A Thorough Examination of the Importance of Machine Learning and Deep Learning Methodologies in the Realm of Cybersecurity: An Exhaustive Analysis
by Ramsha Khalid 1, 2 
, Muhammad Naqi Raza 1
1 Electrical Engineering Technology, University of Sialkot, Sialkot, 51310, Pakistan
2 Electrical Engineering, University of Lahore, Lahore, 53720, Pakistan
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 7, Page # 11-22, 2024; DOI: 10.55708/js0307002
Keywords: Cybersecurity, Cyber attackers, Artificial intelligence, Machine learning, Deep learning, Communication systems, Unauthorized entry
Received: 10 May, 2024, Revised: 17 June, 2024, Accepted: 18 June, 2024, Published Online: 13 July, 2024
APA Style
Khalid, R., & Raza, M. N. (2024). A thorough examination of the importance of machine learning and deep learning methodologies in the realm of cybersecurity: An exhaustive analysis. Journal of Engineering Research and Sciences, 3(7), 11-22. https://doi.org/10.55708/js0307002
Chicago/Turabian Style
Khalid, Ramsha, and Muhammad Naqi Raza. “A Thorough Examination of the Importance of Machine Learning and Deep Learning Methodologies in the Realm of Cybersecurity: An Exhaustive Analysis.” Journal of Engineering Research and Sciences 3, no. 7 (2024): 11-22. https://doi.org/10.55708/js0307002.
IEEE Style
Khalid and M. N. Raza, “A Thorough Examination of the Importance of Machine Learning and Deep Learning Methodologies in the Realm of Cybersecurity: An Exhaustive Analysis,” Journal of Engineering Research and Sciences, vol. 3, no. 7, pp. 11-22, 2024, doi: 10.55708/js0307002.
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Abstract
In today’s digital age, individuals extensively engage with virtual environments hosting a plethora of public and private services alongside social platforms. As a consequence, safeguarding these environments from potential cyber threats such as data breaches and system disruptions becomes paramount. Cybersecurity encompasses a suite of technical, organizational, and managerial measures aimed at thwarting unauthorized access or misuse of electronic information and communication systems. Its objectives include ensuring operational continuity, safeguarding the confidentiality and integrity of sensitive data, and shielding consumers from various forms of cyber intrusions. This paper delves into the realm of cybersecurity practices devised to fortify computer systems against diverse threats including hacking and data breaches. It examines the pivotal role of artificial intelligence within this domain, offering insights into the utilization of machine learning and deep learning techniques. Moreover, it synthesizes key findings from relevant literature exploring the efficacy and impact of these advanced methodologies in cybersecurity. Findings underscore the substantial contributions of machine learning and deep learning techniques in fortifying computer systems against unauthorized access and mitigating the risks posed by malicious software. These methodologies facilitate proactive measures by predicting and comprehending the behavioral patterns and traffic associated with potential cyber threats.
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- N. Bhalaji, “Reliable Data Transmission with Heightened Confidentiality and Integrity in IOT Empowered Mobile Networks,” Journal of IoT in Social, Mobile, Analytics, and Cloud, vol. 2, no. 2, 106–117, 2020, doi:10.36548/jismac.2020.2.004.
- J. Budd, B.S. Miller, E.M. Manning, V. Lampos, M.Z. et al., “Digital technologies in the public-health response to COVID-19,” Nature Medicine, vol. 26, 1183–1192, 2020, doi:10.1038/s41591-020-1011-4.
- K. Leung, J.T. Wu, G.M. Leung, “Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing,” Nature Communications, vol. 12, no. 1501, 1–8, 2021, doi:10.1038/s41467-021-21776-2.
- S. Shrestha, S. Haque, S. Dawadi, R.A. Giri, “Preparations for and practices of online education during the Covid-19 pandemic: A study of Bangladesh and Nepal,” Education and Information Technologies, vol. 27, 243–265, 2021, doi:10.1007/s10639-021-10659-0.
