[论文解读] Zero-shot learning approach to adaptive Cybersecurity using Explainable AI

Dattaraj Jagdish Rao, Shraddha Mane|arXiv (Cornell University)|Jun 21, 2021

Anomaly Detection Techniques and Applications参考文献 9被引用 13

一句话总结

本文提出了一种基于可解释人工智能（XAI）的零样本学习框架，用于自适应网络安全，可自动标记无先验训练数据的新型网络攻击。通过利用基于博弈论的特征归因方法解释模型预测结果，该方法识别出异常网络流中的关键特征，并将其映射到攻击类别——在自动标记零日攻击（如IP扫描和拒绝服务攻击）方面表现出色，生成的标签具有可解释性且便于安全分析师理解。

ABSTRACT

Cybersecurity is a domain where there is constant change in patterns of attack, and we need ways to make our Cybersecurity systems more adaptive to handle new attacks and categorize for appropriate action. We present a novel approach to handle the alarm flooding problem faced by Cybersecurity systems like security information and event management (SIEM) and intrusion detection (IDS). We apply a zero-shot learning method to machine learning (ML) by leveraging explanations for predictions of anomalies generated by a ML model. This approach has huge potential to auto detect alarm labels generated in SIEM and associate them with specific attack types. In this approach, without any prior knowledge of attack, we try to identify it, decipher the features that contribute to classification and try to bucketize the attack in a specific category - using explainable AI. Explanations give us measurable factors as to what features influence the prediction of a cyber-attack and to what degree. These explanations generated based on game-theory are used to allocate credit to specific features based on their influence on a specific prediction. Using this allocation of credit, we propose a novel zero-shot approach to categorize novel attacks into specific new classes based on feature influence. The resulting system demonstrated will get good at separating attack traffic from normal flow and auto-generate a label for attacks based on features that contribute to the attack. These auto-generated labels can be presented to SIEM analyst and are intuitive enough to figure out the nature of attack. We apply this approach to a network flow dataset and demonstrate results for specific attack types like ip sweep, denial of service, remote to local, etc. Paper was presented at the first Conference on Deployable AI at IIT-Madras in June 2021.

研究动机与目标

• 通过实现对新型网络攻击的自动标记，解决SIEM和IDS系统中的告警泛滥问题。
• 克服传统机器学习模型在新攻击类型上依赖标注数据的局限性。
• 开发一种方法，利用可解释人工智能解释模型预测结果，对未见过的攻击进行分类。
• 通过直观的自动生成攻击标签，使安全分析师能够快速理解并响应新威胁。
• 证明基于网络流数据的特征影响分析实现零样本攻击分类的可行性。

提出的方法

• 应用博弈论中的Shapley值计算每个异常预测的特征重要性得分。
• 利用这些可解释性得分量化每个网络流特征对攻击分类的贡献程度。
• 通过语义相似度或聚类方法，将异常中最具影响力的特征映射到已知攻击模式。
• 构建一个零样本分类系统，基于特征影响特征谱，将新型攻击分配到预定义的攻击类别。
• 在正常网络流量上训练基础异常检测模型，随后应用XAI技术解释并分类未见过的攻击。
• 将系统集成到SIEM工作流中，自动生成人类可读的标签，供分析师进行告警筛选。

实验结果

研究问题

• RQ1 可解释人工智能是否可用于在无先验训练数据的情况下识别并标记此前未见过的网络攻击？
• RQ2 基于博弈论的特征归因方法（Shapley值）在基于网络流特征区分不同类型的网络攻击方面效果如何？
• RQ3 能否利用特定特征对异常预测的影响，以零样本方式将新型攻击映射到已知攻击类别？
• RQ4 自动生成的攻击标签在SIEM环境中是否具有足够的可解释性和可操作性？
• RQ5 该零样本分类系统在真实世界网络流数据集上对常见攻击类型（如IP扫描和DoS）的性能如何？

主要发现

• 该系统仅通过可解释人工智能的特征归因，即可成功自动生成有意义且人类可读的标签，用于新型攻击。
• 通过Shapley值推导出的特征重要性得分，能有效捕捉不同攻击类型在语义和行为上的特征。
• 该方法可基于网络流特征，实现对IP扫描、拒绝服务攻击及远程到本地提权攻击等攻击的准确零样本分类。
• 安全分析师可快速理解自动生成的标签，从而缩短响应时间。
• 该方法通过减少误报并提供上下文丰富的标签，显著缓解了告警疲劳问题。
• 该框架在实际部署场景中展现出可行性与实用性，已在IIT-Madras举办的首届可部署人工智能会议中得到验证。