Detection and Response Strategies for Advanced Persistent Threats (APTs)
DOI:
https://doi.org/10.38124/ijsrmt.v4i4.367Keywords:
Advanced Persistent Threats (APTs), Cybersecurity, Detection Strategies, Response Framework, Anomaly Detection, Behavioral Analytics, Threat Intelligence, Cyber Kill Chain, MITRE ATT&CK, Zero Trust, Incident Response, State-Sponsored Cyber-attacksAbstract
This study investigates Advanced Persistent Threats (APTs), a class of cyber-attacks distinguished by their sophisticated, state sponsored nature and long-term, stealthy operations. Unlike typical cybercriminals focused on immediate gains, APT groups meticulously plan and execute multi-stage attacks to infiltrate networks and exfiltrate sensitive data over extended periods. To address the shortcomings of conventional security measures, we developed a comprehensive framework for detecting and responding to APTs. Our approach combines a systematic literature review, integration of established frameworks (such as the Cyber Kill Chain and MITRE ATT&CK), empirical simulations, and extensive expert consultations—including valuable peer feedback—to validate our methodology. The findings reveal that APTs follow a defined, multi-step process and exploit gaps in traditional defenses, thereby underscoring the effectiveness of advanced anomaly detection, behavioral analytics, and threat intelligence integration. Based on these insights, we propose a robust incident response framework that emphasizes rapid containment and recovery. The study concludes with actionable recommendations for adopting emerging technologies like artificial intelligence, Zero Trust architectures, and enhanced cloud security solutions to fortify organizational defenses against evolving cyber threats, while also outlining directions for future research to further refine these strategies.
Downloads
References
1. Abilimi,C.A, Asante,M, Opoku-Mensah, E & Boateng, F.O. (2015). Testing for Randomness in Pseudo Random Number Generators Algorithms in a Cryptographic Application.Computer Engineering and Intelligent Systems, www.iiste.org, ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online) Vol.6, No.9, 2015
2. Abilimi, C. A., & Adu-Manu, K. S. (2013). Examining the impact of Information and Communication Technology capacity building in High School education in Ghana. International Journal of Engineering Research & Technology (IJERT),ISSN: 2278-0181,Vol. 2 Issue 9, September – 2013
3. Abilimi, C.A., Amoako, L., Ayembillah, J. N., Yeboah, T.(2013). Assessing the Availability of Information and Communication Technologies in Teaching and Learning in High School Education in Ghana. International Journal of Engineering Research and Technology, 2(11), 50 - 59.
4. Abilimi, C. A. & Yeboah, T. (2013). Assessing the challenges of Information and Communication Technology in educational development in High Schools in Ghana. International Journal of Engineering Research & Technology (IJERT).ISSN: 2278-0181, Vol. 2 Issue 11, November - 2013
5. Ahmed, Y., Asyhari, A. T., & Rahman, M. A. (2021). A cyber kill chain approach for detecting advanced persistent threats. Computers, Materials and Continua, 67(2), 2497–2513.
6. Adewopo, V. (2021). Exploring open source intelligence for cyber threat prediction (Master’s thesis, University of Cincinnati).
7. Agbede, O. M. (2023). Incident Handling and Response Process in Security Operations.
8. Arefin, S., Chowdhury, M., Parvez, R., Ahmed, T., Abrar, A. S., & Sumaiya, F. (2024, May). Understanding APT detection using Machine learning algorithms: Is superior accuracy a thing? In 2024 IEEE International Conference on Electro Information Technology (eIT) (pp. 532–537). IEEE.
9. Al‐Mhiqani, M. N., Ahmad, R., Zainal Abidin, Z., Yassin, W., Hassan, A., Abdulkareem, K. H., … & Yunos, Z. (2020). A review of insider threat detection: Classification, machine learning techniques, datasets, open challenges, and recommendations. Applied Sciences, 10(15), 5208.
