{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:08:07Z","timestamp":1779203287417,"version":"3.51.4"},"reference-count":178,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T00:00:00Z","timestamp":1646352000000},"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>Today\u2019s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.<\/jats:p>","DOI":"10.3390\/s22052017","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T20:40:02Z","timestamp":1646599202000},"page":"2017","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9535-3842","authenticated-orcid":false,"given":"Chaitanya","family":"Gupta","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5079-1064","authenticated-orcid":false,"given":"Ishita","family":"Johri","sequence":"additional","affiliation":[{"name":"School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9352-0237","authenticated-orcid":false,"given":"Kathiravan","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0183-8182","authenticated-orcid":false,"given":"Yuh-Chung","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electromechanical Engineering, National ILan University, Yilan 26047, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4268-3482","authenticated-orcid":false,"given":"Saeed Mian","family":"Qaisar","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Effat University, Jeddah 22332, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuo-Yi","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Bio-Industrial Mechatronic Engineering, National Chung Hsing University, Taichung 402, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23817","DOI":"10.1109\/ACCESS.2020.2968045","article-title":"Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity","volume":"8","author":"Zeadally","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","first-page":"2","article-title":"An improved information-security risk assessment algorithm for a hybrid model","volume":"5","author":"Kong","year":"2013","journal-title":"Int. J. Adv. Comput. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Suo, H., Liu, Z., Wan, J., and Zhou, K. (2013, January 1\u20135). Security and privacy in mobile cloud computing. Proceedings of the 9th International Wireless Communications and Mobile Computing Conference (IWCMC), Sardinia, Italy.","DOI":"10.1109\/IWCMC.2013.6583635"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.comcom.2021.05.019","article-title":"Information security in the post quantum era for 5G and beyond networks: Threats to existing cryptography, and post-quantum cryptography","volume":"176","author":"Chamola","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2012.09.004","article-title":"Anomaly intrusion detection based on PLS feature extraction and core vector machine","volume":"40","author":"Gan","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s00500-014-1511-6","article-title":"Evaluation of machine learning classifiers for mobile malware detection","volume":"20","author":"Narudin","year":"2014","journal-title":"Soft Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.neucom.2013.04.055","article-title":"Research on virus detection technique based on ensemble neural network and SVM","volume":"137","author":"Zhang","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/MCOM.2015.7180511","article-title":"Security and privacy in mobile crowdsourcing networks: Challenges and opportunities","volume":"53","author":"Yang","year":"2015","journal-title":"IEEE Commun. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1109\/JAS.2015.7296538","article-title":"Security risk assessment of cyber physical power system based on rough set and gene expression programming","volume":"2","author":"Deng","year":"2015","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1109\/JBHI.2015.2407157","article-title":"Privacy-Preserving Patient-Centric Clinical Decision Support System on Na\u00efve Bayesian Classification","volume":"20","author":"Liu","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MSP.2016.82","article-title":"Opportunities and Challenges of Software-Defined Mobile Networks in Network Security","volume":"14","author":"Liyanage","year":"2016","journal-title":"IEEE Secur. Priv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.asoc.2015.10.011","article-title":"A novel SVM-kNN-PSO ensemble method for intrusion detection system","volume":"38","author":"Aburomman","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_13","first-page":"784","article-title":"Correlated Differential Privacy Protection for Mobile Crowdsensing","volume":"7","author":"Chen","year":"2021","journal-title":"IEEE Trans. Big Data"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/MWC.2016.1500356WC","article-title":"Machine Learning Paradigms for Next-Generation Wireless Networks","volume":"24","author":"Jiang","year":"2016","journal-title":"IEEE Wirel. Commun."},{"key":"ref_15","unstructured":"Wang, W., Zhu, M., Zeng, X., Ye, X., and Sheng, Y. (2017, January 11\u201313). Malware traffic classification using convolutional neural network for representation learning. Proceedings of the International Conference on Information Networking (ICOIN), Da Nang, Vietnam."