Designing cyber defense systems to account for cognitive biases in human
decision making has demonstrated significant success in improving performance
against human attackers. However, much of the attention in this area has
focused on relatively simple accounts of biases in human attackers, and little
is known about adversarial behavior or how defenses could be improved by
disrupting attacker's behavior. In this work, we present a novel model of human
decision-making inspired by the cognitive faculties of Instance-Based Learning
Theory, Theory of Mind, and Transfer of Learning. This model functions by
learning from both roles in a security scenario: defender and attacker, and by
making predictions of the opponent's beliefs, intentions, and actions. The
proposed model can better defend against attacks from a wide range of opponents
compared to alternatives that attempt to perform optimally without accounting
for human biases. Additionally, the proposed model performs better against a
range of human-like behavior by explicitly modeling human transfer of learning,
which has not yet been applied to cyber defense scenarios. Results from
simulation experiments demonstrate the potential usefulness of cognitively
inspired models of agents trained in attack and defense roles and how these
insights could potentially be used in real-world cybersecurity.

针对人类决策中的认知偏差设计网络防御系统已经取得了显著的成功，但该领域的研究主要集中在相对简单的攻击者认知偏差上，关于对抗行为或通过破坏攻击者行为来提高防御效果的研究较少。本文提出了一种受 Instance-Based Learning Theory，Theory of Mind 和 Transfer of Learning 认知能力启发的新型人类决策模型，通过在安全场景中从防御者和攻击者角色中学习，并对对手的信念，意图和行动做出预测，以更好地抵御各种攻击，这个模型明确建模了人类学习的转移，对网络安全具有实际应用的潜在价值。

攻防学习：网络安全游戏中的学习迁移

Learning to Defend by Attacking (and Vice-Versa): Transfer of Learning  in Cybersecurity Games

This paper introduces Deep Incremental Boosting, a new technique derived from
AdaBoost, specifically adapted to work with Deep Learning methods, that reduces
the required training time and improves generalisation. We draw inspiration
from Transfer of Learning approaches to reduce the start-up time to training
each incremental Ensemble member. We show a set of experiments that outlines
some preliminary results on some common Deep Learning datasets and discuss the
potential improvements Deep Incremental Boosting brings to traditional Ensemble
methods in Deep Learning.

本文介绍一种新技术 Deep Incremental Boosting，它是从 AdaBoost 发展而来，并专门适用于 Deep Learning 方法，它可以缩短训练时间并提高泛化能力。作者借鉴了 Transfer of Learning 方法，以减少每个增量 Ensemble 成员的启动时间，我们展示了一系列在一些常见 Deep Learning 数据集上的实验，印证了一些初步结果，并讨论了 Deep Incremental Boosting 对传统 Ensemble 方法在 Deep Learning 方面带来的潜在改进。