TOWARDS IMPROVED THREAT MITIGATION IN DIGITAL ENVIRONMENTS: A COMPREHENSIVE FRAMEWORK FOR CYBERSECURITY ENHANCEMENT
DOI:
https://doi.org/10.29121/granthaalayah.v12.i5.2024.5655Keywords:
Cybersecurity, Threat Mitigation, Digital Transformation, Artificial Intelligence (AI), Machine Learning (ML), Blockchain, Risk Management, Cybersecurity Framework, Cyber Threat Detection, Cyber Resilience, Cybersecurity StrategyAbstract [English]
In today's digital landscape, cybersecurity has become a critical concern due to the increasing sophistication of cyber threats. Traditional cybersecurity measures are often inadequate against evolving attacks, necessitating the development of comprehensive and adaptive threat mitigation frameworks. This study aims to address this gap by proposing a robust cybersecurity framework that integrates advanced technologies such as artificial intelligence (AI), machine learning (ML), and blockchain to enhance threat detection, response, and recovery capabilities. The framework adopts a layered defense mechanism, real-time monitoring, and proactive threat hunting to provide a holistic approach to cybersecurity. By examining current methodologies and identifying their limitations, this research highlights the necessity for enhanced threat mitigation strategies. Through a mixed-methods approach involving online surveys and literature review, the study develops a flexible, scalable, and adaptive framework capable of countering sophisticated cyber threats. Key recommendations include adopting advanced technologies, continuous training, enhancing threat intelligence sharing, implementing a layered defense strategy, and conducting regular security audits. This comprehensive framework aims to improve organizational resilience, ensuring the safety and integrity of digital environments in the face of an ever-evolving cyber threat landscape.
Downloads
References
Ahmad, S., Mehfuz, S., Urooj, S., & Alsubaie, N. (2024). Machine Learning-Based Intelligent Security Framework for Secure Cloud Key Management. Cluster Computing, 1-27. https://doi.org/10.1007/s10586-024-04288-8 DOI: https://doi.org/10.1007/s10586-024-04288-8
Ainslie, S., Thompson, D., Maynard, S., & Ahmad, A. (2023). Cyber-Threat Intelligence for Security Decision-Making: A Review and Research Agenda for Practice. Computers & Security. https://doi.org/10.1016/j.cose.2023.103352 DOI: https://doi.org/10.1016/j.cose.2023.103352
Alsirhani, A., Alshahrani, M.M., Hassan, A.M., Taloba, A.I., Abd El-Aziz, R.M., & Samak, A.H. (2023). Implementation of African Vulture Optimisation Algorithm Based on Deep Learning for Cybersecurity Intrusion Detection. Alexandria Engineering Journal, 79, 105-115. https://doi.org/10.1016/j.aej.2023.07.077 DOI: https://doi.org/10.1016/j.aej.2023.07.077
Alsmadi, I. (2023). The NICE Cyber Security Framework: Cyber Security Intelligence and Analytics. Springer Nature. https://doi.org/10.1007/978-3-031-21651-0 DOI: https://doi.org/10.1007/978-3-031-21651-0
Applebaum, S., Gaber, T., & Ahmed, A. (2021). Signature-Based and Machine-Learning-Based Web Application Firewalls: A Short Survey. Procedia Computer Science, 189, 359-367. https://doi.org/10.1016/j.procs.2021.05.105 DOI: https://doi.org/10.1016/j.procs.2021.05.105
Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., & Wahab, A. (2020). A Review of Intrusion Detection Systems using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions. Electronics, 9(7), 1177. https://doi.org/10.3390/electronics9071177 DOI: https://doi.org/10.3390/electronics9071177
Asiri, M., Saxena, N., Gjomemo, R., & Burnap, P. (2023). Understanding Indicators of Compromise Against Cyber-Attacks in Industrial Control Systems: A Security Perspective. ACM Transactions on Cyber-Physical Systems, 7(2), 1-33. https://doi.org/10.1145/3587255 DOI: https://doi.org/10.1145/3587255
Aslan, Ö., Aktuğ, S.S., Ozkan-Okay, M., Yilmaz, A.A., & Akin, E. (2023). A Comprehensive Review of Cyber Security Vulnerabilities, Threats, Attacks, and Solutions. Electronics, 12(6), 1333. https://doi.org/10.3390/electronics12061333 DOI: https://doi.org/10.3390/electronics12061333
Beck, C.T. (2019). Secondary Qualitative Data Analysis in the Health and Social Sciences. Routledge. https://doi.org/10.4324/9781315098753 DOI: https://doi.org/10.4324/9781315098753
Benami, E., Jin, Z., Carter, M.R., Ghosh, A., Hijmans, R.J., Hobbs, A., Kenduiywo, B., & Lobell, D.B. (2021). Uniting Remote Sensing, Crop Modelling and Economics for Agricultural Risk Management. Nature Reviews Earth & Environment, 2(2), 140-159. https://doi.org/10.1038/s43017-020-00122-y DOI: https://doi.org/10.1038/s43017-020-00122-y
Borgi, M.A. (2021). Behavior Profiling-Based Approach for the Security of Smart Home Systems.