- M. Ssenyonga, “Imperatives for post COVID-19 recovery of Indonesia’s education, labor, and SME sectors,” Cogent Economics & Finance, vol. 9, no. 1, 1–51, 2021, doi:10.1080/23322039.2021.1911439.
- H. Saleous, M. Ismail, S.H. AlDaajeh, N. Madathil, S. Alrabaee, “COVID-19 pandemic and the cyberthreat landscape: Research challenges and opportunities,” Digital Communications and Networks, vol. In press, , 2022, doi:10.1016/j.dcan.2022.06.005.
- H.S. Lallie, L.A. Shepherd, J.R.C. Nurse, A. Erola, G.E. et al., “Cyber security in the age of COVID-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic,” Computers & Security, vol. 105, 102248, 2021, doi:10.1016/j.cose.2021.102248.
- J. Li, “Cyber security meets artificial intelligence: a survey,” Frontiers of Information Technology & Electronic Engineering, vol. 19, 1462–1474, 2019, doi:10.1631/FITEE.1800573.
- Z. Zhang, H. Ning, F. Shi, F. Farha, Y. Xu, F.Z. et al., “Artificial intelligence in cyber security: research advances, challenges, and opportunities,” Artificial Intelligence Review, vol. 55, 1029–1053, 2021, doi:10.1007/s10462-021-09976-0.
- M.M. Mijwil, “Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans,” Iraqi Journal of Science, vol. 62, no. 6, 2099–2109, 2021, doi:10.24996/ijs.2021.62.6.35.
- J. Cáceres-Hidalgo, D. Avila-Pesantez, “Cybersecurity Study in 5G Network Slicing Technology: A Systematic Mapping Review,” in Proceedings of IEEE Fifth Ecuador Technical Chapters Meeting, IEEE, Cuenca, Ecuador: 1–6, 2021, doi:10.1109/ETCM53643.2021.9590742.
- T. Ghosh, H. Al Banna, S. Rahman, S. Kaiser, M.M. et al., “Artificial intelligence and internet of things in screening and management of autism spectrum disorder,” Sustainable Cities and Society, vol. 74, 103189, 2021, doi:10.1016/j.scs.2021.103189.
- A. Adadi, M. Lahmer, S. Nasiri, “Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead,” Journal of King Saud University – Computer and Information Sciences, vol. 34, no. 8, 5898–5920, 2022, doi:10.1016/j.jksuci.2021.07.010.
- M. Abdullahi, Y. Baashar, H. Alhussian, A. Alwadain, N.A. et al., “Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review,” Electronics, vol. 11, no. 2, 1–27, 2022, doi:10.3390/electronics11020198.
- I.F. Kilincer, F. Ertam, A. Sengur, “Machine learning methods for cyber security intrusion detection: Datasets and comparative study,” Computer Networks, vol. 188, 107840, 2021, doi:10.1016/j.comnet.2021.107840.
- S. Kuipers, M. Schonheit, “Data Breaches and Effective Crisis Communication: A Comparative Analysis of Corporate Reputational Crises,” Corporate Reputation Review, vol. 25, 176–197, 2021, doi:10.1057/s41299-021-00121-9.
- N. Rawindaran, A. Jayal, E. Prakash, C. Hewage, “Cost Benefits of Using Machine Learning Features in NIDS for Cyber Security in UK Small Medium Enterprises (SME),” Future Internet, vol. 13, no. 8, 1–36, 2021, doi:10.3390/fi13080186.
- F. Quayyum, D.S. Cruzes, L. Jaccheri, “Cybersecurity awareness for children: A systematic literature review,” International Journal of Child-Computer Interaction, vol. 30, 100343, 2021, doi:10.1016/j.ijcci.2021.100343.
- P. Formosa, M. Wilson, D. Richards, “A principlist framework for cybersecurity ethics,” Computers & Security, vol. 109, 102382, 2021, doi:10.1016/j.cose.2021.102382.
- I.H. Sarker, H. Furhad, R. Nowrozy, “AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions,” SN Computer Science, vol. 2, no. 173, 2021, doi:10.1007/s42979-021-00557-0.
