Optimizing Network Security with Machine Learning and Multi-Factor Authentication for Enhanced Intrusion Detection

Rafah Kareem Mahmood, Ans Ibrahim Mahameed, Noor Q. Lateef, Hasanain M. Jasim, Ahmed Dheyaa Radhi, Saadaldeen Rashid Ahmed, Priyanka Tupe-Waghmare

Abstract


This study examines the utilization of machine learning methodologies and multi-factor authentication (MFA) to bolster network security, specifically targeting network intrusion detection. We analyze the way in which the integration of these technologies effectively tackles existing security concerns and constraints. The research highlights the importance of incorporating energy conservation and environmental impact reduction into security solutions, in addition to traditional cryptography and biometric methods. In addition, we tackle the limitations of centralized systems, such as vulnerabilities to security breaches and instances of system failures. The study examines different security models, encompassing categories, frameworks, consensus protocols, applications, services, and deployment goals in order to determine their impact on network security. In addition, we offer a detailed comparison of seven machine learning models, showcasing their effectiveness in enhancing network intrusion detection and overall security. The objective of this study is to provide in-depth understanding and actionable suggestions for utilizing machine learning with MFA (Multi-Factor Authentication) to enhance network defensive tactics.

Keywords


Deep Learning; Network Security; Multi-Factor Authentication; Network Intrusion Detection; Machine Learning.

Full Text:

PDF

References


Y. Shi, Y. Xia, and Y. Gao, “Joint Gateway Selection and Resource Allocation for Cross-Tier Communication in Space-Air-Ground Integrated IoT Networks,” IEEE Access, vol. 9, pp. 4303–4314, 2021, doi: 10.1109/access.2020.3047891.

R. Liu, Y. Ma, X. Zhang, and Y. Gao, “Deep Learning-Based Spectrum Sensing in Space-Air-Ground Integrated Networks,” Journal of Communications and Information Networks, vol. 6, no. 1, pp. 82–90, Mar. 2021, doi: 10.23919/jcin.2021.9387707.

B. T. Yaseen, S. Kurnaz, and S. R. Ahmed, “Detecting and Classifying Drug Interaction using Data mining Techniques,” 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 952-956, Oct. 2022.‏

S. R. Ahmed, A. K. Ahmed, and S. J. Jwmaa, “Analyzing The Employee Turnover by Using Decision Tree Algorithm,” 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pp. 1-4, Jun. 2023.‏

N. Z. Mahmood, S. R. Ahmed, A. F. Al-Hayaly, S. Algburi and J. Rasheed, "The Evolution of Administrative Information Systems: Assessing the Revolutionary Impact of Artificial Intelligence," 2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1-7, 2023.

B. A. Abubaker, S. R. Ahmed, A. T. Guron, M. Fadhil, S. Algburi, and B. F. Abdulrahman, “Spiking Neural Network for Enhanced Mobile Robots’ Navigation Control,” 2023 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS), pp. 1-8, Nov. 2023.

A. K. Ahmed, S. Q. Younus, S. R. Ahmed, S. Algburi, and M. A. Fadhel, “A Machine Learning Approach to Employee Performance Prediction within Administrative Information Systems,” 2023 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS), pp. 1-7, Nov. 2023.

M. H. B. A. Alkareem, F. Q. Nasif, S. R. Ahmed, L. D. Miran, S. Algburi, and M. T. ALmashhadany, “Linguistics for Crimes in the World by AI-Based Cyber Security,” 2023 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS), pp. 1-5, Nov. 2023.

S. R. Ahmed, I. Ahmed Najm, A. Talib Abdulqader, and K. Basem Fadhil, “Energy improvement using Massive MIMO for soft cell in cellular communication,” IOP Conference Series: Materials Science and Engineering, vol. 928, no. 3, p. 032009, Nov. 2020.

