{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T17:21:40Z","timestamp":1776014500884,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, cybersecurity has been strengthened through the adoption of processes, mechanisms and rapid sources of indicators of compromise in critical areas. Among the most latent challenges are the detection, classification and eradication of malware and Denial of Service Cyber-Attacks (DoS). The literature has presented different ways to obtain and evaluate malware- and DoS-cyber-attack-related instances, either from a technical point of view or by offering ready-to-use datasets. However, acquiring fresh, up-to-date samples requires an arduous process of exploration, sandbox configuration and mass storage, which may ultimately result in an unbalanced or under-represented set. Synthetic sample generation has shown that the cost associated with setting up controlled environments and time spent on sample evaluation can be reduced. Nevertheless, the process is performed when the observations already belong to a characterized set, totally detached from a real environment. In order to solve the aforementioned, this work proposes a methodology for the generation of synthetic samples of malicious Portable Executable binaries and DoS cyber-attacks. The task is performed via a Reinforcement Learning engine, which learns from a baseline of different malware families and DoS cyber-attack network properties, resulting in new, mutated and highly functional samples. Experimental results demonstrate the high adaptability of the outputs as new input datasets for different Machine Learning algorithms.<\/jats:p>","DOI":"10.3390\/s23031231","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T01:36:26Z","timestamp":1674437786000},"page":"1231","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4867-2717","authenticated-orcid":false,"given":"Aldo","family":"Hernandez-Suarez","sequence":"first","affiliation":[{"name":"Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriel","family":"Sanchez-Perez","sequence":"additional","affiliation":[{"name":"Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9555-4705","authenticated-orcid":false,"given":"Linda K.","family":"Toscano-Medina","sequence":"additional","affiliation":[{"name":"Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7786-2050","authenticated-orcid":false,"given":"Hector","family":"Perez-Meana","sequence":"additional","affiliation":[{"name":"Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0337-5364","authenticated-orcid":false,"given":"Jesus","family":"Olivares-Mercado","sequence":"additional","affiliation":[{"name":"Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8863-7804","authenticated-orcid":false,"given":"Jose","family":"Portillo-Portillo","sequence":"additional","affiliation":[{"name":"Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4945-8314","authenticated-orcid":false,"given":"Gibran","family":"Benitez-Garcia","sequence":"additional","affiliation":[{"name":"Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo 182-8585, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2846-9017","authenticated-orcid":false,"given":"Ana Lucila","family":"Sandoval Orozco","sequence":"additional","affiliation":[{"name":"Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7573-6272","authenticated-orcid":false,"given":"Luis Javier","family":"Garc\u00eda Villalba","sequence":"additional","affiliation":[{"name":"Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"ref_1","unstructured":"(2022, November 19). Enisa Threat Landscape 2021. Available online: https:\/\/www.enisa.europa.eu\/publications\/enisa-threat-landscape-2021."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MC.2017.201","article-title":"DDoS in the IoT: Mirai and other botnets","volume":"50","author":"Kolias","year":"2017","journal-title":"Computer"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.ijcip.2010.10.002","article-title":"The economics of cybersecurity: Principles and policy options","volume":"3","author":"Moore","year":"2010","journal-title":"Int. J. Crit. Infrastruct."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102376","DOI":"10.1016\/j.cose.2021.102376","article-title":"Review of cybersecurity assessment methods: Applicability perspective","volume":"108","author":"Leszczyna","year":"2021","journal-title":"Comput. Secur."