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2432","DOI":"10.1109\/COMST.2017.2707140","article-title":"State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow\u2019s Intelligent Network Traffic Control Systems","volume":"19","author":"Fadlullah","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shah, S.K., Tariq, Z., Lee, J., and Lee, Y. (2021). Event-Driven Deep Learning for Edge Intelligence (EDL-EI). Sensors, 21.","DOI":"10.3390\/s21186023"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/8959635","article-title":"Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services","volume":"2018","author":"Xiong","year":"2018","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Mozaffari, M., Saad, W., Bennis, M., and Debbah, M. (2018, January 10\u201312). Machine Learning for Predictive On-Demand Deployment of Uavs for Wireless Communications. Proceedings of the IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/GLOCOM.2018.8647209"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6367","DOI":"10.1109\/TII.2019.2917307","article-title":"Making Knowledge Tradable in Edge-AI Enabled IoT: A Consortium Blockchain-Based Efficient and Incentive Approach","volume":"15","author":"Lin","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e73","DOI":"10.1002\/spy2.73","article-title":"Contemplating social engineering studies and attack scenarios: A review study","volume":"2","author":"Yasin","year":"2019","journal-title":"Secur. Priv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, Z., He, Q., Liu, L., Lan, D., Chung, H.-M., and Mao, Z. (2019, January 9\u201311). An Artificial Intelligence Perspective on Mobile Edge Computing. Proceedings of the IEEE International Conference on Smart Internet of Things (SmartIoT), Tianjin, China.","DOI":"10.1109\/SmartIoT.2019.00024"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1109\/JIOT.2018.2887086","article-title":"UAV Communications for 5G and Beyond: Recent Advances and Future Trends","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1109\/COMST.2019.2904897","article-title":"Deep Learning in Mobile and Wireless Networking: A Survey","volume":"21","author":"Zhang","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.comcom.2019.08.003","article-title":"Malware traffic classification using principal component analysis and artificial neural network for extreme surveillance","volume":"147","author":"Arivudainambi","year":"2019","journal-title":"Comput. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Serey, J., Quezada, L., Alfaro, M., Fuertes, G., Vargas, M., Ternero, R., Sabattin, J., Duran, C., and Gutierrez, S. (2021). Artificial Intelligence Methodologies for Data Management. Symmetry, 13.","DOI":"10.3390\/sym13112040"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Kantarci, B. (2019, January 4\u20139). Invited Paper: AI-Based Security Design of Mobile Crowdsensing Systems: Review, Challenges and Case Studies. Proceedings of the 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE), San Francisco, CA, USA.","DOI":"10.1109\/SOSE.2019.00014"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kulin, M., Kazaz, T., De Poorter, E., and Moerman, I. (2021). A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer. Electronics, 10.","DOI":"10.3390\/electronics10030318"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Berman, D.S., Buczak, A.L., Chavis, J.S., and Corbett, C.L. (2019). A Survey of Deep Learning Methods for Cyber Security. Information, 10.","DOI":"10.3390\/info10040122"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ali, M., Hu, Y.-F., Luong, D.K., Oguntala, G., Li, J.-P., and Abdo, K. (2020, January 11\u201315). Adversarial Attacks on AI based Intrusion Detection System for Heterogeneous Wireless Communications Networks. Proceedings of the 2020 AIAA\/IEEE 39th Digital Avionics Systems Conference (DASC), San Antonio, TX, USA.","DOI":"10.1109\/DASC50938.2020.9256597"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/MVT.2020.3002487","article-title":"Artificial Intelligence Security in 5G Networks: Adversarial Examples for Estimating a Travel Time Task","volume":"15","author":"Qiu","year":"2020","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s43926-020-00001-4","article-title":"Role of Artificial Intelligence in the Internet of Things (IoT) cybersecurity","volume":"1","author":"Kuzlu","year":"2021","journal-title":"Discov. Internet Things"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s11235-020-00733-2","article-title":"A comprehensive survey of AI-enabled phishing attacks detection techniques","volume":"76","author":"Basit","year":"2020","journal-title":"Telecommun. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MCI.2019.2954643","article-title":"Artificial Intelligence Enabled Internet of Things: Network Architecture and Spectrum Access","volume":"15","author":"Song","year":"2020","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MNET.021.1900608","article-title":"Blockchain and AI Empowered Trust-Information-Centric Network for Beyond 5G","volume":"34","author":"Pan","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1109\/JPROC.2019.2954595","article-title":"Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches","volume":"108","author":"Tang","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42452-020-03559-4","article-title":"Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge","volume":"2","author":"Radanliev","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1167\/tvst.9.2.36","article-title":"Protecting Data Privacy in the Age of AI-Enabled Ophthalmology","volume":"9","author":"Tom","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_39","first-page":"57","article-title":"Machine learning in cybersecurity: A comprehensive survey","volume":"19","author":"Dasgupta","year":"2020","journal-title":"J. D\u00e9f. Model. Simul. Appl. Methodol. Technol."},{"key":"ref_40","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_41","first-page":"497","article-title":"A survey of neural networks usage for intrusion detection systems","volume":"12","year":"2020","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e4150","DOI":"10.1002\/ett.4150","article-title":"Network intrusion detection system: A systematic study of machine learning and deep learning approaches","volume":"32","author":"Ahmad","year":"2020","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/COMST.2020.3029005","article-title":"A Comprehensive Survey on Moving Networks","volume":"23","author":"Jaffry","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"108553","DOI":"10.1016\/j.comnet.2021.108553","article-title":"A decentralized strongly secure attribute-based encryption and authentication scheme for distributed Internet of Mobile Things","volume":"201","author":"Zhang","year":"2021","journal-title":"Comput. Netw."