M. Al Moteri, S. B. Khan, and M. Alojail, “Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT,” Systems, vol. 11, no. 6, p. 308, Jun. 2023, doi: 10.3390/systems11060308.

W. Zhong, S. Hu, Y. Ma, H. Yang, X. Ma, and B. Yu, “Deep Learning-Driven Simultaneous Layout Decomposition and Mask Optimization,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 41, no. 3, pp. 709–722, Mar. 2022, doi: 10.1109/tcad.2021.3061494.

H. Bangui and B. Buhnova, “Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey,” Procedia Computer Science, vol. 184, pp. 877–886, 2021, doi: 10.1016/j.procs.2021.04.014.

R. Saleem, W. Ni, M. Ikram, and A. Jamalipour, “Deep-Reinforcement-Learning-Driven Secrecy Design for Intelligent-Reflecting-Surface-Based 6G-IoT Networks,” IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8812–8824, May 2023, doi: 10.1109/jiot.2022.3232360.

M. Amrollahi, S. Hadayeghparast, H. Karimipour, F. Derakhshan, and G. Srivastava, “Enhancing Network Security Via Machine Learning: Opportunities and Challenges,” Handbook of Big Data Privacy, pp. 165–189, 2020, doi: 10.1007/978-3-030-38557-6_8.

R. Ahmad, R. Wazirali, and T. Abu-Ain, “Machine learning for wireless sensor networks security: An overview of challenges and issues,” Sensors, vol. 22, no. 13, p. 4730, 2022.

E. Akin, “Deep Reinforcement Learning-Based Multirestricted Dynamic-Request Transportation Framework,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–11, 2023, doi: 10.1109/tnnls.2023.3341471.

F. Ullah et al., “Cyber Security Threats Detection in Internet of Things Using Deep Learning Approach,” IEEE Access, vol. 7, pp. 124379–124389, 2019, doi: 10.1109/access.2019.2937347.

S. Strecker, W. Van Haaften, and R. Dave, “An Analysis of IoT Cyber Security Driven by Machine Learning,” Proceedings of International Conference on Communication and Computational Technologies, pp. 725–753, 2021, doi: 10.1007/978-981-16-3246-4_55.

P. Xiao, “Malware Cyber Threat Intelligence System for Internet of Things (IoT) Using Machine Learning,” Journal of Cyber Security and Mobility, pp. 53-90, Dec. 2023, doi: 10.13052/jcsm2245-1439.1313.

I. Kotenko, K. Izrailov, and M. Buinevich, “Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches,” Sensors, vol. 22, no. 4, p. 1335, Feb. 2022, doi: 10.3390/s22041335.

A. Rapaka, M. Prasad, R. R. Pbv, P. S. Murty, and K. S. Pokkuluri, “Enhancing Network Security: Leveraging Machine Learning for Intrusion Detection,” Journal of Electrical Systems, vol. 20, no. 2, pp. 1555-1562, 2024, doi: 10.52783/jes.1460.

R. Padmasree and K. Muthyam, “Enhancing IoT Network Security through Prompt Intrusion Detection Using Machine Learning,” International Journal of Computer Science and Engineering, vol. 11, no. 4, pp. 10–18, Apr. 2024, doi: 10.14445/23488387/ijcse-v11i4p102.

Z. Zhang, “Machine Learning for Network Intrusion Detection,” Encyclopedia of Cryptography, Security and Privacy, pp. 1–4, 2021, doi: 10.1007/978-3-642-27739-9_1631-1.

Z. Huang, Z. Li, and J. Zhang, “Enhancing network security through machine learning: A study on intrusion detection system using supervised algorithms,” Applied and Computational Engineering, vol. 19, no. 1, pp. 50–66, Oct. 2023, doi: 10.54254/2755-2721/19/20231008.

N. Awadallah Awad, “Enhancing Network Intrusion Detection Model Using Machine Learning Algorithms,” Computers, Materials & Continua, vol. 67, no. 1, pp. 979–990, 2021, doi: 10.32604/cmc.2021.014307.

