One-way Explainability Isn't The Message

Srinivasan, Ashwin; Bain, Michael; Coiera, Enrico

doi:10.48550/arxiv.2205.08954

Cited by 1 publication

(2 citation statements)

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“…The XAI systems used for this research are still very rudimentary, they do not yet fulfill the complete standard expected by Srinivasan et al (2022) in which an XAI environment should to be two-way, requiring the XAI to be further capable of refuting the hypotheses of their human collaborator.…”

Section: Discussionmentioning

confidence: 99%

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XAI Human-Machine collaboration applied to network security

Moyle,

Martin,

Allott

2024

Front. Comput. Sci.

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Cyber attacking is easier than cyber defending—attackers only need to find one breach, while the defenders must successfully repel all attacks. This research demonstrates how cyber defenders can increase their capabilities by joining forces with eXplainable-AI (XAI) utilizing interactive human-machine collaboration. With a global shortfall of cyber defenders there is a need to amplify their skills using AI. Cyber asymmetries make propositional machine learning techniques impractical. Human reasoning and skill is a key ingredient in defense and must be embedded in the AI framework. For Human-Machine collaboration to work requires that the AI is an ultra-strong machine learner and can explain its models. Unlike Deep Learning, Inductive Logic Programming can communicate what it learns to a human. An empirical study was undertaken using six months of eavesdropped network traffic from an organization generating up-to 562K network events daily. Easier-to-defend devices were identified using a form of the Good-Turing Frequency estimator which is a promising form of volatility measure. A behavioral cloning grammar in explicit symbolic form was then produced from a single device's network activity using the compression algorithm SEQUITUR. A novel visualization was generated to allow defenders to identify network sequences they wish to explain. Interactive Inductive Logic Programming (the XAI) is supplied the network traffic meta data, sophisticated pre-existing cyber security background knowledge, and one recurring sequence of events from a single device to explain. A co-inductive process between the human cyber defender and the XAI where the human is able to understand, then refute and shape the XAI's developing model, to produce a model that conforms with the data as well as the original device designers programming. The acceptable model is in a form that can be deployed as an ongoing active cyber defense.

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Section: Discussionmentioning

confidence: 99%

“…A strong humanmachine system will be one where each party can argue both for and against the communicated understanding of the other. A framework for this scenario is reported in Srinivasan et al (2022).…”

Section: Human-machine Collaboration In Scientific Discoverymentioning

confidence: 99%