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Trnka, M., Abdelfattah, A.S., Shrestha, A., Coffey, M., and Cerny, T. (2022). Systematic Review of Authentication and Authorization Advancements for the Internet of Things. Sensors, 22.","DOI":"10.3390\/s22041361"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"103204","DOI":"10.1016\/j.jnca.2021.103204","article-title":"A lightweight and secure handover authentication scheme for 5G network using neighbour base stations","volume":"193","author":"Yan","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1109\/TII.2019.2957130","article-title":"An AI-Enabled Three-Party Game Framework for Guaranteed Data Privacy in Mobile Edge Crowdsensing of IoT","volume":"17","author":"Xiong","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MNET.011.2000628","article-title":"Preventing and Controlling Epidemics Through Blockchain-Assisted AI-Enabled Networks","volume":"35","author":"Otoum","year":"2021","journal-title":"IEEE Netw."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"103213","DOI":"10.1016\/j.jnca.2021.103213","article-title":"A survey on deep learning for challenged networks: Applications and trends","volume":"194","author":"Bochie","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rabbani, M., Wang, Y., Khoshkangini, R., Jelodar, H., Zhao, R., Ahmadi, S.B.B., and Ayobi, S. (2021). A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies. Entropy, 23.","DOI":"10.3390\/e23050529"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yavanoglu, O., and Aydos, M. (2017, January 11\u201314). A review on cyber security datasets for machine learning algorithms. Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA.","DOI":"10.1109\/BigData.2017.8258167"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Apruzzese, G., Colajanni, M., Ferretti, L., Guido, A., and Marchetti, M. (June, January 29). On the Effectiveness of Machine and Deep Learning for Cyber Security. Proceedings of the 2018 10th International Conference on Cyber Conflict (CyCon), Tallinn, Estonia.","DOI":"10.23919\/CYCON.2018.8405026"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Bhamare, D., Salman, T., Samaka, M., Erbad, A., and Jain, R. (2016, January 19). Feasibility of Supervised Machine Learning for Cloud Security. Proceedings of the 2016 International Conference on Information Science and Security (ICISS), Pattaya, Thailand.","DOI":"10.1109\/ICISSEC.2016.7885853"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/sec.341","article-title":"Evaluation of anomaly-based IDS for mobile devices using machine learning classifiers","volume":"5","author":"Damopoulos","year":"2011","journal-title":"Secur. Commun. Netw."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.neucom.2015.04.101","article-title":"Detection of known and unknown DDoS attacks using Artificial Neural Networks","volume":"172","author":"Saied","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_57","unstructured":"Sinclair, C., Pierce, L., and Matzner, S. (1999, January 6\u201310). An application of machine learning to network intrusion detection. Proceedings of the 15th Annual Computer Security Applications Conference (ACSAC\u201999), Phoenix, AZ, USA."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1504\/IJAHUC.2011.038998","article-title":"DDoS detection and traceback with decision tree and grey relational analysis","volume":"7","author":"Wu","year":"2011","journal-title":"Int. J. Ad Hoc Ubiquitous Comput."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Do, V.T., Engelstad, P., Feng, B., and van Do, T. (2017, January 8\u20139). Detection of DNS Tunneling in Mobile Networks Using Machine Learning. Proceedings of the International Conference on Information Science and Applications, Wuhan, China.","DOI":"10.1007\/978-981-10-4154-9_26"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.procs.2016.06.016","article-title":"Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection","volume":"89","author":"Belavagi","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4586875","DOI":"10.1155\/2020\/4586875","article-title":"A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets","volume":"2020","author":"Rajagopal","year":"2020","journal-title":"Secur. Commun. Netw."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Abdullahi, M., Baashar, Y., Alhussian, H., Alwadain, A., Aziz, N., Capretz, L.F., and Abdulkadir, S.J. (2022). Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review. Electronics, 11.","DOI":"10.3390\/electronics11020198"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Nanda, S., Zafari, F., DeCusatis, C., Wedaa, E., and Yang, B. (2016, January 7\u20139). Predicting network attack patterns in SDN using machine learning approach. Proceedings of the 2016 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Palo Alto, CA, USA.","DOI":"10.1109\/NFV-SDN.2016.7919493"},{"key":"ref_64","unstructured":"Shon, T., Kim, Y., Lee, C., and Moon, J. (2005, January 15\u201317). A machine learning framework for network anomaly detection using SVM and GA. Proceedings of the Sixth Annual IEEE SMC Information Assurance Workshop, West Point, NY, USA."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Wang, P., Lin, S.-C., and Luo, M. (July, January 27). A Framework for QoS-aware Traffic Classification Using Semi-supervised Machine Learning in SDNs. Proceedings of the 2016 IEEE International Conference on Services Computing (SCC), San Francisco, CA, USA.","DOI":"10.1109\/SCC.2016.133"},{"key":"ref_66","unstructured":"Wang, J., Hong, X., Ren, R.R., and Li, T.H. (2009, January 23\u201324). A real-time intrusion detection system based on PSO-SVM. International Workshop on Information Security and Application. Proceedings of the 2009 International Workshop on Information Security and Application (IWISA 2009), Wuhan, China."