A. A. Abro, R. S. A. Larik, S. A. Awan, A. O. Panhwar, and I. A. Kandhro, “Network Security Attack Classification: Leveraging Machine Learning Methods for Enhanced Detection and Defense,” International Journal of Electronic Security and Digital Forensics, vol. 1, no. 1, 2025, doi: 10.1504/ijesdf.2025.10062253.

P. Sukavatee and W. Sutthiroj, “Enhancing English oral communication skills and motivation: the impact of AR hotel situated-learning board game in Thai EFL contexts,” International Journal of Innovation and Learning, vol. 1, no. 1, 2025, doi: 10.1504/ijil.2025.10064230

D.-S. Tran, N.-H. Ho, H.-J. Yang, S.-H. Kim, and G. S. Lee, “Real-time virtual mouse system using RGB-D images and fingertip detection,” Multimedia Tools and Applications, vol. 80, no. 7, pp. 10473–10490, Nov. 2020, doi: 10.1007/s11042-020-10156-5.

O. Almomani, “A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms,” Symmetry, vol. 12, no. 6, p. 1046, Jun. 2020, doi: 10.3390/sym12061046.

V. Sstla, V. Kolli, and L. Voggu, “Predictive Model for Network Intrusion Detection System Using Deep Learning,” Revue d’Intelligence Artificielle, vol. 34, no. 3, pp. 323–330, Jun. 2020, doi: 10.18280/ria.340310.

A. M. Mahfouz, D. Venugopal, and S. G. Shiva, “Comparative Analysis of ML Classifiers for Network Intrusion Detection,” Fourth International Congress on Information and Communication Technology, pp. 193–207, 2020, doi: 10.1007/978-981-32-9343-4_16..

B. He, “Artificial Intelligent for Intelligent Manufacturing and Robotics,” Robotics & Automation Engineering Journal, vol. 1, no. 5, Jan. 2019, doi: 10.19080/raej.2018.01.555575.

B. Paikaray, P. K. Swain, S. Mohapatra, and S. Satpathy, “Securing Healthcare in the Cloud: A Machine Learning Perspective,” International Journal of Internet Manufacturing and Services, vol. 11, no. 2, 2025, doi: 10.1504/ijims.2025.10063954.

P. L. S. Jayalaxmi, R. Saha, G. Kumar, M. Conti, and T.-H. Kim, “Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey,” IEEE Access, vol. 10, pp. 121173–121192, 2022, doi: 10.1109/access.2022.3220622.

U. Tariq, “Intrusion Detection and Anticipation System (IDAS) for IEEE 802.15.4 Devices,” Intelligent Automation and Soft Computing, pp. 1–13, 2018, doi: 10.31209/2018.100000040.

S. A. Bakhsh, M. A. Khan, F. Ahmed, M. S. Alshehri, H. Ali, and J. Ahmad, “Enhancing IoT network security through deep learning-powered Intrusion Detection System,” Internet of Things, vol. 24, p. 100936, Dec. 2023, doi: 10.1016/j.iot.2023.100936.

E. E. Abdallah and A. F. Otoom, “Intrusion detection systems using supervised machine learning techniques: a survey,” Procedia Computer Science, vol. 201, pp. 205-212, 2022.

O. Ahmed, R. H. Thaher, and S. R. Ahmed, “Design and fabrication of UWB microstrip Antenna on different substrates for wireless Communication system,” in 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pp. 1-4, 2022.

A. Gupta and R. K. Jha, “Power optimization with low complexity using scaled beamforming approach for a massive MIMO and small cell scenario,” Wireless Networks, vol. 26, no. 2, pp. 1165-1176, 2020.

I. Al_Barazanchi et al., “Proposed New Framework Scheme for Path Loss in Wireless Body Area Network,” Iraqi Journal for Computer Science and Mathematics, pp. 11–21, Jan. 2022, doi: 10.52866/ijcsm.2022.01.01.002.