},{"key":"ref_5","unstructured":"Ford, V., and Siraj, A. (2015, January 1\u201320). Applications of machine learning in cyber security. Proceedings of the 27th International Conference on Computer Applications in Industry and Engineering, New Orleans, LO, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.cose.2018.11.001","article-title":"Survey of machine learning techniques for malware analysis","volume":"81","author":"Ucci","year":"2019","journal-title":"Comput. Secur."},{"key":"ref_7","unstructured":"(2022, October 21). McAfee Labs and Advanced Threat Research. McAfee Labs Threats Report. Available online: https:\/\/www.trellix.com\/fr-ca\/advanced-research-center\/threat-reports.html."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1631\/FITEE.1601745","article-title":"A survey of malware behavior description and analysis","volume":"19","author":"Yu","year":"2018","journal-title":"Front. Inf. Technol. Electron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"51691","DOI":"10.1109\/ACCESS.2019.2908998","article-title":"Comprehensive review of artificial intelligence and statistical approaches in distributed denial of service attack and defense methods","volume":"7","author":"Khalaf","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103093","DOI":"10.1016\/j.jnca.2021.103093","article-title":"Emerging DDoS attack detection and mitigation strategies in software-defined networks: Taxonomy, challenges and future directions","volume":"187","author":"Valdovinos","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nikoloudakis, Y., Kefaloukos, I., Klados, S., Panagiotakis, S., Pallis, E., Skianis, C., and Markakis, E.K. (2021). Towards a Machine Learning Based Situational Awareness Framework for Cybersecurity: An SDN Implementation. Sensors, 21.","DOI":"10.3390\/s21144939"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e1306","DOI":"10.1002\/widm.1306","article-title":"Machine learning in cybersecurity: A review","volume":"9","author":"Handa","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"222310","DOI":"10.1109\/ACCESS.2020.3041951","article-title":"A survey on machine learning techniques for cyber security in the last decade","volume":"8","author":"Shaukat","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TKDE.2019.2946162","article-title":"A survey on data collection for machine learning: A big data-ai integration perspective","volume":"33","author":"Roh","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100336","DOI":"10.1016\/j.patter.2021.100336","article-title":"Data and its (dis) contents: A survey of dataset development and use in machine learning research","volume":"2","author":"Paullada","year":"2021","journal-title":"Patterns"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00318-5","article-title":"Cybersecurity data science: An overview from machine learning perspective","volume":"7","author":"Sarker","year":"2020","journal-title":"J. Big Data"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1007\/978-981-16-3153-5_46","article-title":"Cybersecurity for Data Science: Issues, Opportunities, and Challenges","volume":"248","author":"Humayun","year":"2021","journal-title":"Lect. Notes Netw. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Alshaibi, A., Al-Ani, M., Al-Azzawi, A., Konev, A., and Shelupanov, A. (2022). The Comparison of Cybersecurity Datasets. Data, 7.","DOI":"10.3390\/data7020022"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1177\/1548512920951275","article-title":"Machine learning in cybersecurity: A comprehensive survey","volume":"19","author":"Dasgupta","year":"2022","journal-title":"J. Def. Model. Simul."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.iot.2019.01.007","article-title":"A machine learning based robust prediction model for real-life mobile phone data","volume":"5","author":"Sarker","year":"2019","journal-title":"Internet Things."},{"key":"ref_21","unstructured":"Zheng, M., Robbins, H., Chai, Z., Thapa, P., and Moore, T. (2018, January 13). Cybersecurity research datasets: Taxonomy and empirical analysis. Proceedings of the 11th USENIX Workshop on Cyber Security Experimentation and Test (CSET 18), Baltimore, MD, USA."},{"key":"ref_22","first-page":"012011","article-title":"Malware Detection: Issues and Challenges","volume":"Volume 1807","author":"Naseer","year":"2021","journal-title":"Proceedings of the 2019 International Conference of Science and Information Technology in Smart Administration (ICSINTeSA)"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Alzahrani, R.J., and Alzahrani, A. (2021). Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic. Electronics, 10.","DOI":"10.3390\/electronics10232919"},{"key":"ref_24","unstructured":"Sikorsi, A.M. (2012). Practical Malware Analysis: A Hands-On Guide to Dissecting Malicious Software, No Starch Press. 1st Edition, Kindle Edition."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nikolenko, S.I. (2021). Synthetic Data for Deep Learning, Springer.","DOI":"10.1007\/978-3-030-75178-4"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ye, J., Xue, Y., Long, L.R., Antani, S., Xue, Z., Cheng, K.C., and Huang, X. (2020, January 4\u20138). Synthetic sample selection via reinforcement learning. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru.","DOI":"10.1007\/978-3-030-59710-8_6"},{"key":"ref_27","unstructured":"Polizzotto, M.N., Finfer, S., Garcia, F., S\u00f6nnerborg, A., Zazzi, M., B\u00f6hm, M., Jorm, L., Barbieri, S., Kaiser, R., and I-Hsien Kuo, N. (2022). The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","article-title":"Deep reinforcement learning: A brief survey","volume":"34","author":"Arulkumaran","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_29","unstructured":"Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W. (2016). Openai gym. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"554","DOI":"10.3390\/make3030029","article-title":"Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems: Part 1\u2014Fundamentals and Applications in Games, Robotics and Natural Language Processing","volume":"3","author":"Xiang","year":"2021","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_31","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"101861","DOI":"10.1016\/j.sysarc.2020.101861","article-title":"A survey on machine learning-based malware detection in executable files","volume":"112","author":"Singh","year":"2021","journal-title":"J. Syst. Archit."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Aboaoja, F.A., Zainal, A., Ghaleb, F.A., Al-rimy, B.A.S., Eisa, T.A.E., and Elnour, A.A.H. (2022). Malware Detection Issues, Challenges, and Future Directions: A Survey. Appl. Sci., 12.","DOI":"10.3390\/app12178482"},{"key":"ref_34","unstructured":"(2022, October 21). Karl-Bridge-Microsoft. PE Format-Win32 Apps. Available online: https:\/\/github.com\/Karl-Bridge-Microsoft."},{"key":"ref_35","first-page":"153","article-title":"Malware detection based on multiple PE headers identification and optimization for specific types of files","volume":"1","author":"Zatloukal","year":"2017","journal-title":"JAEC"},{"key":"ref_36","unstructured":"Anderson, H.S., Kharkar, A., Filar, B., Evans, D., and Roth, P. (2018). Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3465361","article-title":"Maat: Automatically analyzing virustotal for accurate labeling and effective malware detection","volume":"24","author":"Salem","year":"2021","journal-title":"ACM Trans. Priv. Secur."},{"key":"ref_38","unstructured":"(2022, September 17). VirusTotal. Available online: https:\/\/www.virustotal.com\/gui\/home\/upload."},{"key":"ref_39","first-page":"1","article-title":"On the impact of sample duplication in machine-learning-based android malware detection","volume":"30","author":"Zhao","year":"2021","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102921","DOI":"10.1016\/j.cose.2022.102921","article-title":"MOTIF: A Malware Reference Dataset with Ground Truth Family Labels","volume":"124","author":"Joyce","year":"2022","journal-title":"Comput. Secur."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Oyama, Y., Miyashita, T., and Kokubo, H. (2019, January 26\u201329). Identifying useful features for malware detection in the ember dataset. Proceedings of the 2019 Seventh International Symposium on Computing and Networking Workshops (CANDARW), Nagasaki, Japan.","DOI":"10.1109\/CANDARW.2019.00069"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Amich, A., and Eshete, B. (2021, January 21\u201323). Explanation-guided diagnosis of machine learning evasion attacks. Proceedings of the International Conference on Security and Privacy in Communication Systems, Washington, WA, USA.","DOI":"10.1007\/978-3-030-90019-9_11"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Castro, R.L., Schmitt, C., and Rodosek, G.D. (2019, January 24\u201327). Armed: How automatic malware modifications can evade static detection?. Proceedings of the 2019 5th International Conference on Information Management (ICIM), Cambridge, UK.","DOI":"10.1109\/INFOMAN.2019.8714698"},{"key":"ref_44","unstructured":"Romain, T. (2022, September 27). LIEF Library to Instrument Executable Formats. Available online: https:\/\/lief-project.github.io\/."