To reach a hardened target, an attacker may try to compromise the system during development. But discovering such a flaw may be difficult if the system is not widely exposed or publicly available. Crafting an attack that can reliably deceive a machine learning system requires knowing a specific flaw in how the system thinks. The first proposition concerns offense: Attackers may need to intrude deep into target networks well in advance of an attack in order to circumvent or defeat machine learning defenses. This dynamic leads to two propositions for how these attack vectors could shape cyber operations. These flaws emerge because of how machine learning systems “think,” and unlike traditional software vulnerabilities, they cannot simply be patched. These attack vectors stem from flaws in machine learning systems that can render them susceptible to deception and manipulation. This study envisions a possible future in which cyber engagements among top-tier actors come to revolve around efforts to target attack vectors unique to machine learning systems or, conversely, defend against attempts to do so. It derives from existing research demonstrating the challenges machine learning faces in dynamic environments with adaptive adversaries. While this forecast is necessarily speculative, its purpose is practical: to anticipate how adversaries might adapt their tactics and strategies, and to determine what challenges might emerge for defenders. Rather than end the cat-and-mouse game between cyber attackers and defenders, machine learning may usher in a dangerous new chapter.Ĭould embracing machine learning systems for cyber defense actually exacerbate the challenges and risks of cyber competition? This study aims to demonstrate the possibility that machine learning could shape cyber operations in ways that drive more aggressive and destabilizing engagements between states. Yet even the most robust machine learning cyber defenses could have potentially fatal flaws that attackers can exploit. Recent advances in machine learning techniques could enable groundbreaking capabilities in the future, including defenses that automatically interdict attackers and reshape networks to mitigate offensive operations. As artificial intelligence begins to transform cybersecurity, the pressure to adapt may put competing states on a collision course.
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