The Evolution of China's Cyber-Espionage Tactics: From Traditional Espionage to AI-Driven Cyber Threats against Critical Infrastructure in the West
DOI:
https://doi.org/10.47672/ajir.2424Keywords:
Cyber-Espionage L86, Artificial Intelligence O33, D74, Geopolitical Implications F51, National Security H56, Critical Infrastructure L86Abstract
Purpose: This article critically investigates the evolution of China’s cyber-espionage strategies, specifically illustrating the shift from traditional espionage methodologies to the incorporation of advanced technologies, particularly artificial intelligence (AI). This transition profoundly reshapes global power dynamics, delineating nuanced threats to critical infrastructure in Western nations, including power grids, financial systems, and communication networks (Wang et al., 2019).
Materials and Methods: Utilizing a theoretical framework grounded in Joseph Nye's concept of soft power and contemporary security studies, this research posits a hypothesis: there exists a positive correlation between technological advancements and the escalation of espionage activities by state actors. The inquiry encompasses a comprehensive analysis of key components, such as vulnerabilities, adaptive strategies, geopolitical implications, deterrence mechanisms, and international collaboration, thereby illuminating the multifaceted risks to national security inherent in the digital age (Nye, 2004).
Findings: The study critically evaluates the countermeasures undertaken by Western countries, probing strategic enhancements of cyber defences and the formation of international coalitions aimed at collective security (Huang et al., 2021). The findings reveal substantial obstacles in achieving a cohesive and effective response to the rapidly escalating and pervasive nature of contemporary cyber threats (Zhang et al., 2020).
Implications to Theory, Practice and Policy: Considering the ongoing maturation of China’s cyber capabilities, characterized by an increased reliance on AI and the impending advent of quantum computing, the article advocates for a comprehensive revaluation of global security practices (Mann et al., 2020). It underscores the imperative for Western nations to not only innovate defensively but to also adopt proactive measures and foster significant international collaboration. This multifaceted approach is essential to address the complex challenges posed by state-sponsored cyber operations within an increasingly interconnected global landscape (Chen et al., 2021).
Downloads
References
Abad, C., et al. (2020). Personalization and phishing: A behavioural analysis. Journal of Cyber Behaviour, Psychology and Social Networking, 23(3), 165-172.
Alkaabi, N., et al. (2020). A survey of big data analytics in cybersecurity. Internet of Things, 12, 200-214.
Al-Samarraie, H., et al. (2021). The role of artificial intelligence in enhancing cybersecurity: A systematic review. Computers & Security, 109, 102339.
Arute, F., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505-510.
Bamford, J., et al. (2022). Cyber offensive operations and evasion techniques. Journal of Cybersecurity Studies, 34(2), 235-249.
Batanova, M., et al. (2020). Social engineering attacks: A review of detection techniques and countermeasures. Journal of Information Security and Applications, 55, 102597.
Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149-158.
Brundage, V., et al. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. ArXiv Preprint ArXiv:1802.07228.
Bryson, J. J. (2018). Artificial intelligence: The revolution hasn't happened yet. Oxford Review of Economic Policy, 34(3), 329-345.
Buchanan, E. (2020). Quantum computing: The future of cybersecurity. Journal of Information Security and Applications, 55, 102159.
Cai, H., et al. (2020). A new model for preventing misinformation in socio-technical systems. Scientific Reports, 10, 12386.
Chen, Q., et al. (2018). Covert data exfiltration via mimicry of normal traffic. International Journal of Information Security, 17(4), 377-387.
Chen, Y., et al. (2021). Quantum algorithms for attacking classical cryptography: A survey. Cryptography, 5(4), 30.
Chesney, R., & Citron, D. K. (2019). Deep fakes and the new disinformation war: The slack of truth in politics. Foreign Affairs, 98(1), 36-43.
Dehghantanha, A., et al. (2018). Digital forensics techniques: History, state of the art, and future directions. Computers & Security, 76, 186-200.
Deng, R., et al. (2020). Smart incident response in cybersecurity: A survey. IEEE Transactions on Information Forensics and Security, 15, 2981-2998.
Depoint, N., et al. (2021). Data-driven decision making in cybersecurity: A meta-analysis. Computers & Security, 104, 102159.
Dhamija, R., et al. (2006). Why phishing works. Proceedings of the Second Symposium on Usable Privacy and Security, 129-140.
Farinella, D., et al. (2019). Malware classification: A new perspective. IEEE Access, 7, 17336-17344.
Farrell, H., & Newman, A. L. (2019). The governance of artificial intelligence: An international perspective. International Studies Review, 21(3), 337-349.
Fischer, H., et al. (2019). Human factors in cybersecurity: A literature review. Computers & Security, 83, 248-269.
Gaikwad, A., et al. (2019). A survey on AI techniques for cybersecurity: Challenges and opportunities. Journal of Cyber Security Technology, 3(2), 84-101.
Guberman, A., et al. (2020). Balancing security with civil liberties: Lessons from AI and cybersecurity. Journal of Cyber Policy, 6(3), 396-421.
Gulati, G., et al. (2020). Misinformation and trust in the age of internet: A literature review. Journal of Information Ethics, 29(2), 213-235.
Gupta, S., et al. (2021). Evaluating machine learning techniques for evasion detection in cybersecurity. Computers & Security, 110, 102413.
Holt, T. J., & Bossler, A. M. (2016). Cybercrime and society. Thousand Oaks, CA: Sage Publications.
