{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T18:57:58Z","timestamp":1770231478582,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819698714","type":"print"},{"value":"9789819698721","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-9872-1_3","type":"book-chapter","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T14:34:54Z","timestamp":1753281294000},"page":"27-38","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RanHunter: Advancing Ransomware Detection with Channel Attention and Multi-head Attention"],"prefix":"10.1007","author":[{"given":"Zhilu","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peinan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingbo","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengkai","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"3_CR1","unstructured":"TechTarget Homepage. https:\/\/www.techtarget.com\/searchsecurity\/news\/366617564\/10-of-the-biggest-ransomware-attacks-in-2024. Accessed 30 Dec 2024"},{"key":"3_CR2","unstructured":"Kaspersky Homepage. https:\/\/www.kaspersky.com\/blog\/ransowmare-attacks-in-2024\/5294. Accessed 31 Jan 2024"},{"key":"3_CR3","unstructured":"SANGFOR Homepage. https:\/\/www.sangfor.com\/blog\/cybersecurity\/ransomware-attacks-2024-top-ransomware-headlines. Accessed 19 Jan 2024"},{"key":"3_CR4","unstructured":"SentinelOne Homepage. https:\/\/www.sentinelone.com\/cybersecurity-101\/threat-intelligence\/what-is-double-extortion\/. Accessed 2 May 2024"},{"key":"3_CR5","unstructured":"DARKTRACE Homepage. https:\/\/www.darktrace.com\/blog\/double-extortion-ransomware\/. Accessed 18 May 2024"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Ayub, M.A., Continella, A., Siraj, A.: An i\/o request packet (IRP) driven effective ransomware detection scheme using artificial neural network. In: Proceedings of 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), pp. 319\u2013324 (2020)","DOI":"10.1109\/IRI49571.2020.00053"},{"issue":"3","key":"3_CR7","doi-asserted-by":"publisher","first-page":"337","DOI":"10.3233\/JCS-191346","volume":"28","author":"B Jethva","year":"2020","unstructured":"Jethva, B., Traor\u00e9, I., Ghaleb, A., et al.: Multilayer ransomware detection using grouped registry key operations, file entropy and file signature monitoring. J. Comput. Secur. 28(3), 337\u2013373 (2020)","journal-title":"J. Comput. Secur."},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Baek, S., Jung, Y., Mohaisen, A., et al.: SSD-insider: internal defense of solid-state drive against ransomware with perfect data recovery. In: Proceedings of 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). pp. 875\u2013884 (2018)","DOI":"10.1109\/ICDCS.2018.00089"},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"169944","DOI":"10.1109\/ACCESS.2020.3023764","volume":"8","author":"SS Chakkaravarthy","year":"2020","unstructured":"Chakkaravarthy, S.S., Sangeetha, D., Cruz, M.V., et al.: Design of intrusion detection honeypot using social leopard algorithm to detect IoT ransomware attacks. IEEE Access 8, 169944\u2013169956 (2020)","journal-title":"IEEE Access"},{"issue":"14","key":"3_CR10","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5726","volume":"32","author":"C Keong Ng","year":"2020","unstructured":"Keong Ng, C., Rajasegarar, S., Pan, L., et al.: Voterchoice: a ransomware detection honeypot with multiple voting framework. Concurr. Comput.: Pract. Exper. 32(14), e5726 (2020)","journal-title":"Concurr. Comput.: Pract. Exper."},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Medhat, M., Gaber, S., Abdelbaki, N.: A new static-based framework for ransomware detection. In: Proceedings of 2018 IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC\/PiCom\/DataCom\/CyberSciTech), pp. 710\u2013715 (2018)","DOI":"10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.00124"},{"key":"3_CR12","unstructured":"Kharaz, A., Arshad, S., Mulliner, C., et al.: {UNVEIL}: A {Large-Scale}, automated approach to detecting ransomware. In: Proceedings of 25th USENIX security symposium (USENIX Security 16), pp. 757\u2013772 (2016)"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Chen, Z.G., Kang, H.S., Yin, S.N., et al.: Automatic ransomware detection and analysis based on dynamic API calls flow graph. In: Proceedings of the International Conference on Research in Adaptive and Convergent Systems, pp. 196\u2013201 (2017)","DOI":"10.1145\/3129676.3129704"},{"key":"3_CR14","first-page":"93","volume":"70","author":"OM Alhawi","year":"2018","unstructured":"Alhawi, O.M., Baldwin, J., Dehghantanha, A.: Leveraging machine learning techniques for windows ransomware network traffic detection. ADIS 70, 93\u2013106 (2018)","journal-title":"ADIS"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Huang, J., Xu, J., Xing, X., et al.: Flashguard: leveraging intrinsic flash properties to defend against encryption ransomware. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 2231\u20132244 (2017)","DOI":"10.1145\/3133956.3134035"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Reidys, B., Liu, P., Huang, J.: RSSD: defend against ransomware with hardwareisolated network-storage codesign and post-attack analysis. In: Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 726\u2013739 (2022)","DOI":"10.1145\/3503222.3507773"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Ma, B., Yang, Y., Li, J., et al.: Travelling the hypervisor and SSD: a tag-based approach against crypto ransomware with fine-grained data recovery. In: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, pp. 341\u2013355(2023)","DOI":"10.1145\/3576915.3616665"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Moore, C.: Detecting ransomware with honeypot techniques. In: Proceedings of 2016 Cybersecurity and Cyberforensics Conference (CCC), pp. 77\u201381 (2016)","DOI":"10.1109\/CCC.2016.14"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Lee, J., Lee, J., Hong, J.: How to make efficient decoy files for ransomware detection? In: Proceedings of the International Conference on Research in Adaptive and Convergent Systems, pp. 208\u2013212 (2017)","DOI":"10.1145\/3129676.3129713"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Moussaileb, R., Bouget, B., Palisse, A., et al.: Ransomware\u2019s early mitigation mechanisms. In: Proceedings of the 13th International Conference on Availability, Reliability and Security, pp. 1\u201310 (2018)","DOI":"10.1145\/3230833.3234691"},{"issue":"11","key":"3_CR21","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.1109\/TC.2020.3011748","volume":"69","author":"D Meng","year":"2020","unstructured":"Meng, D., Hou, R., Shi, G., et al.: Built-in security computer: deploying security-first architecture using active security processor. IEEE Trans. Comput. 69(11), 1571\u20131583 (2020)","journal-title":"IEEE Trans. Comput."},{"key":"3_CR22","unstructured":"VirusTotal.: Analyze suspicious files and urls to detect types of malware (2021). https:\/\/www.virustotal.com"},{"key":"3_CR23","unstructured":"VirusShare.: Virusshare.com - because sharing is caring (2021). https:\/\/virusshare.com"},{"key":"3_CR24","unstructured":"MalwareBazaar.: Malwarebazaar database (2021). https:\/\/bazaar.abuse.ch\/browse\/"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Continella, A., Guagnelli, A., Zingaro, G., et al.: Shieldfs: a self-healing, ransomware-aware filesystem. In: Proceedings of the 32nd Annual Conference on Computer Security Applications. pp. 336\u2013347 (2016)","DOI":"10.1145\/2991079.2991110"},{"issue":"3","key":"3_CR26","first-page":"600","volume":"72","author":"GO Ganfure","year":"2022","unstructured":"Ganfure, G.O., Wu, C.F., Chang, Y.H., et al.: Deepware: imaging performance counters with deep learning to detect ransomware. IEEE Trans. Comput. 72(3), 600\u2013613 (2022)","journal-title":"IEEE Trans. Comput."},{"key":"3_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103703","volume":"139","author":"S Gulmez","year":"2024","unstructured":"Gulmez, S., Kakisim, A.G., Sogukpinar, I.: Xran: explainable deep learning-based ransomware detection using dynamic analysis. Comput. Secur. 139, 103703 (2024)","journal-title":"Comput. Secur."},{"key":"3_CR28","doi-asserted-by":"publisher","first-page":"6113","DOI":"10.1109\/TIFS.2024.3410511","volume":"19","author":"H Zhang","year":"2024","unstructured":"Zhang, H., Zhao, L., Yu, A., et al.: Ranker: Early ransomware detection through kernel-level behavioral analysis. IEEE Trans. Inf. Forensics Secur. 19, 6113\u20136127 (2024)","journal-title":"IEEE Trans. Inf. Forensics Secur."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9872-1_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T06:22:56Z","timestamp":1770186176000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9872-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698714","9789819698721"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9872-1_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}