Brannen, J. (2017). Combining Qualitative and Quantitative Approaches: An Overview. Mixing Methods: Qualitative and Quantitative Research, 3-37. https://doi.org/10.4324/9781315248813-1 DOI: https://doi.org/10.4324/9781315248813-1
Braun, V., & Clarke, V. (2019). Reflecting on Reflexive Thematic Analysis. Qualitative Research in Sport, Exercise and Health, 11(4),589-597. https://doi.org/10.1080/2159676X.2019.1628806 DOI: https://doi.org/10.1080/2159676X.2019.1628806
Bryman, A., & Buchanan, D.A. (2018). Unconventional Methodology in Organisation & Management Research. Oxford University Press. https://doi.org/10.1093/oso/9780198796978.001.0001 DOI: https://doi.org/10.1093/oso/9780198796978.001.0001
Catal, C., Ozcan, A., Donmez, E., & Kasif, A. (2023). Analysis of Cyber Security Knowledge Gaps Based on Cyber Security Body of Knowledge. Education and Information Technologies, 28(2), 1809-1831. https://doi.org/10.1007/s10639-022-11261-8 DOI: https://doi.org/10.1007/s10639-022-11261-8
Cybersecurity, C.I. (2018). Framework for Improving Critical Infrastructure Cybersecurity. CSWP, 4162018, 7. https://nvlpubs.nist.gov/nistpubs/CSWP/NIST
Dastane, D.O. (2020). The Effect of Bad Password Habits on Personal Data Breach. International Journal of Emerging Trends in Engineering Research, 8(10). https://doi.org/10.30534/ijeter/2020/538102020 DOI: https://doi.org/10.30534/ijeter/2020/538102020
Davidson, E., Edwards, R., Jamieson, L., & Weller, S. (2019). Big Data, Qualitative Style: A Breadth-&-Depth Method for Working with Large Amounts of Secondary Qualitative Data. Quality & quantity, 53(1), 363-376. https://doi.org/10.1007/s11135-018-0757-y DOI: https://doi.org/10.1007/s11135-018-0757-y
Debas, E., Alhumam, N., & Riad, K. (2024). Similarity Learning; Siamese Networks; MCESTA; Triplet Loss; Similarity Metrics. International Journal of Advanced Computer Science & Applications, 15(3). https://doi.org/10.14569/IJACSA.2024.01503137 DOI: https://doi.org/10.14569/IJACSA.2024.01503137
Domínguez-Dorado, M., Carmona-Murillo, J., Cortés-Polo, D., & Rodríguez-Pérez, F.J. (2022). CyberTOMP: A Novel Systematic Framework to Manage Asset-Focused Cybersecurity from Tactical and Operational Levels. IEEE Access, 10, 122454-122485. https://doi.org/10.1109/ACCESS.2022.3223440 DOI: https://doi.org/10.1109/ACCESS.2022.3223440
Dufour, I.F., & Richard, M.C. (2019). Theorizing from Secondary Qualitative Data: A Comparison of two Data Analysis Methods. Cogent Education, 6(1). https://doi.org/10.1080/2331186X.2019.1690265 DOI: https://doi.org/10.1080/2331186X.2019.1690265
Duggineni, S. (2023). Impact of Controls on Data Integrity and Information Systems. Science and technology, 13(2), 29-35.
