## 7 Conclusions and Future Work  
By studying the performance trade-offs across the state-of-the-art machine learners-based anti-ransomware tools experimentally, this paper affirms that they were computationally insufficient to classify 0-Day versions of different ransomware families on 40K corpora of data throughout in semi-realistic and realistic environments. They were still limited in static and dynamic analyses, set of traits to extract, type of ransomware to examine, the number of versions to learn, variety of ancestor ransomware families to attribute, multi-descent versions to recognize, the significant and imbalanced data stream to analyze, semi-realistic and realistic testbeds, decisive margins to prune, and inductive functions to adjust. Thus, this paper devotes a multi-tier ransomware streaming analytics model, which is empowered by a rich set of 24 static and dynamic traits, a novel machine learner, statistic, and numeral rates of family attribution and multi-descent family fusion those overlooked by the state-of-the-art anti-ransomware technologies. The proposed solution pursues four tiers of traits extraction, ransomware classification, ancestor family attribution, and multi-descent family fusion to discriminate ransomware versions from malware and good-ware versions. Experiments have qualified how the proposed solution enriches the accuracy, reduces the mistakes and misclassifications, and shortens the elapsed time versus escalating, big, lifelike, and imbalanced corpora of data. Overall results that averaged by 97% of accuracy rate, 2.4% of mistake rate, and 0.34% of miss rate; justify its maximal efficacy and cost-efficiency among its competitors.  
For future improvement, it is recommended to explore more distinctive traits to classify the imperviously seen ransomware families by investigating versions of other ransomware families. Herewith, 14 _crypto_-type, and _locker_-type ransomware families are investigated with 24 traits solely. Furthermore, a hybrid machine learner of other base machine learners rather than NB and DT could be designed as either a single-based learner or an ensemble-based learner for pivoting an ideal decision that could satisfy the aforesaid cases.  
Conceptualization, H.Z.; methodology, H.Z.; software, H.Z.; validation, H.Z. and A.S.; formal analysis, A.S., and O.K.; investigation, A.S. and O.K.; resources, H.Z.; data curation, H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, A.S. and O.K.; visualization, H.Z.; supervision, A.S. and O.K.; project administration, A.S. and O.K.; funding acquisition, A.S. and O.K. All authors have read and agreed to the published version of the manuscript.  
The Article Processing Charge was funded by the SPEV project 2102/2020, Faculty of Informatics and Management, University of Hradec Kralove.  
**Acknowledgments:** This research was partially funded by Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876, and the Fundamental Research Grant Scheme (FRGS) Vot 5F073 supported under the Ministry of Education Malaysia. The work is partially supported by the SPEV project, (ID: 2102-2020), \"Smart Solutions in Ubiquitous Computing Environments\" Faculty of Informatics and Management, University of Hradec Kralove. We are also grateful for the support of Ph.D. students [PERSON] and [PERSON] in consultations regarding application aspects from Hradec Kralove University, Czech Republic.  
**Conflicts of Interest:** The authors declare no conflict of interest.