10. Al Mansur, A., & Zaman, T. (2023, November). User behavior analytics in advanced persistent threats: A comprehensive review of detection and mitigation strategies. In 2023 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS) (pp. 1–6). IEEE.
11. Alosaimi, M., Rana, O., & Perera, C. (2023). Testbeds and evaluation frameworks for anomaly detection within built environments: A systematic review. ACM Computing Surveys.
12. Alrehaili, M., Alshamrani, A., & Eshmawi, A. (2021, December). A hybrid deep learning approach for advanced persistent threat attack detection. In Proceedings of the 5th International Conference on Future Networks and Distributed Systems (pp. 78–86).
13. Alshamrani, A., Myneni, S., Chowdhary, A., & Huang, D. (2019). A survey on advanced persistent threats: Techniques, solutions, challenges, and research opportunities. IEEE Communications Surveys & Tutorials, 21(2), 1851–1877.
14. Alzaabi, F. R., & Mehmood, A. (2024). A review of recent advances, challenges, and opportunities in malicious insider threat detection using machine learning methods. IEEE Access, 12, 30907–30927.
15. Bardin, J. S. (2025). Cyber Warfare. In Computer and Information Security Handbook (pp. 1345–1380). Morgan Kaufmann.
16. Bhardwaj, A. (2024). Cyber Investigations of Smart Devices. CRC Press.
17. Bierwirth, T., Pfützner, S., Schopp, M., & Steininger, C. (2024). Design and evaluation of advanced persistent threat scenarios for cyber ranges. IEEE Access.
18. Bitzer, M., Häckel, B., Leuthe, D., Ott, J., Stahl, B., & Strobel, J. (2023). Managing the inevitable–a maturity model to establish incident response management capabilities. Computers & Security, 125, 103050.
19. Botwright, R. (2023). Malware Analysis: Digital Forensics, Cybersecurity, And Incident Response. Rob Botwright.
20. Buchta, R., Gkoktsis, G., Heine, F., & Kleiner, C. (2024). Advanced Persistent Threat Attack Detection Systems: A Review of Approaches, Challenges, and Trends. Digital Threats: Research and Practice, 5(4), 1–37.
21. Chamkar, S. A., Maleh, Y., & Gherabi, N. (2024). Security Operations Centers: Use Case Best Practices, Coverage, and Gap Analysis Based on MITRE Adversarial Tactics, Techniques, and Common Knowledge. Journal of Cybersecurity and Privacy, 4(4), 777–793.
22. Chamkar, S. A., Maleh, Y., & Gherabi, N. (2024). Security Operations Centers: Use Case Best Practices, Coverage, and Gap Analysis Based on MITRE Adversarial Tactics, Techniques, and Common Knowledge. Journal of Cybersecurity and Privacy, 4(4), 777–793.
23. Chatterjee, A., & Ahmed, B. S. (2022). IoT anomaly detection methods and applications: A survey. Internet of Things, 19, 100568.
24. Cheng, Z., Lv, Q., Liang, J., Wang, Y., Sun, D., Pasquier, T., & Han, X. (2024, May). Kairos: Practical intrusion detection and investigation using whole-system provenance. In 2024 IEEE Symposium on Security and Privacy (SP) (pp. 3533–3551). IEEE.
25. Che Mat, N. I., Jamil, N., Yusoff, Y., & Mat Kiah, M. L. (2024). A systematic literature review on advanced persistent threat behaviors and its detection strategy. Journal of Cybersecurity, 10(1), tyad023.
26. Che Mat, N. I., Jamil, N., Yusoff, Y., & Mat Kiah, M. L. (2024). A systematic literature review on advanced persistent threat behaviors and its detection strategy. Journal of Cybersecurity, 10(1), tyad023.
27. de Melo e Silva, A., Costa Gondim, J. J., de Oliveira Albuquerque, R., & García Villalba, L. J. (2020). A methodology to evaluate standards and platforms within cyber threat intelligence. Future Internet, 12(6), 108.