Ferrag, M. A., & Maglaras, L. (2023). DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids. IEEE Transactions on Engineering Management, 67(4), 1285-1297. https://doi.org/10.1109/TEM.2019.2922936 DOI: https://doi.org/10.1109/TEM.2019.2922936
Ganesh, A.D., & Kalpana, P. (2022). Future of Artificial Intelligence and its Influence on Supply Chain Risk Management-A Systematic Review. Computers & Industrial Engineering, 169. https://doi.org/10.1016/j.cie.2022.108206 DOI: https://doi.org/10.1016/j.cie.2022.108206
Gao, X., Wen, Z., & Hu, J. (2023). A Survey of Security Challenges in Cloud-Based SCADA Systems. Sensors, 21(4). https://doi.org/10.3390/s21041234 DOI: https://doi.org/10.3390/s21041234
George, A.S., George, A.H., & Baskar, T. (2023). Digitally Immune Systems: Building Robust Defences in the Age of Cyber Threats. Partners Universal International Innovation Journal, 1(4), 155-172. https://doi.org/10.5040/9781350033061.ch-8 DOI: https://doi.org/10.5040/9781350033061.ch-8
Habeeb, R.A.A., Nasaruddin, F., Gani, A., Hashem, I.A.T., Ahmed, E., & Imran, M. (2019). Real-Time Big Data Processing for Anomaly Detection: A Survey. International Journal of Information Management, 45, 289-307. https://doi.org/10.1016/j.ijinfomgt.2018.08.006 DOI: https://doi.org/10.1016/j.ijinfomgt.2018.08.006
Hajj, S., El Sibai, R., Bou Abdo, J., Demerjian, J., Makhoul, A., & Guyeux, C. (2021). Anomaly-Based Intrusion Detection Systems: The Requirements, Methods, Measurements, and Datasets. Transactions on Emerging Telecommunications Technologies, 32(4). https://doi.org/10.1002/ett.4240 DOI: https://doi.org/10.1002/ett.4240
Hemberg, E., Turner, M.J., Rutar, N., & O'reilly, U.M. (2024). Enhancements to Threat, Vulnerability, and Mitigation Knowledge for Cyber Analytics, Hunting, and Simulations. Digital Threats: Research and Practice, 5(1), 1-33. https://doi.org/10.1145/3615668 DOI: https://doi.org/10.1145/3615668
Hider, B., & Shabir, G. (2024). Cybersecurity Threats and Mitigation Strategies in the Digital Age: A Comprehensive Overview.
Hossain, M. S., Muhammad, G., & Guizani, N. (2023). Secure and Efficient Multiparty Data Aggregation for Smart Grid Communications in the Internet of Things. IEEE Transactions on Parallel and Distributed Systems, 30(12), 2819-2832. https://doi.org/10.1109/TPDS.2019.2926979 DOI: https://doi.org/10.1109/TPDS.2019.2926979
How, M.L., & Cheah, S.M. (2023). Business Renaissance: Opportunities and challenges at the Dawn of the Quantum Computing Era. Businesses, 3(4), 585-605. https://doi.org/10.3390/businesses3040036 DOI: https://doi.org/10.3390/businesses3040036
Hughes, K., Frank, V.A., Herold, M.D., & Houborg, E. (2023). Data Reuse Across International Contexts? Reflections on New Methods for International Qualitative Secondary Analysis. Qualitative Research, 23(4), 1155-1168. https://doi.org/10.1177/14687941211052278 DOI: https://doi.org/10.1177/14687941211052278
Islam, M.M., Hasan, M.K., Islam, S., Balfaqih, M., Alzahrani, A.I., Alalwan, N., Safie, N., Bhuiyan, Z.A., Thakkar, R., & Ghazal, T.M. (2024). Enabling Pandemic-Resilient Healthcare: Narrowband Internet of Things and Edge Intelligence for Real-Time Monitoring. CAAI Transactions on Intelligence Technology. https://doi.org/10.1049/cit2.12314 DOI: https://doi.org/10.1049/cit2.12314
Jamshed, M.A., Ali, K., Abbasi, Q.H., Imran, M.A., & Ur-Rehman, M. (2022). Challenges, Applications, and Future of Wireless Sensors in Internet of Things: A Review. IEEE Sensors Journal, 22(6), 5482-5494. https://doi.org/10.1109/JSEN.2022.3148128 DOI: https://doi.org/10.1109/JSEN.2022.3148128
