In the world of AI, cybersecurity and explainable AI (XAI) are a match made in heaven, since AI is becoming more and more of a critical aspect in security applications., so as to ensure those applications are transparent and accountable.
In this article, we will get a glimpse into how those two worlds collaborate and walk hand in hand, the top 5 benefits of their intersection, and 2 use cases that will reveal to you that cybersecurity is no joke and essential for all industries.
Cybersecurity, also known as information security, is the practice of protecting computer systems, networks, and digital information from unauthorized access, theft, damage, or disruption. Cybersecurity is becoming increasingly important as more and more business, government, and personal activities are conducted online. With the proliferation of connected devices and the growth of the internet of things (IoT), there are more opportunities for cyber attacks to occur. Cybersecurity threats can come from a variety of sources, including hackers, cyber criminals, state-sponsored actors, and even insiders with malicious intent.
XAI stands for Explainable Artificial Intelligence. It refers to a set of methods and techniques used to develop and design artificial intelligence systems that can provide clear explanations of their decision-making processes and predictions. Explainable AI is increasingly important as AI systems are being used to make critical decisions in various industries, such as healthcare, finance, and cybersecurity. In some cases, it is important to be able to understand how an AI system arrived at a particular decision, not just that it made the decision. XAI techniques include rule-based models, decision trees, and symbolic models, which can provide a clear and transparent explanation of how the AI system arrived at a particular decision. Other techniques involve generating natural language explanations or visual representations of the decision-making process.
Explanation methods are used to identify the contribution of each data parameter to the classification made by ML algorithms. Here’s a classification of methods into two families: local explanation and global explanation methods.
This type is used to understand the classification of a single data input.
A.1 Local surrogate models is a model that accurately does approximation in a local feature space around a single input, explaining an individual prediction. A surrogate model itself is a statistical model that has been trained to accurately approximate the output of a black-box model.
A.2 SHapley Additive exPlanations (SHAP) is a method to explain individual predictions in a black-box setting. The prediction is based on the Shapley value, an average contribution value of a feature across all possible combinations.
The main purpose of SHAP is to measure the contribution of each feature to the prediction result. It can be described using the following formula:
where g denotes the explanation model, ' denotes the combination vector, M denotes the maximum combination size, and ; denotes the feature attribution for feature j.
Feature attribution indicates the contribution level of each feature to the prediction result.
A.3 Anchors modeling is an explanation method on any black-box model that attempts to find a decision rule for the prediction process. A rule becomes an anchor of a prediction if changes in any feature value will not affect the prediction result.
A.4 Individual Conditional Expectation (ICE) is an explanation method that uses a line plot for each instance to demonstrate the degree of variation in predictions when a feature is modified. ICE focuses on a specific instance and visualizes the prediction dependence of each feature separately. Thus, it can uncover a heterogeneous relationship with an intuitive curve that is legible. However, ICE can only display one feature at a time. There can also be some invalid data points if the feature of interest correlates with another feature.
A.5 Counterfactual explanations represent a causal scenario that can be described as “If A does not happen, B will not happen.” When applied in XAI, this concept describes the smallest change in feature values that can affect the output. Counterfactual explanations can be applied to both model-agnostic and model-specific scenarios. A counterfactual instance must generate predefined predictions as close as possible with similar instances regarding feature values.
Being the counter method, global explanations are focused on the overall behavior of the model, the average distribution of data.
B.1 Partial dependence plot (PDP) is an explanation method that illustrates the marginal effect of a feature on the output of an AI model. The PDP focuses on the overall average instance, instead of a specific one. Thus, it is also the opposite of ICE.
B.2 Accumulated Local Effects (ALE) explain the influence of a feature on the prediction result of an AI model on aver- age. The concept of ALE was introduced to address the main limitation of a PDP: its fidelity level reduces drastically if the features in the AI model are correlated. ALE shows the variation of model prediction in a small area where the ana- lyzed input is located.
B.3 Global surrogate model is an explainable method for generating a surrogate model by approximating the predic- tion result and the interpretability of the underlying explain- ability model. First, a dataset is selected (it can be the same dataset that was used to train the underlying model or a new dataset). Then, for the selected dataset, the prediction result is derived from the original model. Subsequently, an inter- pretable model is trained based on the dataset and its predic- tion. Finally, a global surrogate model is generated.
B.4 Feature Interaction is an explainable method based on marginal distribution estimations. It was proposed to address the problem that when features are correlated, the prediction cannot be manifested as the sum of feature effects. The effect of one feature influences other features. The feature interaction concept states that the interaction between features represents a change in the prediction result, which happens by varying the features considering each feature effect.
B.5 Functional decomposition is a method that constructs a visualization of individual features and interaction effects. The prediction function can be represented as the sum of functional components.
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Conclusion
In conclusion, as AI becomes more prevalent in cybersecurity applications, the importance of Explainable AI (XAI) cannot be overstated. XAI techniques can help improve root cause analysis, regulatory compliance, threat detection and response, and trust in AI-powered decisions. This article explored the top benefits of XAI for cybersecurity, as well as local and global explanation methods used to develop and design AI systems that can provide clear explanations of their decision-making processes and predictions.
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