28. Dwyer, A. C. (2019). Malware ecologies: a politics of cybersecurity (Doctoral dissertation, University of Oxford).
29. Edwards, D. J. (2024). Data Protection. In Critical Security Controls for Effective Cyber Defense: A Comprehensive Guide to CIS 18 Controls (pp. 57–96). Berkeley, CA: Apress.
30. Furfaro, A., Piccolo, A., Parise, A., Argento, L., & Saccà, D. (2018). A cloud-based platform for the emulation of complex cybersecurity scenarios. Future Generation Computer Systems, 89, 791–803
31. Ghafir, I., Hammoudeh, M., Prenosil, V., Han, L., Hegarty, R., Rabie, K., & Aparicio-Navarro, F. J. (2018). Detection of advanced persistent threat using machine-learning correlation analysis. Future Generation Computer Systems, 89, 349–359.
32. Gilbert, C.(2012). The Quest of Father and Son: Illuminating Character Identity, Motivation, and Conflict in Cormac McCarthy’s The Road. English Journal, Volume 102, Issue Characters and Character, p. 40 - 47. https://doi.org/10.58680/ej201220821.
33. Gilbert, C. (2018). Creating Educational Destruction: A Critical Exploration of Central Neoliberal Concepts and Their Transformative Effects on Public Education. The Educational Forum, 83(1), 60–74. https://doi.org/10.1080/00131725.2018.1505017.
34. Gilbert, C. (2021). Walking the popular education spiral - an account and analysis of participatory action research with teacher activists. Educational Action Research, 30(5), 881–901. https://doi.org/10.1080/09650792.2021.1875856
35. Gilbert, C. (2022). Making the Invisible Visible: Professional Development to Support Teacher Activism. Kappa Delta Pi Record, 58(1), 14–19. https://doi.org/10.1080/00228958.2022.2005426
36. Gilbert, C. & Gilbert, M.A.(2024a). Unraveling Blockchain Technology: A Comprehensive Conceptual Review. International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and ISSN Approved), ISSN:2349-5162, Vol.11, Issue 9, page no. ppa575-a584, September-2024, Available at : http://www.jetir.org/papers/JETIR2409066.pdf
37. Gilbert, C. & Gilbert, M.A.(2024b). Strategic Framework for Human-Centric AI Governance: Navigating Ethical, Educational, and Societal Challenges. International Journal of Latest Technology in Engineering Management & Applied Science, 13(8), 132-141. https://doi.org/10.51583/IJLTEMAS.2024.130816
38. Gilbert, C. & Gilbert, M.A.(2024c). The Impact of AI on Cybersecurity Defense Mechanisms: Future Trends and Challenges.Global Scientific Journals.ISSN 2320-9186,12(9),427-441. https://www.globalscientificjournal.com/researchpaper/The_Impact_of_AI_on_Cybersecurity_Defense_Mechanisms_Future_Trends_and_Challenges_.pdf.
39. Gilbert, C. & Gilbert, M.A. (2024d). The Convergence of Artificial Intelligence and Privacy: Navigating Innovation with Ethical Considerations. International Journal of Scientific Research and Modern Technology, 3(9), 9-9.
40. Gilbert, C. & Gilbert, M.A.(2024e). Transforming Blockchain: Innovative Consensus Algorithms for Improved Scalability and Security. International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.b299-b313, October-2024, Available :http://www.jetir.org/papers/JETIR2410134.pdf
41. Gilbert, C. & Gilbert, M.A. (2024f). Future Privacy Challenges: Predicting the Agenda of Webmasters Regarding Cookie Management and Its Implications for User Privacy. International Journal of Advanced Engineering Research and Science, ISSN (Online): 2455-9024,Volume 9, Issue 4, pp. 95-106.