Jawaid, S.A. (2022). Data Protection in Organization by the Implementation of Cyber Security. https://doi.org/10.20944/preprints202211.0371.v1 DOI: https://doi.org/10.20944/preprints202211.0371.v1
Jeffrey, N., Tan, Q., & Villar, J.R. (2023). A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems. Electronics, 12(15). https://doi.org/10.3390/electronics12153283 DOI: https://doi.org/10.3390/electronics12153283
Jeffrey, N., Tan, Q., & Villar, J.R. (2024). A Hybrid Methodology for Anomaly Detection in Cyber-Physical Systems. Neurocomputing, 568. https://doi.org/10.1016/j.neucom.2023.127068 DOI: https://doi.org/10.1016/j.neucom.2023.127068
Jimmy, F.N.U. (2024). Cyber Security Vulnerabilities and Remediation Through Cloud Security Tools. Journal of Artificial Intelligence General science (JAIGS), 2(1), 129-171. https://doi.org/10.60087/jaigs.vol03.issue01.p233 DOI: https://doi.org/10.60087/jaigs.vol03.issue01.p233
Kalla, D., & Kuraku, S. (2023). Advantages, Disadvantages and Risks Associated with ChatGPT and AI on Cybersecurity. Journal of Emerging Technologies and Innovative Research, 10(10). https://ssrn.com/abstract=4619204
Kaloudi, N., & Li, J. (2020). The Ai-Based Cyber Threat Landscape: A Survey. ACM Computing Surveys (CSUR), 53(1), 1-34. https://doi.org/10.1145/3372823 DOI: https://doi.org/10.1145/3372823
Kandasamy, K., Srinivas, S., Achuthan, K., & Rangan, V.P. (2020). IoT Cyber Risk: A Holistic Analysis of Cyber Risk Assessment Frameworks, Risk Vectors, and Risk Ranking Process. EURASIP Journal on Information Security, 1-18. https://doi.org/10.1186/s13635-020-00111-0 DOI: https://doi.org/10.1186/s13635-020-00111-0
Kayode-Ajala, O. (2023). Applications of Cyber Threat Intelligence (CTI) in Financial Institutions and Challenges in its Adoption. Applied Research in Artificial Intelligence and Cloud Computing, 6(8), 1-21.
Kinyua, J., & Awuah, L. (2021). AI/ML in Security Orchestration, Automation and Response: Future Research Directions. Intelligent Automation & Soft Computing, 28(2). https://doi.org/10.32604/iasc.2021.016240 DOI: https://doi.org/10.32604/iasc.2021.016240
Knapp, E.D. (2024). Industrial Network Security: Securing Critical Infrastructure Networks for Smart Grid, SCADA, and other Industrial Control Systems. Elsevier.
Komasawa, N. (2024). Revitalizing Postoperative Pain Management in Enhanced Recovery After Surgery via Inter-Departmental Collaboration Toward Precision Medicine: A Narrative Review. Cureus, 16(4). https://doi.org/10.7759/cureus.59031 DOI: https://doi.org/10.7759/cureus.59031
Kordestani, M., & Saif, M. (2021). Observer-Based Attack Detection and Mitigation for Cyberphysical Systems: A Review. IEEE Systems, Man, and Cybernetics Magazine, 7(2), 35-60. https://doi.org/10.1109/MSMC.2020.3049092 DOI: https://doi.org/10.1109/MSMC.2020.3049092
Kumar, A., & Somani, G. (2022). Security Infrastructure for Cyber Attack Targeted Networks and Services. In Recent Advancements in ICT Infrastructure and Applications. Singapore: Springer Nature Singapore, 209-229. https://doi.org/10.1007/978-981-19-2374-6_9 DOI: https://doi.org/10.1007/978-981-19-2374-6_9
Kunduru, A.R. (2023). Industry Best Practices on Implementing Oracle Cloud ERP Security. International Journal of Computer Trends and Technology, 71(6), 1-8. https://doi.org/10.14445/22312803/IJCTT-V71I6P101 DOI: https://doi.org/10.14445/22312803/IJCTT-V71I6P101
Landoll, D. (2021). The Security Risk Assessment Handbook: A Complete Guide for Performing Security Risk Assessments. CRC Press. https://doi.org/10.1201/9781003090441 DOI: https://doi.org/10.1201/9781003090441
Manoharan, A., & Sarker, M. (2023). Revolutionizing Cybersecurity: Unleashing the Power of Artificial Intelligence and Machine Learning for Next-Generation Threat Detection.