- E. Fosch-Villaronga, T. Mahler, “Cybersecurity, safety and robots: Strengthening the link between cybersecurity and safety in the context of care robots,” Computer Law & Security Review, vol. 41, 105528, 2021, doi:10.1016/j.clsr.2021.105528.
- P. Sharma, S. Jain, S. Gupta, V. Chamola, “Role of machine learning and deep learning in securing 5G-driven industrial IoT applications,” Ad Hoc Networks, vol. 123, 102685, 2021, doi:10.1016/j.adhoc.2021.102685.
- A. Rehman, T. Saba, T. Mahmood, Z. Mehmood, M.S. et al., “Data hiding technique in steganography for information security using number theory,” Journal of Information Science, vol. 45, no. 6, 767–778, 2018, doi:10.1177/0165551518816303.
- G. Hale, C. Bartlett, “Managing the Regulatory Tangle: Critical Infrastructure Security and Distributed Governance in Alberta’s Major Traded Sectors,” Journal of Borderlands Studies, vol. 34, no. 2, 257–279, 2018, doi:10.1080/08865655.2017.1367710.
- Y. Wang, A. Smahi, H. Zhang, H. Li, “Towards Double Defense Network Security Based on Multi-Identifier Network Architecture,” Sensors, vol. 22, no. 3, 1–17, 2022, doi:10.3390/s22030747.
- D.G. Broo, U. Boman, M. Törngren, “Cyber-physical systems research and education in 2030: Scenarios and strategies,” Journal of Industrial Information Integration, vol. 21, 100192, 2021, doi:10.1016/j.jii.2020.100192.
- M.M. Mijwil, “Malware Detection in Android OS Using Machine Learning Techniques,” Data Science and Applications, vol. 3, no. 2, 5–9, 2020.
- U. Urooj, B.A.S. Al-rimy, A. Zainal, F.A. Ghaleb, M.A. Rassam, “Ransomware Detection Using the Dynamic Analysis and Machine Learning: A Survey and Research Directions,” Applied Sciences, vol. 12, no. 1, 1–45, 2021, doi:10.3390/app12010172.
- A.F. AL-Otaibi, E.S. Alsuwat, “A Study on Social Engineering Attacks: Phishing Attack,” International Journal of Recent Advances in Multidisciplinary Research, vol. 7, no. 11, 6374–6379, 2020.
- A. Narote, V. Zutshi, A. Potdar, R. Vichare, “Detection of DDoS Attacks using Concepts of Machine Learning,” International Journal for Research in Applied Science & Engineering Technology, vol. 10, no. VI, 390–403, 2022.
- N. Bedeković, L. Havaš, T. Horvat, D. Crčić, “The Importance of Developing Preventive Techniques for SQL Injection Attacks,” Tehnički Glasnik, vol. 16, no. 4, 523–529, 2022, doi:10.31803/tg-20211203090618.
- U.K. Singh, C. Joshi, D. Kanellopoulos, “A framework for zero-day vulnerabilities detection and prioritization,” Journal of Information Security and Applications, vol. 46, 164–172, 2019, doi:10.1016/j.jisa.2019.03.011.
- Y. Wang, A. Zhou, S. Liao, R. Zheng, R. Hu, L. Zhang, “A comprehensive survey on DNS tunnel detection,” Computer Networks, vol. 179, 108322, 2021, doi:10.1016/j.comnet.2021.108322.
- Y. Zhou, P. Wang, “An ensemble learning approach for XSS attack detection with domain knowledge and threat intelligence,” Computers & Security, vol. 82, 261–269, 2019, doi:10.1016/j.cose.2018.12.016.
- J. He, C. Chang, P. He, M.S. Pathan, “Network Forensics Method Based on Evidence Graph and Vulnerability Reasoning,” Future Internet, vol. 8, no. 4, 1–18, 2016, doi:10.3390/fi8040054.
- M.P. Singh, A. Bhandari, “New-flow based DDoS attacks in SDN: Taxonomy, rationales, and research challenges,” Computer Communications, vol. 15, 509–527, 2020, doi:10.1016/j.comcom.2020.02.085.