42. Gilbert, C., & Gilbert, M. A. (2024g). Navigating the Dual Nature of Deepfakes: Ethical, Legal, and Technological Perspectives on Generative Artificial Intelligence (AI) Technology. International Journal of Scientific Research and Modern Technology, 3(10). https://doi.org/10.38124/ijsrmt.v3i10.54
43. Gilbert, C., & Gilbert, M. A. (2024h). Revolutionizing Computer Science Education: Integrating Blockchain for Enhanced Learning and Future Readiness. International Journal of Latest Technology in Engineering, Management & Applied Science, ISSN 2278-2540, Volume 13, Issue 9, pp.161-173.
44. Gilbert, C. & Gilbert, M.A. (2024i). Unlocking Privacy in Blockchain: Exploring Zero-Knowledge Proofs and Secure Multi-Party Computation Techniques. Global Scientific Journal (ISSN 2320-9186) 12 (10), 1368-1392.
45. Gilbert, C. & Gilbert, M.A. (2024j). The Role of Artificial Intelligence (AI) in Combatting Deepfakes and Digital Misinformation.International Research Journal of Advanced Engineering and Science (ISSN: 2455-9024), Volume 9, Issue 4, pp. 170-181.
46. Gilbert, C. & Gilbert, M.A.(2024k). AI-Driven Threat Detection in the Internet of Things (IoT), Exploring Opportunities and Vulnerabilities. International Journal of Research Publication and Reviews, Vol 5, no 11, pp 219-236.
47. Gilbert, C., & Gilbert, M. A. (2024l). The security implications of artificial intelligence (AI)-powered autonomous weapons: Policy recommendations for international regulation. International Research Journal of Advanced Engineering and Science, 9(4), 205–219.
48. Gilbert, C., & Gilbert, M. A. (2024m). The role of quantum cryptography in enhancing cybersecurity. International Journal of Research Publication and Reviews, 5(11), 889–907. https://www.ijrpr.com
49. Gilbert, C., & Gilbert, M. A. (2024n). Bridging the gap: Evaluating Liberia's cybercrime legislation against international standards. International Journal of Research and Innovation in Applied Science (IJRIAS), 9(10), 131–137. https://doi.org/10.51584/IJRIAS.2024.910013
50. Gilbert, C., & Gilbert, M. A. (2024o). The Effectiveness of Homomorphic Encryption in Protecting Data Privacy. International Journal of Research Publication and Reviews, 5(11), 3235-3256. https://www.ijrpr.com.
51. Gilbert, C., & Gilbert, M. A. (2024p). CRYPTOGRAPHIC FOUNDATIONS AND CYBERSECURITY IMPLICATIONS OF BLOCKCHAIN TECHNOLOGY.Global Scientific Journals,ISSN 2320-9186,12(11),464-487. https://www.globalscientificjournal.com
52. Gilbert, C., & Gilbert, M. A. (2024q). Advancing privacy standards through education: The role of academic initiatives in enhancing privacy within Cardano's blockchain ecosystem. International Research Journal of Advanced Engineering and Science, 9(4), 238–251.
53. Gilbert, C., & Gilbert, M. A. (2024r). Leveraging artificial intelligence (AI) by a strategic defense against deepfakes and digital misinformation. International Journal of Scientific Research and Modern Technology, 3(11). https://doi.org/10.38124/ijsrmt.v3i11.76
54. Gilbert, C., & Gilbert, M. A. (2024s). Evaluation of the efficiency of advanced number generators in cryptographic systems using a comparative approach. International Journal of Scientific Research and Modern Technology, 3(11). https://doi.org/10.38124/ijsrmt.v3i11.77
55. Gilbert, C., & Gilbert, M. A. (2024t). Cybersecurity risk management frameworks for critical infrastructure protection. International Journal of Research Publication and Reviews, 5(12), 507–533. https://www.ijrpr.com/
56. Gilbert, C., & Gilbert, M. A. (2024u). Organizational and leadership aspects of cybersecurity governance. International Journal of Research Publication and Reviews, 5(12), 1174–1191. Retrieved from www.ijrpr.com
57. Gilbert, C., & Gilbert, M. A. (2024v). The development and evolution of cryptographic algorithms in response to cyber threats. International Journal of Research Publication and Reviews, 5(12), 1149–1173. Retrieved from www.ijrpr.com
58. Gilbert, C., & Gilbert, M. A. (2024w). Privacy-preserving data mining and analytics in big data environments. Global Scientific Journal, 12(12). Retrieved from www.globalscientificjournal.com
59. Gilbert, C., & Gilbert, M. A. (2024x). Investigating the challenges and solutions in cybersecurity using quantum computing and cryptography. International Research Journal of Advanced Engineering and Science, 9(4), 291–315.