Martins, I., Resende, J.S., Sousa, P.R., Silva, S., Antunes, L., & Gama, J. (2022). Host-Based IDS: A Review and Open Issues of an Anomaly Detection System in IoT. Future Generation Computer Systems, 133, 95-113. https://doi.org/10.1016/j.future.2022.03.001 DOI: https://doi.org/10.1016/j.future.2022.03.001
Mazhar, T., Irfan, H.M., Khan, S., Haq, I., Ullah, I., Iqbal, M., & Hamam, H. (2023). Analysis of Cyber Security Attacks and its Solutions for the Smart Grid Using Machine Learning and Blockchain Methods. Future Internet, 15(2), 83. https://doi.org/10.3390/fi15020083 DOI: https://doi.org/10.3390/fi15020083
McCall Jr, G.C. (2022). Exploring a Cyber Threat Intelligence (CTI) Approach in the Thwarting of Adversary Attacks: An Exploratory Case Study (Doctoral Dissertation, Northcentral University).
Mik-Meyer, N. (2020). Multimethod Qualitative Research. Qualitative Research, 5, 357-374.
Mishra, A., Alzoubi, Y.I., Anwar, M.J., & Gill, A.Q. (2022). Attributes Impacting Cybersecurity Policy Development: An Evidence from Seven Nations. Computers & Security, 120. https://doi.org/10.1016/j.cose.2022.102820 DOI: https://doi.org/10.1016/j.cose.2022.102820
Nova, K. (2022). Security and Resilience in Sustainable Smart Cities through Cyber Threat Intelligence. International Journal of Information and Cybersecurity, 6(1), 21-42.
Okunlaya, R.O., Syed Abdullah, N., & Alias, R.A. (2022). Artificial Intelligence (AI) Library Services Innovative Conceptual Framework for the Digital Transformation of University Education. Library Hi Tech, 40(6), 1869-1892. https://doi.org/10.1108/LHT-07-2021-0242 DOI: https://doi.org/10.1108/LHT-07-2021-0242
Ortega Vázquez, C., Vanden Broucke, S., & De Weerdt, J. (2023). A Two-Step Anomaly Detection Based Method for PU Classification in Imbalanced Data Sets. Data Mining and Knowledge Discovery, 37(3), 1301-1325. https://doi.org/10.1007/s10618-023-00925-9 DOI: https://doi.org/10.1007/s10618-023-00925-9
Poth, C.N. (2019). Rigorous and Ethical Qualitative Data Reuse: Potential Perils and Promising Practices. International Journal of Qualitative Methods, 18. https://doi.org/10.1177/1609406919868870 DOI: https://doi.org/10.1177/1609406919868870
Ruggiano, N., & Perry, T.E. (2019). Conducting Secondary Analysis of Qualitative Data: Should We, Can We, and How?. Qualitative Social Work, 18(1), 81-97. https://doi.org/10.1177/1473325017700701 DOI: https://doi.org/10.1177/1473325017700701
Safitra, M.F., Lubis, M., & Fakhrurroja, H. (2023). Counterattacking Cyber Threats: A Framework for the Future of Cybersecurity. Sustainability, 15(18). https://doi.org/10.3390/su151813369 DOI: https://doi.org/10.3390/su151813369
Saritac, U., Liu, X., & Wang, R. (2022). Assessment of Cybersecurity Framework in Critical Infrastructures. In 2022 IEEE Delhi Section Conference (DELCON). IEEE. 1-4. https://doi.org/10.1109/DELCON54057.2022.9753250 DOI: https://doi.org/10.1109/DELCON54057.2022.9753250
Saunders, M.N., Lewis, P., Thornhill, A., & Bristow, A. (2015). Understanding Research Philosophy and Approaches to Theory Development.
Schiller, E., Aidoo, A., Fuhrer, J., Stahl, J., Ziörjen, M., & Stiller, B. (2022). Landscape of IoT Security. Computer Science Review, 44. https://doi.org/10.1016/j.cosrev.2022.100467 DOI: https://doi.org/10.1016/j.cosrev.2022.100467
Shaikh, A., Khan, A.A., Zebanaaz, S., Shaikh, S., & Akhter, N. (2021). Exploring Recent Challenges in Cyber Security and their Solutions. International Journal of Creative Research Thoughts, 9(12), 6.