Q. Liu, V. Hagenmeyer, and H. B. Keller, “A Review of Rule Learning-Based Intrusion Detection Systems and Their Prospects in Smart Grids,” IEEE Access, vol. 9, pp. 57542–57564, 2021, doi: 10.1109/access.2021.3071263.

N. Duffield, P. Haffner, B. Krishnamurthy, and H. Ringberg, “Rule-Based Anomaly Detection on IP Flows,” IEEE INFOCOM 2019, pp. 424-432, Apr. 2009, doi: 10.1109/infcom.2009.5061947.

V. Kumar, D. Sinha, A. K. Das, S. C. Pandey, and R. T. Goswami, “An integrated rule based intrusion detection system: analysis on UNSW-NB15 data set and the real time online dataset,” Cluster Computing, vol. 23, no. 2, pp. 1397–1418, Oct. 2019, doi: 10.1007/s10586-019-03008-x.

K. Rasane, L. Bewoor, and V. Meshram, “A Comparative Analysis of Intrusion Detection Techniques: Machine Learning Approach,” SSRN Electronic Journal, 2019, doi: 10.2139/ssrn.3418748.

K. Rajora and N. salih Abdulhussein, “Reviews research on applying machine learning techniques to reduce false positives for network intrusion detection systems,” Babylonian Journal of Machine Learning, vol. 2023, pp. 26–30, May 2023, doi: 10.58496/bjml/2023/005.

P. Mishra, V. Varadharajan, U. Tupakula, and E. S. Pilli, “A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 686–728, 2019, doi: 10.1109/comst.2018.2847722.

G. J. Pandeeswari and S. Jeyanthi, “Analysis of Intrusion Detection Using Machine Learning Techniques,” 2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), pp. 1-5, Dec. 2022, doi: 10.1109/icatiece56365.2022.10047057.

I. A. Najm et al., “Enhanced Network Traffic Classification with Machine Learning Algorithms,” Proceedings of the Cognitive Models and Artificial Intelligence Conference, pp. 322-327, May 2024, doi: 10.1145/3660853.3660935.

A. S. Dina and D. Manivannan, “Intrusion detection based on Machine Learning techniques in computer networks,” Internet of Things, vol. 16, p. 100462, Dec. 2021, doi: 10.1016/j.iot.2021.100462.

I. A. Najm, A. K. Hamoud, J. Lloret, and I. Bosch, “Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment,” Electronics, vol. 8, no. 6, p. 607, May 2019, doi: 10.3390/electronics8060607.

W. S. Al-Dayyeni et al., “A Review on Electronic Nose: Coherent Taxonomy, Classification, Motivations, Challenges, Recommendations and Datasets,” IEEE Access, vol. 9, pp. 88535–88551, 2021, doi: 10.1109/access.2021.3090165.

I. A. Najm, M. Ismail, J. Lloret, K. Z. Ghafoor, B. B. Zaidan, and A. A. T. Rahem, “Improvement of SCTP congestion control in the LTE-A network,” Journal of Network and Computer Applications, vol. 58, pp. 119–129, Dec. 2019, doi: 10.1016/j.jnca.2015.09.003.

A. K. Hamoud et al., “A comparative study of supervised/unsupervised machine learning algorithms with feature selection approaches to predict student performance,” International Journal of Data Mining, Modelling and Management, vol. 15, no. 4, pp. 393–409, 2023, doi: 10.1504/ijdmmm.2023.134590.

M. Y. Ma’aji, “Models Comparison Based On Intrusion Detection Using Machine Learning,” SLU Journal of Science and Technology, pp. 74–86, Mar. 2023, doi: 10.56471/slujst.v6i.358.