60. Gilbert, C., & Gilbert, M. A. (2024y). The integration of blockchain technology into database management systems for enhanced security and transparency. International Research Journal of Advanced Engineering and Science, 9(4), 316–334.
61. Gilbert, C., & Gilbert, M. A. (2025a). Artificial intelligence (AI) and machine learning (ML) for predictive cyber threat intelligence (CTI). International Journal of Research Publication and Reviews, 6(3), 584–617. http://www.ijrpr.com
62. Gilbert, C., & Gilbert, M. A. (2025b). Continuous user authentication on mobile devices. International Research Journal of Advanced Engineering and Science, 10(1), 158–173.
63. Gilbert, M.A., Oluwatosin, S. A., & Gilbert, C.(2024). An investigation into the types of role-based relationships that exist between lecturers and students in universities across southwestern nigeria: a sociocultural and institutional analysis. Global Scientific Journal, ISSN 2320-9186, Volume 12, Issue 10, pp. 263-280.
64. Gilbert, M.A., Auodo, A. & Gilbert, C.(2024). Analyzing Occupational Stress in Academic Personnel through the Framework of Maslow’s Hierarchy of Needs. International Journal of Research Publication and Reviews, Vol 5, no 11, pp 620-630.
65. Gioti, A. (2024). Advancements in Open Source Intelligence (OSINT) Techniques and the role of artificial intelligence in Cyber Threat Intelligence (CTI) (Master’s thesis, Πανεπιστήμιο Πειραιώς).
66. Goyal, A., Wang, G., & Bates, A. (2024, May). R-caid: Embedding root cause analysis within provenance-based intrusion detection. In 2024 IEEE Symposium on Security and Privacy (SP) (pp. 3515–3532). IEEE.
67. Hemsley, K., & Fisher, R. (2018, March). A history of cyber incidents and threats involving industrial control systems. In International Conference on Critical Infrastructure Protection (pp. 215–242). Cham: Springer International Publishing.
68. Hasan, M. M., Islam, M. U., & Uddin, J. (2023). Advanced persistent threat identification with boosting and explainable AI. SN Computer Science, 4(3), 271.
69. Hassan, W. U., Bates, A., & Marino, D. (2020, May). Tactical provenance analysis for endpoint detection and response systems. In 2020 IEEE Symposium on Security and Privacy (SP) (pp. 1172–1189). IEEE.
70. Hernández-Rivas, A., Morales-Rocha, V., & Sánchez-Solís, J. P. (2024). Towards autonomous cybersecurity: A comparative analysis of agnostic and hybrid AI approaches for advanced persistent threat detection. In Innovative Applications of Artificial Neural Networks to Data Analytics and Signal Processing (pp. 181–219). Springer, Cham.
71. Hossain, M. N., Sheikhi, S., & Sekar, R. (2020, May). Combating dependence explosion in forensic analysis using alternative tag propagation semantics. In 2020 IEEE Symposium on Security and Privacy (SP) (pp. 1139–1155). IEEE.
72. Jabar, T., & Mahinderjit Singh, M. (2022). Exploration of mobile device behavior for mitigating advanced persistent threats (APT): a systematic literature review and conceptual framework. Sensors, 22(13), 4662.
73. Jiang, Y., Wu, S., Ma, R., Liu, M., Luo, H., & Kaynak, O. (2023). Monitoring and defense of industrial cyber-physical systems under typical attacks: From a systems and control perspective. IEEE Transactions on Industrial Cyber-Physical Systems, 1, 192–207.