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Bensalem, M., Singh, S.K., and Jukan, A. (2019, January 9\u201313). On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Big Island, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9013238"},{"key":"ref_68","first-page":"61","article-title":"Malware Detection Module using Machine Learning Algorithms to Assist in Centralized Security in Enterprise Networks","volume":"4","author":"Singhal","year":"2012","journal-title":"Int. J. Netw. Secur. Its Appl."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1504\/IJWMC.2018.094644","article-title":"Using machine learning methods for detecting network anomalies within SNMP-MIB dataset","volume":"15","year":"2018","journal-title":"Int. J. Wirel. Mob. Comput."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Yong, B., Wei, W., Li, K., Shen, J., Zhou, Q., Wozniak, M., Po\u0142ap, D., and Dama\u0161evi\u010dius, R. (2020). Ensemble machine learning approaches for webshell detection in Internet of things environments. Trans. Emerg. Telecommun. Technol.","DOI":"10.1002\/ett.4085"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1109\/TVLSI.2019.2928960","article-title":"Securing a Wireless Network-on-Chip Against Jamming-Based Denial-of-Service and Eavesdropping Attacks","volume":"27","author":"Vashist","year":"2019","journal-title":"IEEE Trans. Very Large Scale Integr. (VLSI) Syst."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MWC.001.1900119","article-title":"Reliable Federated Learning for Mobile Networks","volume":"27","author":"Kang","year":"2020","journal-title":"IEEE Wirel. Commun."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"3133","DOI":"10.1109\/COMST.2019.2916583","article-title":"Applications of Deep Reinforcement Learning in Communications and Networking: A Survey","volume":"21","author":"Luong","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1109\/COMST.2021.3086296","article-title":"A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks","volume":"23","author":"Rodriguez","year":"2021","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/CC.2018.8485465","article-title":"Two-phase rate adaptation strategy for improving real-time video QoE in mobile networks","volume":"15","author":"Xiao","year":"2018","journal-title":"China Commun."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.comcom.2018.06.001","article-title":"Privacy-preserving image retrieval for mobile devices with deep features on the cloud","volume":"127","author":"Rahim","year":"2018","journal-title":"Comput. Commun."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Zhong, Y., Yuan, Z., Zhao, S., and Luo, X. (December, January 30). A Wifi Positioning Method Based on Stack Auto Encoder. Proceedings of the 2018 7th International Conference on Digital Home (ICDH), Guilin, China.","DOI":"10.1109\/ICDH.2018.00057"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"7700","DOI":"10.1109\/ACCESS.2018.2803446","article-title":"A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks","volume":"6","author":"Maimo","year":"2018","journal-title":"IEEE Access"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1007\/s12083-019-00741-3","article-title":"A deep learning based data forwarding algorithm in mobile social networks","volume":"12","author":"Wang","year":"2019","journal-title":"Peer-to-Peer Netw. Appl."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"101438","DOI":"10.1016\/j.phycom.2021.101438","article-title":"Federated user activity analysis via network traffic and deep neural network in mobile wireless networks","volume":"48","author":"Guo","year":"2021","journal-title":"Phys. Commun."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"9340","DOI":"10.1109\/ACCESS.2022.3142082","article-title":"Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication","volume":"10","author":"Arya","year":"2022","journal-title":"IEEE Access"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"101752","DOI":"10.1016\/j.cose.2020.101752","article-title":"A deep learning method with wrapper based feature extraction for wireless intrusion detection system","volume":"92","author":"Kasongo","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"124379","DOI":"10.1109\/ACCESS.2019.2937347","article-title":"Cyber Security Threats Detection in Internet of Things Using Deep Learning Approach","volume":"7","author":"Ullah","year":"2019","journal-title":"IEEE Access"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"04018093","DOI":"10.1061\/(ASCE)WR.1943-5452.0001007","article-title":"Cyberattack Detection Using Deep Generative Models with Variational Inference","volume":"145","author":"Chandy","year":"2019","journal-title":"J. Water Resour. Plan. Manag."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Kos, J., Fischer, I., and Song, D. (2018, January 21\u201323). Adversarial Examples for Generative Models. Proceedings of the 2018 IEEE Security and Privacy Workshops (SPW), San Francisco, CA, USA.","DOI":"10.1109\/SPW.2018.00014"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"15978","DOI":"10.1109\/ACCESS.2022.3149050","article-title":"Design and Implementation of Traffic Generation Model and Spectrum Requirement Calculator for Private 5G Network","volume":"10","author":"Kim","year":"2022","journal-title":"IEEE Access"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1109\/TMM.2016.2615524","article-title":"A Joint Deep Boltzmann Machine (jDBM) Model for Person Identification Using Mobile Phone Data","volume":"19","author":"Alam","year":"2016","journal-title":"IEEE Trans. Multimed."