{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:46:59Z","timestamp":1760147219239,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T00:00:00Z","timestamp":1673827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"JSPS KAKENHI","award":["19K11965","JP22K12028"],"award-info":[{"award-number":["19K11965","JP22K12028"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Self-propagating malware has been infecting thousands of IoT devices and causing security breaches worldwide. Mitigating and cleaning self-propagating malware is important but challenging because they propagate unpredictably. White-hat botnets have been used to combat self-propagating malware with the concept of fight fire-with-fire. However, white-hat botnets can also overpopulate and consume the resource of IoT devices. Later, lifespan was introduced as a self-destruct measure to restrain white-hat botnets\u2019 overpopulation, but unable to change based on real-time situations. This paper proposes a method for diffusing white-hat botnets by controlling lifespan. The main contribution of this paper is that the method uses a dynamic lifespan that increases and decreases based on the congregation\u2019s situation of the self-propagating malware and white-hat botnets. The method tackles the problem of overpopulation of white-hat botnets since they can self-propagate by controlling the ripple effect that widens the white-hat botnet\u2019s diffusion area but suppresses the number of white-hat botnets to achieve a \u2019zero-botnet\u2019 situation. The effectiveness in reducing the overpopulation rate was confirmed. The experiment result showed that the ripple effect could reduce the number of white-hat botnets in the network by around 80%, depending on different control parameters.<\/jats:p>","DOI":"10.3390\/s23021018","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T01:31:15Z","timestamp":1673832675000},"page":"1018","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Diffusion of White-Hat Botnet Using Lifespan with Controllable Ripple Effect for Malware Removal in IoT Networks"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0952-7151","authenticated-orcid":false,"given":"Mohd Anuaruddin","family":"Bin Ahmadon","sequence":"first","affiliation":[{"name":"Graduate School of Sciences and Technology for Innovation, Yamaguchi University, 2-16-1 Tokiwadai, Ube 755-8611, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0579-8501","authenticated-orcid":false,"given":"Shingo","family":"Yamaguchi","sequence":"additional","affiliation":[{"name":"Graduate School of Sciences and Technology for Innovation, Yamaguchi University, 2-16-1 Tokiwadai, Ube 755-8611, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,16]]},"reference":[{"key":"ref_1","unstructured":"Antonakakis, M., April, T., Bailey, M., Bernhard, M., Bursztein, E., Cochran, J., Durumeric, Z., Halderman, J.A., Invernizzi, L., and Kallitsis, M. (2017, January 16\u201318). Understanding the Mirai Botnet. Proceedings of the USENIX Security Symposium, Vancouver, BC, Canada."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Grammatikakis, K.P., Koufos, I., Kolokotronis, N., Vassilakis, C., and Shiaeles, S. (2021, January 26\u201328). Understanding and Mitigating Banking Trojans: From Zeus to Emotet. Proceedings of the 2021 IEEE International Conference on Cyber Security and Resilience (CSR), Rhodes, Greece.","DOI":"10.1109\/CSR51186.2021.9527960"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yamaguchi, S., and Gupta, B. (2022). Botnet Defense System and White-Hat Worm Launch Strategy in IoT Network. Advances in Malware and Data-Driven Network Security, IGI Global.","DOI":"10.4018\/978-1-7998-7789-9.ch008"},{"key":"ref_4","unstructured":"Donno, M.D., Dragoni, N., Giaretta, A., and Mazzara, M. (2016, January 10). AntibIoTic: Protecting IoT Devices Against DDoS Attacks. Proceedings of the International Conference on Software Engineering for Defence Applications, Rome, Italy."},{"key":"ref_5","unstructured":"(2022, December 27). 300,000 Obeying Devices: Hajime Is Conquering the Internet of Things World\u2014kaspersky.com. Available online: https:\/\/www.kaspersky.com\/about\/press-releases\/2017_300000-obeying-devices-hajime-is-conquering-the-internet-of-things-world."