- J.L.G. Torres, C.A. Catania, E. Veas, “Active learning approach to label network traffic datasets,” Journal of Information Security and Applications, vol. 49, 102388, 2019, doi:10.1016/j.jisa.2019.102388.
- S. Choudhary, N. Kesswani, “Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 Datasets using Deep Learning in IoT,” Procedia Computer Science, vol. 167, 1561–1573, 2020, doi:10.1016/j.procs.2020.03.367.
- L. Dhanabal, S.P. Shantharajah, “A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, no. 6, 446–452, 2015.
- B. Bouyeddou, F. Harrou, B. Kadri, Y. Sun, “Detecting network cyber-attacks using an integrated statistical approach,” Cluster Computing, vol. 24, 1435–1453, 2020, doi:10.1007/s10586-020-03203-1.
- M. Idhammad, K. Afdel, M. Belouch, “Semi-supervised machine learning approach for DDoS detection,” Applied Intelligence, vol. 48, 3193–3208, 2018, doi:10.1007/s10489-018-1141-2.
- N. Koroniotis, N. Moustafa, E. Sitnikova, B. Turnbull, “Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset,” Future Generation Computer Systems, vol. 100, 779–796, 2019, doi:10.1016/j.future.2019.05.041.
- I.H. Sarker, “Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective,” SN Computer Science, vol. 2, no. 154, 1–16, 2021, doi:10.1007/s42979-021-00535-6.
- S.M. Kasongo, Y. Sun, “Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset,” Journal of Big Data, vol. 7, no. 105, 1–20, 2020, doi:10.1186/s40537-020-00379-6.
- R.T. S., R. Sathya, “Ensemble Machine Learning Techniques for Attack Prediction in NIDS Environment,” Iraqi Journal For Computer Science and Mathematics, vol. 3, no. 2, 78–82, 2022, doi:10.52866/ijcsm.2022.02.01.008.
- Y. Niu, A. Korneev, “Identification Method of Power Internet Attack Information Based on Machine Learning,” Iraqi Journal For Computer Science and Mathematics, vol. 3, no. 2, 1–7, 2022, doi:10.52866/ijcsm.2022.02.01.001.
- M.M. Mijwil, E.A. Al-Zubaidi, “Medical Image Classification for Coronavirus Disease (COVID-19) Using Convolutional Neural Networks,” Iraqi Journal of Science, vol. 62, no. 8, 2740–2747, 2021, doi:10.24996/ijs.2021.62.8.27.
- M. Sarhan, S. Layeghy, N. Moustafa, M. Gallagher, M. Portmann, “Feature extraction for machine learning-based intrusion detection in IoT networks,” Digital Communications and Networks, vol. In press, , 2022, doi:10.1016/j.dcan.2022.08.012.
- M.A. Teixeira, T. Salman, M. Zolanvari, R. Jain, N. Meskin, M. Samaka, “SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach,” Future Internet, vol. 10, no. 8, 1–15, 2018, doi:10.3390/fi10080076.
- K. Aggarwal, M.M. Mijwil, Sonia, A.H. Al-Mistarehi, S. Alomari, M. Gök, A.M. Alaabdin, S.H. Abdulrhman, “Has the Future Started? The Current Growth of Artificial Intelligence, Machine Learning, and Deep Learning,” Iraqi Journal for Computer Science and Mathematics, vol. 3, no. 1, 115–123, 2022, doi:10.52866/ijcsm.2022.01.01.013.
- L.F. Maimó, A.H. Celdrán, A.L.P. Gómez, F.J.G. Clemente, J. Weimer, I. Lee, “Intelligent and Dynamic Ransomware Spread Detection and Mitigation in Integrated Clinical Environments,” Sensors, vol. 19, no. 5, 1–31, 2019, doi:10.3390/s19051114.