Hossain, M. M., et al. (2020). Data exfiltration detection in cloud environments. IEEE Transactions on Cloud Computing, 10(2), 931-945.
Huang, W., et al. (2021). Real-time adaptation of cyber defence systems driven by data analytics. ACM Transactions on Internet Technology, 21(3), 1-29.
Huang, Z., et al. (2020). Insider threat detection: A survey of the state of the art. IEEE Transactions on Information Forensics and Security, 15, 40-57.
Jha, S., et al. (2019). Polymorphic malware detection and analysis: A survey. Journal of Computer Virology and Hacking Techniques, 15(1), 1-20.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
Kahani, M., et al. (2022). Quantum computing in cybersecurity: Current trends and future directions. IEEE Access, 10, 12386-12396.
Kahn, B., et al. (2020). Accountability in the age of artificial intelligence: Who is responsible for AI decisions? AI & Society, 35(3), 629-639.
Katz, F., et al. (2021). Understanding the impacts of machine learning on offensive cyber operations. Journal of Cyber Policy, 6(2), 145-165.
Koutroumpouchos, N., et al. (2021). Cyber threat intelligence in organization’s network security. IEEE Access, 9, 96646-96666.
Krah, A., et al. (2018). The effect of algorithms on the dynamics of disinformation campaigns. Digital Journalism, 6(10), 1267-1281.
Leblanc, M., et al. (2021). Automating vulnerability discovery: Current challenges and future opportunities. International Journal of Information Security, 20(5), 245-262.
Liu, H., & Wu, J. (2019). Cybersecurity threats emerging from automated tools: A systematic review. Journal of Cybersecurity Research, 22(4), 215-231.
Liu, Y., et al. (2019). Automation of vulnerability scanning in web applications: Promising approaches and future directions. IEEE Access, 7, 35760-35772.
Lohr, S. (2021). The role of automation in data management: Transforming cybersecurity practices. Journal of Cybersecurity Assessment, 19(3), 120-140.
Lyul’ko, V., et al. (2021). Automation of cybersecurity processes and its influence on cyber threats. ACM Transactions on Internet Technology, 21(4), 44-55.
Mande, V., et al. (2018). A survey on anomaly detection techniques in cyber security. Journal of Computer Networks and Communications, 2018, 1-21.
Mann, A., et al. (2020). AI for cybersecurity: Opportunities and challenges. IEEE Security & Privacy, 18(4), 12-18.
Miller, A., et al. (2021). The benefits of data analytics for cyber threat intelligence. Journal of Information Assurance and Security, 16(6), 324-336.
Montanaro, A. (2016). Quantum algorithms for fixed qubit architectures. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 472(2187), 20160736.
Moustafa, N., et al. (2019). Anomaly-based intrusion detection systems: A survey and comparison. Journal of Network and Computer Applications, 139, 179-199.
Ngai, E. W. T., et al. (2019). A review of machine learning techniques in cybersecurity: Issues and recommendations. Journal of Intelligent Manufacturing, 30(1), 673-686.
Orcutt, M., et al. (2020). Coordination of cyber-attacks with automated tools: Examining advances and challenges. Artificial Intelligence Review, 53(2), 881-905.
Pereira, A., et al. (2021). Collaborative cyber threat intelligence sharing: A review of competing interests and financial incentives. Computers & Security, 104, 102158.
Rid, T., & McBurney, P. (2012). Cyber war will not take place. Journal of Strategic Studies, 35(1), 5-32.
Sahouria, A., & Bandi, M. (2020). Leveraging cyber threat intelligence for enterprise risk management. Computers & Security, 97, 101947.
Sauer, B., et al. (2021). Improving cybersecurity through advanced algorithms and automation. IEEE Internet Computing, 25(3), 14-20.
Sengupta, A., et al. (2020). Cybersecurity in the age of AI and machine learning: A review. Journal of Information Security and Applications, 54, 102421.
Shor, P. W. (1997). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Journal on Computing, 26(5), 1484-1509.
Siegfried, J. A., et al. (2020). Attacking through adapting: Machine learning for dynamic intrusion techniques. Journal of Cyber Security Technologies, 4(1), 45-67.
Singh, K., et al. (2020). Individual target profiling and its role in cybersecurity threat landscape: A real-world perspective. International Journal of Information Systems, 42(3), 277-293.
Taneja, S., et al. (2021). Targeting information in the age of misinformation: Merging data analytics with social media intelligence. American Behavioural Scientist, 65(3), 323-347.
Wang, H., et al. (2019). Target identification in cyber espionage: Data mining techniques and cyber threat analysis. ACM Computing Surveys, 52(4), 1-35.
Wang, Q., et al. (2020). The global race for quantum computing: Opportunities and implications for cybersecurity. IEEE Access, 8, 160501-160509.
Xiong, R., et al. (2021). Data-driven cyber threat intelligence: Enabling rapid response through AI. Journal of Cyber Security Technology, 5(4), 214-233.
Yang, Y., et al. (2019). A study of machine learning applications in cybersecurity: Current developments and future directions. IEEE Access, 7, 96274-96288.
Zhang, H., et al. (2020). The evolution and detection of polymorphic malware: A survey. ACM Computing Surveys, 53(2), 1-36.
Zhao, W., et al. (2019). Detection of using covert channels for data exfiltration. IEEE Access, 7, 12344-12358.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Christian C. Madubuko, Chamunorwa Chitsungo
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