},{"key":"ref_45","unstructured":"Anderson, H.S., and Roth, P. (2018). Ember: An open dataset for training static pe malware machine learning models. arXiv."},{"key":"ref_46","first-page":"1","article-title":"The problem of overfitting","volume":"44","author":"Hawkins","year":"2004","journal-title":"J. Chem. Inf. Model"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Weinberger, K., Dasgupta, A., Langford, J., Smola, A., and Attenberg, J. (2009, January 14\u201318). Feature hashing for large scale multitask learning. Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553516"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Vishnu, N., Batth, R.S., and Singh, G. (2019, January 11\u201312). Denial of service: Types, techniques, defence mechanisms and safe guards. Proceedings of the 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, UAE.","DOI":"10.1109\/ICCIKE47802.2019.9004388"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Pokrinchak, M., and Chowdhury, M.M. (2021, January 14\u201315). Distributed Denial of Service: Problems and Solutions. Proceedings of the 2021 IEEE International Conference on Electro Information Technology (EIT), Mt. Pleasant, MI, USA.","DOI":"10.1109\/EIT51626.2021.9491925"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"100332","DOI":"10.1016\/j.cosrev.2020.100332","article-title":"Distributed denial of service attacks in cloud: State-of-the-art of scientific and commercial solutions","volume":"39","author":"Bhardwaj","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.procs.2016.03.103","article-title":"DDoS attack analyzer: Using JPCAP and WinCap","volume":"79","author":"Shinde","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Goyal, P., and Goyal, A. (2017, January 16\u201317). Comparative study of two most popular packet sniffing tools-Tcpdump and Wireshark. Proceedings of the 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN), Cyprus, Turkey.","DOI":"10.1109\/CICN.2017.8319360"},{"key":"ref_53","first-page":"393","article-title":"A feature reduction based reflected and exploited DDoS attacks detection system","volume":"13","author":"Kshirsagar","year":"2022","journal-title":"JAIHC"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"01052","DOI":"10.1051\/e3sconf\/202018401052","article-title":"A survey of DDoS attacks using machine learning techniques","volume":"Volume 184","author":"Arshi","year":"2020","journal-title":"Proceedings of the E3S Web of Conferences"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1109\/SURV.2013.031413.00127","article-title":"A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks","volume":"15","author":"Zargar","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Gohil, M., and Kumar, S. (2020, January 9\u201313). Evaluation of classification algorithms for distributed denial of service attack detection. Proceedings of the 2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Laguna Hills, CA, USA.","DOI":"10.1109\/AIKE48582.2020.00028"},{"key":"ref_57","unstructured":"Kaspersky (2015). DDoS Protection White Paper, Kaspersky."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Hakak, S., and Ghorbani, A.A. (2019, January 1\u20133). Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. Proceedings of the 2019 International Carnahan Conference on Security Technology (ICCST), Chennai, India.","DOI":"10.1109\/CCST.2019.8888419"},{"key":"ref_59","unstructured":"Radoyska, P., and Atanasova, M. (2020, January 3\u20134). Free tools for Testing the Security of Web Services in the UTP Network. Proceedings of the Fifth International Scientific Conference \u201cTelecommunications, Informatics, Energy and Management\u201d, Sofia, Bulgaria."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3424155","article-title":"On generating network traffic datasets with synthetic attacks for intrusion detection","volume":"24","author":"Cordero","year":"2021","journal-title":"ACM Trans. Priv. Secur."