- V.M. Rios, P.R.M. Inácio, D. Magoni, M.M. Freire, “Detection of reduction-of-quality DDoS attacks using Fuzzy Logic and machine learning algorithms,” Computer Networks, vol. 186, 107792, 2021, doi:10.1016/j.comnet.2020.107792.
- Y. Li, J. Xia, S. Zhang, J. Yan, X. Ai, K. Dai, “An efficient intrusion detection system based on support vector machines and gradually feature removal method,” Expert Systems with Applications, vol. 39, no. 1, 424–430, 2012, doi:10.1016/j.eswa.2011.07.032.
- W. Meng, W. Li, L. Kwok, “Design of intelligent KNN-based alarm filter using knowledge-based alert verification in intrusion detection,” Security and Communication Networks, vol. 8, no. 18, 3883–3895, 2015, doi:10.1002/sec.1307.
- A. Mahindru, A.L. Sangal, “MLDroid—framework for Android malware detection using machine learning techniques,” Neural Computing and Applications, vol. 33, 5183–5240, 2020, doi:10.1007/s00521-020-05309-4.
- H. Zuhair, A. Selamat, “RANDS: A Machine Learning-Based Anti-Ransomware Tool for Windows Platforms,” in Advancing Technology Industrialization Through Intelligent Software Methodologies, Tools and Techniques, 573–587, 2019, doi:10.3233/FAIA190081.
- U. Adamu, I. Awan, “Ransomware Prediction Using Supervised Learning Algorithms,” in Proceedings of International Conference on Future Internet of Things and Cloud, Istanbul, Turkey: 1–6, 2019, doi:10.1109/FiCloud.2019.00016.
- S. Puthran, K. Shah, “Intrusion Detection Using Improved Decision Tree Algorithm with Binary and Quad Split,” in Proceedings of International Symposium on Security in Computing and Communication, 427–438, 2016, doi:10.1007/978-981-10-2738-3_37.
- J. Zhang, M. Zulkernine, A. Haque, “Random-Forests-Based Network Intrusion Detection Systems,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 38, no. 5, 649–659, 2008, doi:10.1109/TSMCC.2008.923876.
- F. Musumeci, A.C. Fidanci, F. Paolucci, F. Cugini, M. Tornatore, “Machine-Learning-Enabled DDoS Attacks Detection in P4 Programmable Networks,” Journal of Network and Systems Management, vol. 30, no. 21, 2021, doi:10.1007/s10922-021-09633-5.
- A.M. Chandrasekhar, K. Raghuveer, “Confederation of FCM clustering, ANN and SVM techniques to implement hybrid NIDS using corrected KDD cup 99 dataset,” in Proceedings of International Conference on Communication and Signal Processing, Melmaruvathur, India: 1–6, 2014, doi:10.1109/ICCSP.2014.6949927.
- S. Ahmed, Z.A. Abbood, H.M. Farhan, B.T. Yasen, M.R. Ahmed, A.D. Duru, “Speaker Identification Model Based on Deep Neural Networks,” Iraqi Journal For Computer Science and Mathematics, vol. 3, no. 1, 108–114, 2022, doi:10.52866/ijcsm.2022.01.01.012.
- A.K. Faieq, M.M. Mijwil, “Prediction of Heart Diseases Utilising Support Vector Machine and Artificial Neural Network,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 26, no. 1, 374–380, 2022, doi:10.11591/ijeecs.v26.i1.pp374-380.
- M.M. Mijwil, R.A. Abttan, A. Alkhazraji, “Artificial intelligence for COVID-19: A Short Article,” Asian Journal of Pharmacy, Nursing and Medical Sciences, vol. 10, no. 1, 1–6, 2022, doi:10.24203/ajpnms.v10i1.6961.
- K. Shaukat, S. Luo, V. Varadharajan, I.A. Hameed, S. Chen, et al., “Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity,” Energies, vol. 13, no. 10, 1–27, 2020, doi:10.3390/en13102509.
- D. Chen, P. Wawrzynski, Z. Lv, “Cyber security in smart cities: A review of deep learning-based applications and case studies,” Sustainable Cities and Society, vol. 66, 102655, 2021, doi:10.1016/j.scs.2020.102655.