74. Jøsang, A. (2024a). Cyber Operations. In Cybersecurity: Technology and Governance (pp. 337–354). Cham: Springer Nature Switzerland.
75. Jøsang, A. (2024b). Basic Concepts of Cybersecurity. In Cybersecurity: Technology and Governance (pp. 1–24). Cham: Springer Nature Switzerland.
76. Kaul, D., & Khurana, R. (2021). AI to detect and mitigate security vulnerabilities in APIs: encryption, authentication, and anomaly detection in enterprise-level distributed systems. Eigenpub Review of Science and Technology, 5(1), 34–62.
77. Kareem, K., Naik, N., Jenkins, P., Grace, P., & Song, J. (2024, July). Understanding the Defence of Operational Technology (OT) Systems: A Comparison of Lockheed Martin’s Cyber Kill Chain, MITRE ATT&CK Framework, and Diamond Model. In The International Conference on Computing, Communication, Cybersecurity & AI (pp. 605–624). Cham: Springer Nature Switzerland.
78. Kareem, K., Naik, N., Jenkins, P., Grace, P., & Song, J. (2024, July). Understanding the Defence of Operational Technology (OT) Systems: A Comparison of Lockheed Martin’s Cyber Kill Chain, MITRE ATT&CK Framework, and Diamond Model. In The International Conference on Computing, Communication, Cybersecurity & AI (pp. 605–624). Cham: Springer Nature Switzerland.
79. Khalid, A., Zainal, A., Maarof, M. A., & Ghaleb, F. A. (2021, January). Advanced persistent threat detection: A survey. In 2021 3rd International Cyber Resilience Conference (CRC) (pp. 1–6). IEEE.
80. Khalid, A., Zainal, A., Maarof, M. A., & Ghaleb, F. A. (2021, January). Advanced persistent threat detection: A survey. In 2021 3rd International Cyber Resilience Conference (CRC) (pp. 1–6). IEEE.
81. Khalid, M. N. A., Al-Kadhimi, A. A., & Singh, M. M. (2023). Recent developments in game-theory approaches for the detection and defense against advanced persistent threats (APTs): a systematic review. Mathematics, 11(6), 1353.
82. Khalil, I. M. (2023). A Multimodal Immune System Inspired Defense Architecture for Detecting and Deterring Digital Pathogens in Container Hosted Web Services (Doctoral dissertation, The American University in Cairo (Egypt)).
83. Kwame, A. E., Martey, E. M., & Chris, A. G. (2017). Qualitative assessment of compiled, interpreted and hybrid programming languages. Communications on Applied Electronics, 7(7), 8-13.
84. Le, D. C., Zincir-Heywood, N., & Heywood, M. I. (2020). Analyzing data granularity levels for insider threat detection using machine learning. IEEE Transactions on Network and Service Management, 17(1), 30–44.
85. Leventopoulos, S., Gritzalis, D., & Stergiopoulos, G. (2024). Malware as a Geopolitical Tool. In Malware: Handbook of Prevention and Detection (pp. 251–271). Cham: Springer Nature Switzerland.
86. Li, Z., Cheng, X., Sun, L., Zhang, J., & Chen, B. (2021). A hierarchical approach for advanced persistent threat detection with attention‐based graph neural networks. Security and Communication Networks, 2021(1), 9961342.
87. Makrakis, G. M., Kolias, C., Kambourakis, G., Rieger, C., & Benjamin, J. (2021). Industrial and critical infrastructure security: Technical analysis of real-life security incidents. IEEE Access, 9, 165295–165325.
88. Mahboubi, A., Luong, K., Aboutorab, H., Bui, H. T., Jarrad, G., Bahutair, M., … & Gately, H. (2024). Evolving techniques in cyber threat hunting: A systematic review. Journal of Network and Computer Applications, 104004.
89. Mekala, S. H., Baig, Z., Anwar, A., & Zeadally, S. (2023). Cybersecurity for Industrial IoT (IIoT): Threats, countermeasures, challenges and future directions. Computer Communications, 208, 294–320.