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Alkasassbeh, M., Al-Naymat, G., Hassanat, A.B., and Almseidin, M. (2016). Detecting distributed denial of service attacks using data mining techniques. Int. J. Adv. Comput. Sci. Appl., 7.","DOI":"10.14569\/IJACSA.2016.070159"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Alothman, B. (2019, January 3\u20134). Raw network traffic data preprocessing and preparation for automatic analysis. Proceedings of the 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), Oxford, UK.","DOI":"10.1109\/CyberSecPODS.2019.8885333"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Han, L.q., and Zhang, Y. (2020, January 28\u201329). Pca-based ddos attack detection of sdn environments. Proceedings of the International conference on Big Data Analytics for Cyber-Physical-Systems, Shanghai, China.","DOI":"10.1007\/978-981-33-4572-0_204"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2812","DOI":"10.1039\/C3AY41907J","article-title":"Principal component analysis","volume":"6","author":"Bro","year":"2014","journal-title":"Anal. methods"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Masri, R., and Aldwairi, M. (2017, January 4\u20136). Automated malicious advertisement detection using virustotal, urlvoid, and trendmicro. Proceedings of the 2017 8th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan.","DOI":"10.1109\/IACS.2017.7921994"},{"key":"ref_66","unstructured":"Sanders, C. (2017). Practical Packet Analysis, 3E: Using Wireshark to Solve Real-World Network Problems, No Starch Press."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.dcan.2021.11.007","article-title":"N-gram MalGAN: Evading machine learning detection via feature n-gram","volume":"8","author":"Zhu","year":"2022","journal-title":"Digit. Commun. Netw."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Lu, Y., and Li, J. (2019, January 8\u201311). Generative adversarial network for improving deep learning based malware classification. Proceedings of the 2019 Winter Simulation Conference (WSC), National Harbor, MD, USA.","DOI":"10.1109\/WSC40007.2019.9004932"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1550147717703116","DOI":"10.1177\/1550147717703116","article-title":"Fuzzy\u2013synthetic minority oversampling technique: Oversampling based on fuzzy set theory for Android malware detection in imbalanced datasets","volume":"13","author":"Xu","year":"2017","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Mazaed Alotaibi, F. (2022). A Multifaceted Deep Generative Adversarial Networks Model for Mobile Malware Detection. Appl. Sci., 12.","DOI":"10.3390\/app12199403"},{"key":"ref_71","unstructured":"Hsiao, S.W., and Chu, P.Y. (2022). Sequence Feature Extraction for Malware Family Analysis via Graph Neural Network. arXiv."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Hekmati, A., Grippo, E., and Krishnamachari, B. (2021, January 15\u201317). Large-scale Urban IoT Activity Data for DDoS Attack Emulation. Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, Coimbra, Portugal.","DOI":"10.1145\/3485730.3493695"},{"key":"ref_73","unstructured":"Charlier, J., Singh, A., Ormazabal, G., State, R., and Schulzrinne, H. (2019). SynGAN: Towards generating synthetic network attacks using GANs. arXiv."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Arnaboldi, L., and Morisset, C. (2018, January 25\u201329). Generating synthetic data for real world detection of DoS attacks in the IoT. Proceedings of the Software Technologies: Applications and Foundations, Toulouse, France.","DOI":"10.1007\/978-3-030-04771-9_11"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Hernandez-Suarez, A., Sanchez-Perez, G., Toscano-Medina, L.K., Olivares-Mercado, J., Portillo-Portilo, J., Avalos, J.G., and Garc\u00eda Villalba, L.J. (2022). Detecting Cryptojacking Web Threats: An Approach with Autoencoders and Deep Dense Neural Networks. Appl. Sci., 12.","DOI":"10.3390\/app12073234"},{"key":"ref_76","unstructured":"Liu, M., Mroueh, Y., Ross, J., Zhang, W., Cui, X., Das, P., and Yang, T. (2019). Towards better understanding of adaptive gradient algorithms in generative adversarial nets. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1231\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:12:25Z","timestamp":1760119945000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1231"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,20]]},"references-count":76,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031231"],"URL":"https:\/\/doi.org\/10.3390\/s23031231","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,20]]}}}