},{"key":"ref_88","unstructured":"Wang, J., Lu, S., Wang, S.-H., and Zhang, Y.-D. (2021). A review on extreme learning machine. Multimed. Tools Appl., 1\u201350."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"155171","DOI":"10.1109\/ACCESS.2019.2948059","article-title":"Fault and Noise Tolerance in the Incremental Extreme Learning Machine","volume":"7","author":"Leung","year":"2019","journal-title":"IEEE Access"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"49399","DOI":"10.1109\/ACCESS.2018.2868713","article-title":"Safe Semi-Supervised Extreme Learning Machine for EEG Signal Classification","volume":"6","author":"She","year":"2018","journal-title":"IEEE Access"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.asoc.2018.05.049","article-title":"Semi-supervised learning based distributed attack detection framework for IoT","volume":"72","author":"Rathore","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Alimi, O.A., Ouahada, K., Abu-Mahfouz, A.M., Rimer, S., and Alimi, K.O.A. (2021). A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification. Sustainability, 13.","DOI":"10.3390\/su13179597"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Ahmad, W., Rasool, A., Javed, A.R., Baker, T., and Jalil, Z. (2022). Cyber Security in IoT-Based Cloud Computing: A Comprehensive Survey. Electronics, 11.","DOI":"10.3390\/electronics11010016"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"McLaughlin, N., del Rincon, J.M., Kang, B., Yerima, S., Miller, P., Sezer, S., Safaei, Y., Trickel, E., Zhao, Z., and Doupe, A. (2017, January 22\u201324). Deep android malware detection. Proceedings of the 7th ACM on Conference on Data and Application Security and Privacy, Scottsdale, AZ, USA.","DOI":"10.1145\/3029806.3029823"},{"key":"ref_96","unstructured":"Aminanto, M.E., and Kwangjo, K. (December, January 30). Deep Learning-based Feature Selection for Intrusion Detection System in Transport Layer 1). Proceedings of the Korea Institutes of Information Security and Cryptology Conference, Seoul, Korea."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1007\/s00500-019-04030-2","article-title":"Deep packet: A novel approach for encrypted traffic classification using deep learning","volume":"24","author":"Lotfollahi","year":"2019","journal-title":"Soft Comput."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1016\/j.procs.2021.03.107","article-title":"Spam Email Detection Using Deep Learning Techniques","volume":"184","author":"AbdulNabi","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"2505","DOI":"10.1109\/TSG.2017.2703842","article-title":"Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism","volume":"8","author":"He","year":"2017","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_100","first-page":"80","article-title":"Application of recurrent neural networks for user verification based on keystroke dynamics","volume":"3","author":"Kobojek","year":"2016","journal-title":"J. Telecommun. Inf. Technol."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Cheng, M., Xu, Q., Lv, J., Liu, W., Li, Q., and Wang, J. (2016, January 8\u201311). MS-LSTM: A multi-scale LSTM model for BGP anomaly detection. Proceedings of the IEEE 24th International Conference Network Protocols (ICNP), Singapore.","DOI":"10.1109\/ICNP.2016.7785326"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Mac, H., Tran, D., Tong, V., Nguyen, L.G., and Tran, H.A. (2017, January 7\u20138). DGA Botnet Detection Using Supervised Learning Methods. Proceedings of the 8th International Symposium on Information and Communication Technology, Nhatrang, Vietnam.","DOI":"10.1145\/3155133.3155166"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"102221","DOI":"10.1016\/j.cose.2021.102221","article-title":"Deep learning for insider threat detection: Review, challenges and opportunities","volume":"104","author":"Yuan","year":"2021","journal-title":"Comput. Secur."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1002\/sec.1460","article-title":"FFSc: A novel measure for low-rate and high-rate DDoS attack detection using multivariate data analysis","volume":"9","author":"Hoque","year":"2016","journal-title":"Secur. Commun. Networks"},{"key":"ref_105","first-page":"8","article-title":"Botnet and Botnet Detection Techniques","volume":"178","author":"Malik","year":"2019","journal-title":"Int. J. Comput. Appl."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A. (2009, January 8\u201310). A detailed analysis of the KDD CUP 99 data set. Proceedings of the Second IEEE Symposium on Computational Intelligence for Security and Defence Applications, Ottawa, ON, Canada.","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"9","DOI":"10.5815\/ijmecs.2013.10.02","article-title":"An Efficient Machine Learning Based Classification Scheme for Detecting Distributed Command & Control Traffic of P2P Botnets","volume":"5","author":"Barthakur","year":"2013","journal-title":"Int. J. Mod. Educ. Comput. Sci."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Turcotte, M.J.M., Kent, A.D., and Hash, C. (2018). Unified Host and Network Data Set. Data Science for Cyber-Security, World Scientific.","DOI":"10.1142\/9781786345646_001"},{"key":"ref_109","unstructured":"Joseph, B. (2015, December 24). Yahoo Password Frequency Corpus. Figshare. Available online: https:\/\/figshare.com\/articles\/dataset\/Yahoo_Password_Frequency_Corpus\/2057937."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Tyson, G., Huang, S., Cuadrado, F., Castro, I., Perta, V.C., Sathiaseelan, A., and Uhlig, S. (2017, January 3\u20137). Exploring HTTP Header Manipulation In-The-Wild. Proceedings of the 26th International Conference on World Wide Web, Perth, Australia.","DOI":"10.1145\/3038912.3052571"},{"key":"ref_111","first-page":"3654","article-title":"Deep Learning in Drebin: Android malware Image Texture Median Filter Analysis and Detection","volume":"13","author":"Luo","year":"2019","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_112","first-page":"1047","article-title":"LanguageCrawl: A generic tool for building language models upon common Crawl","volume":"55","author":"Roziewski","year":"2021","journal-title":"Comput. Humanit."