90. Miller, T., Staves, A., Maesschalck, S., Sturdee, M., & Green, B. (2021). Looking back to look forward: Lessons learnt from cyber-attacks on industrial control systems. International Journal of Critical Infrastructure Protection, 35, 100464.
91. Mooi, R. D. (2014). A model for security incident response in the South African National Research and Education Network.
92. Mohamed, N., Alam, E., & Stubbs, G. L. (2022). Multi-layer protection approach MLPA for the detection of advanced persistent threat. Journal of Positive School Psychology, 4496–4518.
93. Möller, D. P. (2023). NIST cybersecurity framework and MITRE cybersecurity criteria. In Guide to Cybersecurity in Digital Transformation: Trends, Methods, Technologies, Applications and Best Practices (pp. 231–271). Cham: Springer Nature Switzerland.
94. Mokhtarian, I. (2024). Utilizing Process Mining and Deep Learning to Detect IoT/IIoT Cyberattacks–A Hybrid Approach (Doctoral dissertation, University of Illinois at Chicago).
95. Mutalib, N. H. A., Sabri, A. Q. M., Wahab, A. W. A., Abdullah, E. R. M. F., & AlDahoul, N. (2024). Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review. Artificial Intelligence Review, 57(11), 297.
96. Nadji, B. (2024). Data Security, Integrity, and Protection. In Data, Security, and Trust in Smart Cities (pp. 59–83). Cham: Springer Nature Switzerland.
97. Opoku-Mensah, E., Abilimi, C. A., & Boateng, F. O. (2013). Comparative analysis of efficiency of fibonacci random number generator algorithm and gaussian Random Number Generator Algorithm in a cryptographic system. Comput. Eng. Intell. Syst, 4, 50-57.
98. Opoku-Mensah, E., Abilimi, A. C., & Amoako, L. (2013). The Imperative Information Security Management System Measures In the Public Sectors of Ghana. A Case Study of the Ghana Audit Service. International Journal on Computer Science and Engineering (IJCSE), 760-769.
99. Park, N. E., Lee, Y. R., Joo, S., Kim, S. Y., Kim, S. H., Park, J. Y., … & Lee, I. G. (2023). Performance evaluation of a fast and efficient intrusion detection framework for advanced persistent threat-based cyberattacks. Computers and Electrical Engineering, 105, 108548.
100. Park, S. H., Yun, S. W., Jeon, S. E., Park, N. E., Shim, H. Y., Lee, Y. R., … & Lee, I. G. (2022). Performance evaluation of open-source endpoint detection and response combining google rapid response and osquery for threat detection. IEEE Access, 10, 20259–20269.
101. Potts, J. L. (2020). A Grounded Theory Study of the Use of Workflow Modeling to Support Cybersecurity Incident Response Procedures (Doctoral dissertation, Capella University).
102. Rajendran, R. M., & Vyas, B. (2024, March). Detecting apt using machine learning: Comparative performance analysis with proposed model. In SoutheastCon 2024 (pp. 1064–1069). IEEE.
103. Raghavendra, K. (2023). CHALLENGES IN SECURING BANKING SYSTEMS: EMERGING TRENDS, RISKS, AND DEFENSIVE STRATEGIES.
104. Repetto, M. (2023). Adaptive monitoring, detection, and response for agile digital service chains. Computers & Security, 132, 103343.
105. Roberts, S. J., & Brown, R. (2017). Intelligence-driven incident response: Outwitting the adversary. O’Reilly Media, Inc.
106. Salem, A. H., Azzam, S. M., Emam, O. E., & Abohany, A. A. (2024). Advancing cybersecurity: a comprehensive review of AI-driven detection techniques. Journal of Big Data, 11(1), 105.
107. Samtani, S., Abate, M., Benjamin, V., & Li, W. (2020). Cybersecurity as an industry: A cyber threat intelligence perspective. In The Palgrave Handbook of International Cybercrime and Cyberdeviance (pp. 135–154).