I. F. Kilincer, F. Ertam, and A. Sengur, “Machine learning methods for cyber security intrusion detection: Datasets and comparative study,” Computer Networks, vol. 188, p. 107840, Apr. 2021, doi: 10.1016/j.comnet.2021.107840.

Md. E. Haque and T. M. Alkharobi, “Adaptive Hybrid Model for Network Intrusion Detection and Comparison among Machine Learning Algorithms,” International Journal of Machine Learning and Computing, vol. 5, no. 1, pp. 17–23, Feb. 2015, doi: 10.7763/ijmlc. 2015.v5.476.

T. Khorram and N. A. Baykan, “Network Intrusion Detection using Optimized Machine Learning Algorithms,” European Journal of Science and Technology, no. 25, pp. 463-474, Jun. 2021, doi: 10.31590/ejosat.849723.

A. Hamed Hamad, A. Yousif Dawod, M. Fakhrulddin Abdulqader, I. Al_Barazanchi, and H. Muwafaq Gheni, “A secure sharing control framework supporting elastic mobile cloud computing,” International Journal of Electrical and Computer Engineering (IJECE), vol. 13, no. 2, p. 2270, Apr. 2023, doi: 10.11591/ijece.v13i2.pp2270-2277.

A. Alghazali and Z. Hanoosh, “Using a Hybrid Algorithm with Intrusion Detection System based on Hierarchical Deep Learning for Smart Meter Communication Network,” Webology, vol. 19, no. 1, pp. 3850–3865, Jan. 2022, doi: 10.14704/web/v19i1/web19253.

A. Aldallal, “Toward Efficient Intrusion Detection System Using Hybrid Deep Learning Approach,” Symmetry, vol. 14, no. 9, p. 1916, Sep. 2022, doi: 10.3390/sym14091916.

F. M. Alotaibi, “Network Intrusion Detection Model Using Fused Machine Learning Technique,” Computers, Materials & Continua, vol. 75, no. 2, 2023.

J. Carneiro, N. Oliveira, N. Sousa, E. Maia, and I. Praça, “Machine Learning for Network-Based Intrusion Detection Systems: An Analysis of the CIDDS-001 Dataset,” Lecture Notes in Networks and Systems, pp. 148–158, Sep. 2021, doi: 10.1007/978-3-030-86261-9_15.

C. I. Nwakanma et al., “Explainable Artificial Intelligence (XAI) for Intrusion Detection and Mitigation in Intelligent Connected Vehicles: A Review,” Applied Sciences, vol. 13, no. 3, p. 1252, Jan. 2023, doi: 10.3390/app13031252.

S.-Y. Kuo, F.-H. Tseng, and Y.-H. Chou, “Metaverse intrusion detection of wormhole attacks based on a novel statistical mechanism,” Future Generation Computer Systems, vol. 143, pp. 179–190, Jun. 2023, doi: 10.1016/j.future.2023.01.017.

A. M. Mostafa et al., “Strengthening Cloud Security: An Innovative Multi-Factor Multi-Layer Authentication Framework for Cloud User Authentication,” Applied Sciences, vol. 13, no. 19, p. 10871, Sep. 2023, doi: 10.3390/app131910871.

T. Suleski, M. Ahmed, W. Yang, and E. Wang, “A review of multi-factor authentication in the Internet of Healthcare Things,” Digital Health, vol. 9, p. 205520762311771, Jan. 2023, doi: 10.1177/20552076231177144.

S. Rawther and S. Sathyalakshmi, “Protecting Cloud Computing Environments from Malicious Attacks Using multi-factor Authentication and Modified DNA Cryptography,” Recent Patents on Engineering, vol. 18, Sep. 2023, doi: 10.2174/1872212118666230905141926.

I. A. Najm, O. Gheni Abdulateef, A. H. Ali, S. Rashid Ahmed, Mohsin. A. Ahmed, and S. Algburi, “Deep learning detection approach for speech impairment children in Parkinson’s disease,” 2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pp. 1-6, May 2024, doi: 10.1109/hora61326.2024.10550841.