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1504\/IJESDF.2009.027524","article-title":"Challenges and complexities of managing information security","volume":"2","author":"Onwubiko","year":"2009","journal-title":"Int. J. Electron. Secur. Digit. Forensics"},{"key":"ref_114","first-page":"57","article-title":"Management of Information Security: Challenges and Research Directions","volume":"20","author":"Choobineh","year":"2007","journal-title":"Commun. Assoc. Inf. Syst."},{"key":"ref_115","first-page":"1","article-title":"1 Impact of available resources on software patch management","volume":"4","author":"Anand","year":"2020","journal-title":"Syst. Perform. Modeling"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Appling, D.S., Briscoe, E.J., and Hutto, C.J. (2015, January 18\u201322). Discriminative Models for Predicting Deception Strategies. Proceedings of the 24th International Conference on World Wide Web, Florence, Italy.","DOI":"10.1145\/2740908.2742575"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.istr.2008.10.006","article-title":"Information Security management: A human challenge?","volume":"13","author":"Ashenden","year":"2008","journal-title":"Inf. Secur. Tech. Rep."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2635673","article-title":"A Survey of Interdependent Information Security Games","volume":"47","author":"Laszka","year":"2015","journal-title":"ACM Comput. Surv."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"824898","DOI":"10.3389\/fpubh.2021.824898","article-title":"A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning","volume":"9","author":"Kumaar","year":"2022","journal-title":"Front. Public Health"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Angel, N.A., Ravindran, D., Vincent, P.M.D.R., Srinivasan, K., and Hu, Y.-C. (2021). Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies. Sensors, 22.","DOI":"10.3390\/s22010196"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Mamdiwar, S.D., Shakruwala, Z., Chadha, U., Srinivasan, K., and Chang, C.-Y. (2021). Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring. Biosensors, 11.","DOI":"10.3390\/bios11100372"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Patel, D., Srinivasan, K., Chang, C.-Y., Gupta, T., and Kataria, A. (2020). Network Anomaly Detection inside Consumer Networks\u2014A Hybrid Approach. Electronics, 9.","DOI":"10.3390\/electronics9060923"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Oliveira, N., Pra\u00e7a, I., Maia, E., and Sousa, O. (2021). Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems. Appl. Sci., 11.","DOI":"10.3390\/app11041674"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Nisa, M., Shah, J.H., Kanwal, S., Raza, M., Khan, M.A., Dama\u0161evi\u010dius, R., and Bla\u017eauskas, T. (2020). Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features. Appl. Sci., 10.","DOI":"10.3390\/app10144966"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"119795","DOI":"10.1109\/ACCESS.2020.3004814","article-title":"Defenses Against Perception-Layer Attacks on IoT Smart Furniture for Impaired People","volume":"8","author":"Nasralla","year":"2020","journal-title":"IEEE Access"},{"key":"ref_126","first-page":"1","article-title":"Internet of Things Based Intelligent Techniques in Workable Computing: An Overview","volume":"2021","author":"Guo","year":"2021","journal-title":"Sci. Program."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Nasralla, M.M., Umar, M.M., Rehman, G.U., Khan, S., and Choudhury, N. (2022). An Efficient Multilevel Probabilistic Model for Abnormal Traffic Detection in Wireless Sensor Networks. Sensors, 22.","DOI":"10.3390\/s22020410"},{"key":"ref_128","first-page":"147","article-title":"Identification and Prediction of Internet Traffic Using Artificial Neural Networks","volume":"2","author":"Chabaa","year":"2010","journal-title":"J. Intell. Learn. Syst. Appl."},{"key":"ref_129","first-page":"2238","article-title":"When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network","volume":"8","author":"Shuai","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.1631\/FITEE.1800573","article-title":"Cyber security meets artificial intelligence: A survey","volume":"19","author":"Li","year":"2018","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"e06522","DOI":"10.1016\/j.heliyon.2021.e06522","article-title":"Human factor, a critical weak point in the information security of an organization\u2019s Internet of things","volume":"7","author":"Li","year":"2021","journal-title":"Heliyon"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Cram, W.A., University of Waterloo, Proudfoot, J.G., D\u2019Arcy, J., Bentley University, and University of Delaware (2020). Maximizing Employee Compliance with Cybersecurity Policies. MIS Q. Exec., 183\u2013198.","DOI":"10.17705\/2msqe.00032"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Bal, P.K., Mohapatra, S.K., Das, T.K., Srinivasan, K., and Hu, Y.