- P. Suresh, K. Logeswaran, R.M. Devi, K. Sentamilselvan, G.K. Kamalam, H. Muthukrishnan, Contemporary survey on effectiveness of machine and deep learning techniques for cyber security, 177–200, 2022, doi:10.1016/B978-0-323-85209-8.00007-9.
- M. Taseer, H. Ghafory, “SQL Injection Attack Detection Using Machine Learning Algorithm,” Mesopotamian Journal of CyberSecurity, 5–17, 2022, doi:10.58496/MJCS/2022/002.
- I.E. Salem, M. Mijwil, A.W. Abdulqader, M.M. Ismaeel, A. Alkhazraji, A.M.Z. Alaabdin, “Introduction to The Data Mining Techniques in Cybersecurity,” Mesopotamian Journal of CyberSecurity, 28–37, 2022, doi:10.58496/MJCS/2022/004.
- R.T. Rasheed, Y. Niu, S.N. Abd, “Harmony Search for Security Enhancement,” Mesopotamian Journal of CyberSecurity, 5–8, 2021, doi:10.58496/MJCS/2021/002.
- T.H.H. Aldhyani, H. Alkahtani, “Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity,” Sensors, vol. 22, no. 1, 1–20, 2022, doi:10.3390/s22010360.
- G. Loukas, T. Vuong, R. Heartfield, G. Sakellari, Y. Yoon, et al., “Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning,” IEEE Access, vol. 6, 3491–3508, 2017, doi:10.1109/ACCESS.2017.2782159.
- J. Yin, M. Tang, J. Cao, H. Wang, “Apply transfer learning to cybersecurity: Predicting exploitability of vulnerabilities by description,” Knowledge-Based Systems, vol. 210, 106529, 2020, doi:10.1016/j.knosys.2020.106529.
- Z. Tian, C. Luo, J. Qiu, X. Du, M. Guizani, “A Distributed Deep Learning System for Web Attack Detection on Edge Devices,” IEEE Transactions on Industrial Informatics, vol. 16, no. 3, 1963–1971, 2020, doi:10.1109/TII.2019.2938778.
- A. Thirumalairaj, M. Jeyakarthic, “Perimeter Intrusion Detection with Multi Layer Perception using Quantum Classifier,” in Proceedings of International Conference on Inventive Systems and Control, Coimbatore, India: 1–6, 2020, doi:10.1109/ICISC47916.2020.9171159.
- K. Atefi, H. Hashim, M. Kassim, “Anomaly Analysis for the Classification Purpose of Intrusion Detection System with K-Nearest Neighbors and Deep Neural Network,” in Proceedings of Conference on Systems, Process and Control, Melaka, Malaysia: 1–6, 2019, doi:10.1109/ICSPC47137.2019.9068081.
- M. Almiani, A. AbuGhazleh, A. Al-Rahayfeh, S. Atiewi, A. Razaque, “Deep recurrent neural network for IoT intrusion detection system,” Simulation Modelling Practice and Theory, vol. 101, 102031, 2020, doi:10.1016/j.simpat.2019.102031.
- K. Alrawashdeh, C. Purdy, “Toward an Online Anomaly Intrusion Detection System Based on Deep Learning,” in Proceedings of International Conference on Machine Learning and Applications, Anaheim, CA, USA: 1–6, 2016, doi:10.1109/ICMLA.2016.0040.
- L. Gupta, T. Salman, A. Ghubaish, D. Unal, A.K. Al-Ali, R. Jain, “Cybersecurity of multi-cloud healthcare systems: A hierarchical deep learning approach,” Applied Soft Computing, vol. 118, 108439, 2022, doi:10.1016/j.asoc.2022.108439.
- W. Wang, M. Zhu, X. Zeng, X. Ye, Y. Sheng, “Malware traffic classification using convolutional neural network for representation learning,” in Proceedings of International Conference on Information Networking, Da Nang, Vietnam: 1–6, 2017, doi:10.1109/ICOIN.2017.7899588.
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