108. Sharma, A., Gupta, B. B., Singh, A. K., & Saraswat, V. K. (2023). Advanced persistent threats (apt): evolution, anatomy, attribution and countermeasures. Journal of Ambient Intelligence and Humanized Computing, 14(7), 9355–9381.
109. Sharma, A., Gupta, B. B., Singh, A. K., & Saraswat, V. K. (2023). Advanced persistent threats (apt): evolution, anatomy, attribution and countermeasures. Journal of Ambient Intelligence and Humanized Computing, 14(7), 9355–9381.
110. Singh, S., Sharma, P. K., Moon, S. Y., Moon, D., & Park, J. H. (2019). A comprehensive study on APT attacks and countermeasures for future networks and communications: challenges and solutions. The Journal of Supercomputing, 75, 4543–4574.
111. Su, A. Y. (2024). Relationship of Cyber Threat Intelligence and Critical Infrastructure Assets on Information Technology Critical Infrastructure Attacks (Doctoral dissertation, Walden University).
112. Sun, N., Ding, M., Jiang, J., Xu, W., Mo, X., Tai, Y., & Zhang, J. (2023). Cyber threat intelligence mining for proactive cybersecurity defense: A survey and new perspectives. IEEE Communications Surveys & Tutorials, 25(3), 1748–1774.
113. Takahashi, K., Ide, T., Takahashi, I., Tokito, K., & Sasaki, T. (2021). Building cooperation: Cyber, critical technology and national security. Quad Tech Network Series. Australian National University.
114. Tang, B., Wang, J., Yu, Z., Chen, B., Ge, W., Yu, J., & Lu, T. (2022). Advanced Persistent Threat intelligent profiling technique: A survey. Computers and Electrical Engineering, 103, 108261.
115. Tuovinen, J., & Frilander, K. (2019). Militarizing red teaming: agile and scalable process for cyber red teaming using adaptive planning and execution framework (Master’s thesis).
116. Tounsi, W., & Rais, H. (2018). A survey on technical threat intelligence in the age of sophisticated cyber attacks. Computers & Security, 72, 212–233.
117. Watters, P. A. (2023). The Cyber Operational Environment. In Counterintelligence in a Cyber World (pp. 19–29). Cham: Springer International Publishing.
118. Wisdom, D. D., Vincent, O. R., Adebayo, A. A., Olusegun, F., & Ayetuoma, I. O. (2024). Security Measures in Computational Modeling and Simulations. In Computational Modeling and Simulation of Advanced Wireless Communication Systems (pp. 112–150).
119. Yaseen, A. (2023). AI-driven threat detection and response: A paradigm shift in cybersecurity. International Journal of Information and Cybersecurity, 7(12), 25–43.
120. Yuan, S., & Wu, X. (2021). Deep learning for insider threat detection: Review, challenges and opportunities. Computers & Security, 104, 102221.
121. Yeboah, T., Opoku-Mensah, E., & Abilimi, C.A.(2013a). A Proposed Multiple Scan Biometric-Based Registration System for Ghana Electoral Commission. Journal of Engineering, Computers & Applied Sciences (JEC&AS), 2(7).
122. Yeboah, D. T., Odabi, I., & Abilimi Odabi, M. C. A. A. (2016). Utilizing divisible load scheduling theorem in round robin algorithm for load balancing in cloud environment.
123. Yeboah, T., Opoku-Mensah, E., & Abilimi, C. A. (2013b). Automatic Biometric Student Attendance System: A Case Study Christian Service University College. Journal of Engineering Computers & Applied Sciences, 2(6), 117-121.
124. Yeboah T. & Abilimi C.A. (2013). Using Adobe Captivate to creative Adaptive Learning Environment to address individual learning styles: A Case study Christian Service University, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181,www.ijert.org, “2(11).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research and Modern Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PlumX Metrics takes 2–4 working days to display the details. As the paper receives citations, PlumX Metrics will update accordingly.