-C. (2022). A Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques. Sensors, 22.","DOI":"10.3390\/s22031242"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1016\/j.future.2019.06.026","article-title":"A cloud-edge based data security architecture for sharing and analysing cyber threat information","volume":"102","author":"Chadwick","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Gupta, A., and Srinivasan, K. (2019, January 20\u201322). Quantum Computing: A Brief Study. Proceedings of the 2019 IEEE International Conference on Consumer Electronics\u2014Taiwan (ICCE-TW), Yilan, Taiwan.","DOI":"10.1109\/ICCE-TW46550.2019.8991904"},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Badsha, S., Vakilinia, I., and Sengupta, S. (2019, January 7\u20139). Privacy Preserving Cyber Threat Information Sharing and Learning for Cyber Defense. Proceedings of the 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC.2019.8666477"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Mavroeidis, V., and Bromander, S. (2017, January 11\u201313). Cyber Threat Intelligence Model: An Evaluation of Taxonomies, Sharing Standards, and Ontologies within Cyber Threat Intelligence. Proceedings of the 2017 European Intelligence and Security Informatics Conference (EISIC), Athens, Greece.","DOI":"10.1109\/EISIC.2017.20"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Hrri, J., and Bonnet, C. (2009). Security in Mobile Telecommunication Networks. Wireless and Mobile Network Security, John Wiley and Sons.","DOI":"10.1002\/9780470611883.ch9"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"271","DOI":"10.4304\/jcp.6.2.271-279","article-title":"An Efficient Global K-means Clustering Algorithm","volume":"6","author":"Xie","year":"2011","journal-title":"J. Comput."},{"key":"ref_140","first-page":"723","article-title":"An Efficient Sound and Data Steganography Based Secure Authentication System","volume":"67","author":"Datta","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"33789","DOI":"10.1109\/ACCESS.2018.2841987","article-title":"Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection","volume":"6","author":"Ahmad","year":"2018","journal-title":"IEEE Access"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Liu, H., and Lang, B. (2019). Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey. Appl. Sci., 9.","DOI":"10.3390\/app9204396"},{"key":"ref_143","first-page":"73","article-title":"QoS-Aware Routing in Wireless Networks Using Aerial Vehicles","volume":"19","author":"Sharma","year":"2018","journal-title":"J. Internet Technol."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"137","DOI":"10.7763\/IJMLC.2015.V5.497","article-title":"Evaluation of Machine Learning Method for Intrusion Detection System on Jubatus","volume":"5","author":"Ogino","year":"2015","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_145","unstructured":"Kumar, N.S., Vincent, P.M.D.R., Srinivasan, K., Rajagopal, S., Angayarkanni, S.A., and Alhadidi, B. (2021, January 30\u201331). Role of M-CORD Computing Architecture for Over the Top (OTT) Services and Applications. Proceedings of the International Conference on Innovative Computing and Cuttingedge Technologies, Istanbul, Turkey."},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Ma, X., Zhang, X., Dong, C., and Chen, X. (2021). A Survey on Secure Outsourced Deep Learning. Cyber Security Meets Machine Learning, Springer.","DOI":"10.1007\/978-981-33-6726-5_6"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MCOM.2019.1900271","article-title":"The Roadmap to 6G: AI Empowered Wireless Networks","volume":"57","author":"Letaief","year":"2019","journal-title":"IEEE Commun. Mag."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Sesto-Castilla, D., Garcia-Villegas, E., Lyberopoulos, G., and Theodoropoulou, E. (2019, January 15\u201318). Use of Machine Learning for energy efficiency in present and future mobile networks. Proceedings of the 2019 IEEE Wireless Communications and Networking Conference (WCNC), Marrakesh, Morocco.","DOI":"10.1109\/WCNC.2019.8885478"},{"key":"ref_149","first-page":"297","article-title":"Discussion and Research on Information Security Attack and Defense Platform Construction in Universities Based on Cloud Computing and Virtualization","volume":"07","author":"Ding","year":"2016","journal-title":"J. Inf. Secur."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Sriram, P.P., Wang, H.-C., Jami, H.G., and Srinivasan, K. (2019). 5G Security: Concepts and Challenges, Springer International Publishing.","DOI":"10.1007\/978-3-030-03508-2_1"},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Kotenko, I., Izrailov, K., and Buinevich, M. (2022). Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches. Sensors, 22.","DOI":"10.3390\/s22041335"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3337065","article-title":"Big Data Analytics for Large-scale Wireless Networks","volume":"52","author":"Dai","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"17","DOI":"10.32604\/iasc.2021.018380","article-title":"Expert System for Stable Power Generation Prediction in Microbial Fuel Cell","volume":"29","author":"Srinivasan","year":"2021","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Zwilling, M. (2022). Trends and Challenges Regarding Cyber Risk Mitigation by CISOs\u2014A Systematic Literature and Experts\u2019 Opinion Review Based on Text Analytics. Sustainability, 14.","DOI":"10.3390\/su14031311"},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Schlatt, V., Guggenberger, T., Schmid, J., and Urbach, N. (2022). Attacking the trust machine: Developing an information systems research agenda for blockchain cybersecurity. Int. J. Inf. Manag., 102470.","DOI":"10.1016\/j.ijinfomgt.2022.102470"},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Yang, B., Guo, H., and Cao, E. (2020). Design of cyber-physical-social systems with forensic-awareness based on deep learning. Advances in Computers, Elsevier.","DOI":"10.1016\/bs.adcom.2020.09.001"},{"key":"ref_157","first-page":"1942","article-title":"Extension clustering-based extreme learning machine neural network","volume":"33","author":"Luo","year":"2013","journal-title":"J. Comput. Appl."