论文标题
自动网络防御引入风险：我们可以管理风险吗？
Autonomous Cyber Defense Introduces Risk: Can We Manage the Risk?
论文作者
论文摘要
从拒绝服务攻击到勒索软件或其他恶意软件在组织的网络中的传播，手动操作的防御能力可能无法在所需的规模上实时响应，并且当检测到违规行为并进行修复时，损坏已经造成。因此，自主网络防御对于减轻成功攻击及其损害的风险至关重要，尤其是在这些防御措施中所需的响应时间，精力和准确性是不切实际或不可能通过人类操作的防御措施时的不切实际或不可能的。自主代理有可能将ML与大量有关已知网络攻击的数据作为输入，以学习模式并预测未来攻击的特征。此外，从过去和现在的攻击中学习使防御能够适应与先前攻击共享特征的新威胁。另一方面，自主网络防御引发了意外伤害的风险。由自主辩护人产生的行动可能会对功能，安全，道德或道德性质产生有害后果。在这里，我们专注于机器学习培训，算法反馈和算法约束，目的是激励人们讨论对自主网络防御的信任。
From denial-of-service attacks to spreading of ransomware or other malware across an organization's network, it is possible that manually operated defenses are not able to respond in real time at the scale required, and when a breach is detected and remediated the damage is already made. Autonomous cyber defenses therefore become essential to mitigate the risk of successful attacks and their damage, especially when the response time, effort and accuracy required in those defenses is impractical or impossible through defenses operated exclusively by humans. Autonomous agents have the potential to use ML with large amounts of data about known cyberattacks as input, in order to learn patterns and predict characteristics of future attacks. Moreover, learning from past and present attacks enable defenses to adapt to new threats that share characteristics with previous attacks. On the other hand, autonomous cyber defenses introduce risks of unintended harm. Actions arising from autonomous defense agents may have harmful consequences of functional, safety, security, ethical, or moral nature. Here we focus on machine learning training, algorithmic feedback, and algorithmic constraints, with the aim of motivating a discussion on achieving trust in autonomous cyber defenses.