A. M. Aburbeian and M. Fernández-Veiga, “Secure Internet Financial Transactions: A Framework Integrating Multi-Factor Authentication and Machine Learning,” AI, vol. 5, no. 1, pp. 177–194, Jan. 2024, doi: 10.3390/ai5010010.

A. A. S. AlQahtani, T. Alshayeb, M. Nabil, and A. Patooghy, “Leveraging Machine Learning for Wi-Fi-Based Environmental Continuous Two-Factor Authentication,” IEEE Access, vol. 12, pp. 13277–13289, 2024, doi: 10.1109/access.2024.3356351.

https://www.kaggle.com/datasets/sampadab17/network-intrusion-detection.

F. Wang, N. Yang, P. M. Shakeel, and V. Saravanan, “Machine learning for mobile network payment security evaluation system,” Transactions on Emerging Telecommunications Technologies, vol. 35, no. 4, p. e4226, 2024.

Z. T. Pritee, M. H. Anik, S. B. Alam, J. R. Jim, M. M. Kabir, and M. F. Mridha, “Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review,” Computers & Security, p. 103747, 2024.

S. Muneer, U. Farooq, A. Athar, M. Ahsan Raza, T. M. Ghazal, and S. Sakib, “A Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A Comprehensive Analysis,” Journal of Engineering, vol. 2024, no. 1, p. 3909173, 2024.

K. S. Gill and A. Dhillon, “A hybrid machine learning framework for intrusion detection system in smart cities,” Evolving Systems, pp. 1-15, 2024.

I. Wanisha, J. B. James, J. S. Witeno, L. H. M. Bakery, M. Samuel, and M. Faisal, “Multi-Factor Authentication Using Blockchain: Enhancing Privacy, Security and Usability,” International Journal of Computer Technology and Science, vol. 1, no. 3, pp. 41-55, 2024.

X. Fang et al., “Pioneering advanced security solutions for reinforcement learning-based adaptive key rotation in Zigbee networks,” Scientific Reports, p. 14, 2024.

S. Dhote, P. Maidamwar, and S. Thakur, “Integrating Blockchain and Multi-Factor Authentication for Enhanced Cloud Security in Certificate Verification Systems,” in 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT), pp. 1-7, 2024.

S. A. Babu, A. Ranganath, M. M. Goswami, T. Gnanaprakasam, and M. K. Ishak, “Modified Marine Predators Algorithm With Deep Learning-Driven Security Solution for IoT-Assisted UAV Networks,” IEEE Access, vol. 12, pp. 54991-54998, 2024.

A. Hamarsheh, “An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning,” Applied Sciences, vol. 14, no. 11, p. 4530, 2024.

S. E. V. S. Pillai, S. S. Poddar, Y. Nagendar, P. K. Pareek, and P. Zanke, “Automated Cybersecurity Attack Detection Using Prairie Dog Optimization and Multilayer Perceptron in Healthcare System,” in 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), pp. 1-6, 2024.




DOI: https://doi.org/10.18196/jrc.v5i5.22508

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Rafah Kareem Mahmood, Ans Ibrahim Mahameed, Noor Q. Lateef, Hasanain M. Jasim, Ahmed Dheyaa Radhi, Saadaldeen Rashid Ahmed, Priyanka Tupe-Waghmare

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

Journal of Robotics and Control (JRC)

P-ISSN: 2715-5056 || E-ISSN: 2715-5072
Organized by Peneliti Teknologi Teknik Indonesia
Published by Universitas Muhammadiyah Yogyakarta in collaboration with Peneliti Teknologi Teknik Indonesia, Indonesia and the Department of Electrical Engineering
Website: http://journal.umy.ac.id/index.php/jrc
Email: jrcofumy@gmail.com

Kuliah Teknik Elektro Terbaik