},{"key":"ref_158","first-page":"4109","article-title":"Performance Comparison of Deep CNN Models for Detecting Driver\u2019s Distraction","volume":"68","author":"Srinivasan","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1007\/s11831-020-09478-2","article-title":"A Review on the Effectiveness of Machine Learning and Deep Learning Algorithms for Cyber Security","volume":"28","author":"Geetha","year":"2020","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.ijinfomgt.2019.01.021","article-title":"Artificial intelligence for decision making in the era of Big Data\u2014Evolution, challenges and research agenda","volume":"48","author":"Duan","year":"2019","journal-title":"Int. J. Inf. Manag."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/MCI.2010.938364","article-title":"Deep Machine Learning\u2014A New Frontier in Artificial Intelligence Research [Research Frontier]","volume":"5","author":"Arel","year":"2010","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.jpdc.2020.03.012","article-title":"MalFCS: An effective malware classification framework with automated feature extraction based on deep convolutional neural networks","volume":"141","author":"Xiao","year":"2020","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Srinivasan, K., and Agrawal, N.K. (2018, January 13\u201317). A study on M-CORD based architecture in traffic offloading for 5G-enabled multiaccess edge computing networks. Proceedings of the 2018 IEEE International Conference on Applied System Invention (ICASI), Chiba, Japan.","DOI":"10.1109\/ICASI.2018.8394593"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/S0140-3664(99)00187-5","article-title":"Intrusion Detection; Network Security Beyond the Firewall","volume":"23","author":"Oppliger","year":"2000","journal-title":"Comput. Commun."},{"key":"ref_165","unstructured":"Buschkes, R., Kesdogan, D., and Reichl, P. (1998, January 7\u201311). How to increase security in mobile networks by anomaly detection. Proceedings of the 14th Annual Computer Security Applications Conference (Cat. No. 98EX217), Phoenix, AZ, USA."},{"key":"ref_166","unstructured":"Looi, M. (2001, January 21\u201329). Enhanced authentication services for Internet systems using mobile networks. Proceedings of the GLOBECOM\u201901. IEEE Global Telecommunications Conference (Cat. No. 01CH37270), San Antonio, TX, USA."},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Srinivasan, K., Cherukuri, A.K., and Das, T.K. (2021). Realizing a Ultra-Low Latency M-CORD Model for Real-Time Traffic Settings in Smart Cities. Innovations in the Industrial Internet of Things (IIoT) and Smart Factory, IGI Global.","DOI":"10.4018\/978-1-7998-3375-8.ch007"},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Taherdoost, H. (2021). A Review on Risk Management in Information Systems: Risk Policy, Control and Fraud Detection. Electronics, 10.","DOI":"10.3390\/electronics10243065"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1108\/09576050210447046","article-title":"Perceived information security, financial liability and consumer trust in electronic commerce transactions","volume":"15","author":"Chellappa","year":"2002","journal-title":"Logist. Inf. Manag."},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Srinivasan, K., Agrawal, N.K., Cherukuri, A.K., and Pounjeba, J. (2018, January 19\u201321). An M-CORD Architecture for Multi-Access Edge Computing: A Review. Proceedings of the 2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Taichung, Taiwan.","DOI":"10.1109\/ICCE-China.2018.8448950"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/S0167-4048(02)00504-7","article-title":"Information security policy\u2014What do international information security standards say?","volume":"21","author":"Eloff","year":"2002","journal-title":"Comput. Secur."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1108\/09576050210447019","article-title":"An information security meta-policy for emergent organizations","volume":"15","author":"Baskerville","year":"2002","journal-title":"Logist. Inf. Manag."},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Srinivasan, K., Mahendran, N., Vincent, D.R., Chang, C.-Y., and Syed-Abdul, S. (2020). Realizing an Integrated Multistage Support Vector Machine Model for Augmented Recognition of Unipolar Depression. Electronics, 9.","DOI":"10.3390\/electronics9040647"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"321","DOI":"10.3844\/jcssp.2020.321.329","article-title":"Assessing Information Security Vulnerabilities and Threats to Implementing Security Mechanism and Security Policy Audit","volume":"16","author":"Afifi","year":"2020","journal-title":"J. Comput. Sci."},{"key":"ref_175","first-page":"3574675","article-title":"Information Security Risk Assessment Method for Ship Control System Based on Fuzzy Sets and Attack Trees","volume":"2019","author":"Shang","year":"2019","journal-title":"Secur. Commun. Networks"},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/IOTM.0001.2000041","article-title":"A Multifaceted Vigilare System for Intelligent Transportation Services in Smart Cities","volume":"3","author":"Kumar","year":"2020","journal-title":"IEEE Internet Things Mag."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1016\/j.comcom.2004.07.038","article-title":"End-to-end support for statistical quality of service in heterogeneous mobile ad hoc networks","volume":"28","author":"Kamal","year":"2005","journal-title":"Comput. Commun."},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Fichtner, L. (2018). What kind of cyber security? Theorising cyber security and mapping approaches. Internet Policy Rev., 7.","DOI":"10.14763\/2018.2.788"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/2017\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:32:06Z","timestamp":1760135526000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/2017"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,4]]},"references-count":178,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22052017"],"URL":"https:\/\/doi.org\/10.3390\/s22052017","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,4]]}}}