Siwakoti, Y.R., Bhurtel, M., Rawat, D.B., Oest, A., & Johnson, R.C. (2023). Advances in IOT Security: Vulnerabilities, Enabled Criminal Services, Attacks and Countermeasures. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2023.3252594 DOI: https://doi.org/10.1109/JIOT.2023.3252594
Steingartner, W., Galinec, D., & Kozina, A. (2021). Threat Defense: Cyber Deception Approach and Education for Resilience in Hybrid Threats Model. Symmetry, 13(4), 597. https://doi.org/10.3390/sym13040597 DOI: https://doi.org/10.3390/sym13040597
Tahmasebi, M. (2024). Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises. Journal of Information Security, 15(2), 106-133. https://doi.org/10.4236/jis.2024.152008 DOI: https://doi.org/10.4236/jis.2024.152008
Talaat, F.M., & ZainEldin, H. (2023). An Improved Fire Detection Approach Based on YOLO-v8 for Smart Cities. Neural Computing and Applications, 35(28), 20939-20954. https://doi.org/10.1007/s00521-023-08809-1 DOI: https://doi.org/10.1007/s00521-023-08809-1
Tsakalidis, G., Vergidis, K., Petridou, S., & Vlachopoulou, M. (2019). A Cybercrime Incident Architecture with Adaptive Response Policy. Computers & Security, 83, 22-37. https://doi.org/10.1016/j.cose.2019.01.011 DOI: https://doi.org/10.1016/j.cose.2019.01.011
Ukwandu, E., Farah, M.A.B., Hindy, H., Brosset, D., Kavallieros, D., Atkinson, R., Tachtatzis, C., Bures, M., Andonovic, I., & Bellekens, X. (2020). A Review of Cyber-Ranges and Test-Beds: Current and Future Trends. Sensors, 20(24). https://doi.org/10.3390/s20247148 DOI: https://doi.org/10.3390/s20247148
Ullah, F., Qayyum, S., Thaheem, M.J., Al-Turjman, F., & Sepasgozar, S.M. (2021). Risk management in Sustainable Smart Cities Governance: A TOE Framework. Technological Forecasting and Social Change, 167. https://doi.org/10.1016/j.techfore.2021.120743 DOI: https://doi.org/10.1016/j.techfore.2021.120743
Vanin, P., Newe, T., Dhirani, L.L., O'Connell, E., O'Shea, D., Lee, B., & Rao, M. (2022). A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning. Applied Sciences, 12(22). https://doi.org/10.3390/app122211752 DOI: https://doi.org/10.3390/app122211752
Verma, P., & S. Sangle, P. (2023). Role of Digital Transformation in Inspection and Certification. In Handbook of Quality System, Accreditation and Conformity Assessment. Singapore: Springer Nature Singapore, 1-29. https://doi.org/10.1007/978-981-99-4637-2_28-1 DOI: https://doi.org/10.1007/978-981-99-4637-2_28-1
Xie, S., Dong, S., Chen, Y., Peng, Y., & Li, X. (2021). A Novel Risk Evaluation Method for Fire and Explosion Accidents in Oil Depots Using Bow-Tie Analysis and Risk Matrix Analysis Method Based on Cloud Model Theory. Reliability Engineering & System Safety, 215. https://doi.org/10.1016/j.ress.2021.107791 DOI: https://doi.org/10.1016/j.ress.2021.107791
Zeng, P., Fang, W., Zhang, H., & Liang, Z. (2023). Cost-Benefit Analysis of the Wuxikou Integrated Flood Management Project Considering the Effects of Flood Risk Reduction and Resettlement. International Journal of Disaster Risk Science, 14(5), 795-812. https://doi.org/10.1007/s13753-023-00520-y DOI: https://doi.org/10.1007/s13753-023-00520-y
Zhao, J., Yan, Q., Li, J., Shao, M., He, Z., & Li, B. (2020). TIMiner: Automatically Extracting and Analysing Categorised Cyber Threat Intelligence from Social Data. Computers & Security, 95. https://doi.org/10.1016/j.cose.2020.101867 DOI: https://doi.org/10.1016/j.cose.2020.101867
Zheng, Y., Li, Z., Xu, X., & Zhao, Q. (2022). Dynamic Defenses in Cyber Security: Techniques, Methods and Challenges. Digital Communications and Networks, 8(4), 422-435. https://doi.org/10.1016/j.dcan.2021.07.006 DOI: https://doi.org/10.1016/j.dcan.2021.07.006
Zoppi, T., Ceccarelli, A., Capecchi, T., & Bondavalli, A. (2021). Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape. ACM/IMS Transactions on Data Science, 2(2), 1-26. https://doi.org/10.1145/3441140 DOI: https://doi.org/10.1145/3441140
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Hewa Balisane, Ehigiator Iyobor Egho-Promise, Emmanuel Lyada, Folayo Aina
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.