The Impact of Machine Learning on Future Defense Strategies
DOI:
https://doi.org/10.33592/pelita.v23i2.4824Keywords:
autonomous defense systems, defense cybersecurity, logistics, machine learning, resource allocationAbstract
Abstract:
Machine learning (ML) is increasingly pivotal in shaping future defense strategies, offering advancements across critical domains such as cybersecurity, resource allocation, and autonomous systems. This research explores the multifaceted impact of ML in defense, focusing on three primary areas: enhancing threat detection and mitigation in cybersecurity, optimizing resource allocation and logistics in military operations, and navigating the ethical and strategic implications of autonomous defense systems. The study utilizes qualitative research methods, particularly through secondary data analysis from government reports, academic publications, industry white papers, and ethical frameworks. Findings indicate that ML algorithms significantly bolster threat detection by leveraging Anomaly Detection Theory and Game Theory, thereby enhancing responsiveness to cyber threats. In military logistics, ML models informed by Operations Research and Supply Chain Management Theory optimize resource distribution, improve operational efficiencies, and support mission readiness. Ethically, the deployment of ML in autonomous defense systems prompts considerations of moral responsibilities, biases, and strategic risks, necessitating comprehensive governance frameworks. In conclusion, while ML offers transformative potential in defense strategies, effective implementation requires robust ethical guidelines, strategic foresight, and interdisciplinary collaboration to mitigate risks and maximize benefits.
Keywords: autonomous defense systems, defense cybersecurity, logistics, machine learning, resource allocation
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