"163_CR51","doi-asserted-by":"publisher","first-page":"28039","DOI":"10.1109\/ACCESS.2021.3058897","volume":"9","author":"FM Alotaibi","year":"2021","unstructured":"Alotaibi FM, Vassilakis VG (2021) Sdn-based detection of self-propagating ransomware: the case of badrabbit. IEEE Access 9:28039\u201328058","journal-title":"IEEE Access"},{"key":"163_CR52","unstructured":"Greenberg A (2018) The untold story of notpetya, the most devastating cyberattack in history. Wired, August 22"},{"key":"163_CR53","doi-asserted-by":"crossref","unstructured":"Chen Q, Bridges RA (2017) Automated behavioral analysis of malware: A case study of wannacry ransomware. In: 16th IEEE ICMLA, pp 454\u2013460","DOI":"10.1109\/ICMLA.2017.0-119"},{"key":"163_CR54","doi-asserted-by":"crossref","unstructured":"Kirsch J, Zhechev Z, Bierbaumer B, Kittel T (2018) PwIN\u2013Pwning Intel piN: why DBI is unsuitable for security applications. In: Computer Security: 23rd European Symposium on Research in Computer Security, ESORICS 2018, Barcelona, Spain, September 3-7, 2018, Proceedings, Part I 23, Springer, pp 363\u2013382","DOI":"10.1007\/978-3-319-99073-6_18"},{"key":"163_CR55","unstructured":"Zeiler MD (2012) Adadelta: an adaptive learning rate method. arXiv:1212.5701"},{"key":"163_CR56","doi-asserted-by":"crossref","unstructured":"Zhang Z (2018) Improved Adam optimizer for deep neural networks. In: 2018 IEEE\/ACM 26th international symposium on quality of service (IWQoS), Ieee, pp 1\u20132","DOI":"10.1109\/IWQoS.2018.8624183"},{"issue":"19","key":"163_CR57","first-page":"28","volume":"45","author":"B Deokar","year":"2012","unstructured":"Deokar B, Hazarnis A (2012) Intrusion detection system using log files and reinforcement learning. Int J Comput Appl 45(19):28\u201335","journal-title":"Int J Comput Appl"},{"key":"163_CR58","doi-asserted-by":"crossref","unstructured":"Xu X, Xie T (2005) A reinforcement learning approach for host-based intrusion detection using sequences of system calls.\u00a0In: International conference on intelligent computing, Springer, pp 995\u20131003","DOI":"10.1007\/11538059_103"},{"key":"163_CR59","doi-asserted-by":"publisher","first-page":"70700","DOI":"10.1109\/ACCESS.2021.3076599","volume":"9","author":"S Yoon","year":"2021","unstructured":"Yoon S, Cho J-H, Kim DS, Moore TJ, Free-Nelson F, Lim H (2021) Desolater: deep reinforcement learning-based resource allocation and moving target defense deployment framework. IEEE Access 9:70700\u201370714","journal-title":"IEEE Access"},{"key":"163_CR60","doi-asserted-by":"crossref","unstructured":"Charpentier A, Boulahia\u00a0Cuppens N, Cuppens F, Yaich R (2022) Deep reinforcement learning-based defense strategy selection. In: Proceedings of the 17th international conference on availability, reliability and security, pp 1\u201311","DOI":"10.1145\/3538969.3543789"},{"key":"163_CR61","doi-asserted-by":"crossref","unstructured":"Gogineni K, Derasari P, Venkataramani G (2022) Foreseer: efficiently forecasting malware event series with long short-term memory. In: IEEE SEED, pp 97\u2013108","DOI":"10.1109\/SEED55351.2022.00016"}],"container-title":["Journal of Hardware and Systems Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41635-025-00163-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41635-025-00163-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41635-025-00163-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T04:34:27Z","timestamp":1758688467000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41635-025-00163-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":61,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["163"],"URL":"https:\/\/doi.org\/10.1007\/s41635-025-00163-z","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-6779906\/v1","asserted-by":"object"}]},"ISSN":["2509-3428","2509-3436"],"issn-type":[{"value":"2509-3428","type":"print"},{"value":"2509-3436","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"30 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"The authors provide the consent to publish this work.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}