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yamaguchi, S. (2020). White-Hat Worm to Fight Malware and Its Evaluation by Agent-Oriented Petri Nets. Sensors, 20.","DOI":"10.37247\/PASen.1.2020.15"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yamaguchi, S. (2022). Botnet Defense System: Observability, Controllability, and Basic Command and Control Strategy. Sensors, 22.","DOI":"10.3390\/s22239423"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kapoor, A., Gupta, A., Gupta, R., Tanwar, S., Sharma, G., and Davidson, I.E. (2022). Ransomware Detection, Avoidance, and Mitigation Scheme: A Review and Future Directions. Sustainability, 14.","DOI":"10.3390\/su14010008"},{"key":"ref_9","first-page":"19","article-title":"Malware Detection and Mitigation Techniques: Lessons Learned from Mirai DDOS Attack","volume":"3","year":"2018","journal-title":"J. Inf. Syst. Eng. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1504\/IJWMC.2012.046776","article-title":"Detection and Prevention of Botnets and malware in an enterprise network","volume":"5","author":"Thakur","year":"2012","journal-title":"Int. J. Wirel. Mob. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1016\/j.future.2019.04.044","article-title":"Similarity hash based scoring of portable executable files for efficient malware detection in IoT","volume":"110","author":"Namanya","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103443","DOI":"10.1016\/j.csi.2020.103443","article-title":"Metamorphic malware identification using engine-specific patterns based on co-opcode graphs","volume":"71","author":"Kakisim","year":"2020","journal-title":"Comput. Stand. Interfaces"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1016\/j.compeleceng.2017.11.028","article-title":"Machine learning-assisted signature and heuristic-based detection of malwares in Android devices","volume":"69","author":"Rehman","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"101748","DOI":"10.1016\/j.cose.2020.101748","article-title":"Image-Based malware classification using ensemble of CNN architectures (IMCEC)","volume":"92","author":"Vasan","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"124579","DOI":"10.1109\/ACCESS.2020.3006143","article-title":"A Review of Android Malware Detection Approaches Based on Machine Learning","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"117083","DOI":"10.1016\/j.eswa.2022.117083","article-title":"HEAVEN: A Hardware-Enhanced AntiVirus ENgine to accelerate real-time, signature-based malware detection","volume":"201","author":"Botacin","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"163412","DOI":"10.1109\/ACCESS.2021.3131014","article-title":"A Two-Fold Machine Learning Approach to Prevent and Detect IoT Botnet Attacks","volume":"9","author":"Hussain","year":"2021","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/MSP.2018.1331034","article-title":"Defending from Stealthy Botnets Using Moving Target Defenses","volume":"16","author":"Albanese","year":"2018","journal-title":"IEEE Secur. Priv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Amich, A., and Eshete, B. (2021, January 6\u201310). Morphence: Moving Target Defense Against Adversarial Examples. Proceedings of the Annual Computer Security Applications Conference, Virtual Event.","DOI":"10.1145\/3485832.3485899"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hwang, S.Y., and Kim, J.N. (2021). A Malware Distribution Simulator for the Verification of Network Threat Prevention Tools. Sensors, 21.","DOI":"10.3390\/s21216983"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"164200","DOI":"10.1109\/ACCESS.2020.3022272","article-title":"eMUD: Enhanced Manufacturer Usage Description for IoT Botnets Prevention on Home WiFi Routers","volume":"8","author":"Sajjad","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"126023","DOI":"10.1109\/ACCESS.2021.3104260","article-title":"Offensive Security: Towards Proactive Threat Hunting via Adversary Emulation","volume":"9","author":"Ajmal","year":"2021","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"182309","DOI":"10.1109\/ACCESS.2019.2960398","article-title":"Botnet Vulnerability Intelligence Clustering Classification Mining and Countermeasure Algorithm Based on Machine Learning","volume":"7","author":"Chu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102169","DOI":"10.1016\/j.simpat.2020.102169","article-title":"Model checking and machine learning techniques for HummingBad mobile malware detection and mitigation","volume":"105","author":"Martinelli","year":"2020","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_25","unstructured":"Kepner, J., Bernays, J., Buckley, S., Cho, K., Conrad, C., Daigle, L., Erhardt, K., Gadepally, V., Greene, B., and Jones, M. (2022). Zero Botnets: An Observe-Pursue-Counter Approach. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yamaguchi, S. (2020). Botnet Defense System: Concept, Design, and Basic Strategy. Information, 11.","DOI":"10.3390\/info11110516"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pan, X., and Yamaguchi, S. (2022). Machine Learning White-Hat Worm Launcher for Tactical Response by Zoning in Botnet Defense System. Sensors, 22.","DOI":"10.3390\/s22134666"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"102687","DOI":"10.1016\/j.cose.2022.102687","article-title":"On the vulnerability of anti-malware solutions to DNS attacks","volume":"116","author":"Nadler","year":"2022","journal-title":"Comput. Secur."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wichmann, A., and Gerhards-Padilla, E. (2012, January 20\u201323). Using Infection Markers as a Vaccine against Malware Attacks. Proceedings of the 2012 IEEE International Conference on Green Computing and Communications, Besancon, France.","DOI":"10.1109\/GreenCom.2012.121"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kouliaridis, V., and Kambourakis, G. (2021). A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection. Information, 12.","DOI":"10.3390\/info12050185"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Moussas, V., and Andreatos, A. (2021). Malware Detection Based on Code Visualization and Two-Level Classification. Information, 12.","DOI":"10.3390\/info12030118"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"102622","DOI":"10.1016\/j.cose.2022.102622","article-title":"EfficientNet convolutional neural networks-based Android malware detection","volume":"115","author":"Yadav","year":"2022","journal-title":"Comput. Secur."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"138508","DOI":"10.1109\/ACCESS.2020.3011919","article-title":"Cognitive and Scalable Technique for Securing IoT Networks Against Malware Epidemics","volume":"8","author":"Dinakarrao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJSSCI.291713","article-title":"Machine-Learning-Based White-Hat Worm Launcher in Botnet Defense System","volume":"14","author":"Pan","year":"2022","journal-title":"Int. J. Softw. Sci. Comput. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Thanh Vu, S.N., Stege, M., El-Habr, P.I., Bang, J., and Dragoni, N. (2021). A Survey on Botnets: Incentives, Evolution, Detection and Current Trends. Future Internet, 13.","DOI":"10.3390\/fi13080198"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"228818","DOI":"10.1109\/ACCESS.2020.3044277","article-title":"Stochastic Modeling of IoT Botnet Spread: A Short Survey on Mobile Malware Spread Modeling","volume":"8","author":"Mahboubi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","unstructured":"Healey, J. (2018). Zero Botnets: Building a Global Effort to Clean Up the Internet, Council on Foreign Relations."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bin Ahmadon, M.A., and Yamaguchi, S. (2022, January 7\u20139). Evaluation on White-Hat Worm Diffusion Method Based on The Evolution of Its Lifespan in Wireless Networks. Proceedings of the 2022 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE53296.2022.9730312"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.ejor.2020.10.036","article-title":"To clean or not to clean: Malware removal strategies for servers under load","volume":"292","author":"Doroudi","year":"2021","journal-title":"Eur. J. Oper. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cosrev.2019.01.002","article-title":"A Survey on malware analysis and mitigation techniques","volume":"32","author":"Sangeetha","year":"2019","journal-title":"Comput. Sci. Rev."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/1018\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:06:58Z","timestamp":1760119618000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/1018"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,16]]},"references-count":40,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23021018"],"URL":"https:\/\/doi.org\/10.3390\/s23021018","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,1,16]]}}}