Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essential in the development of cybersecurity solutions. However, sharing the same fundamental limitation with other DL application domains, such as computer vision (CV) and natural language processing (NLP), AI-based cybersecurity solutions are incapable of justifying the results (ranging from detection and prediction to reasoning and decision-making) and making them understandable to humans. Consequently, explainable AI (XAI) has emerged as a paramount topic addressing the related challenges of making AI models explainable or interpretable to human users. It is particularly relevant in cybersecurity domain, in that XAI may allow security operators, who are overwhelmed with tens of thousands of security alerts per day (most of which are false positives), to better assess the potential threats and reduce alert fatigue. We conduct an extensive literature review on the intersection between XAI and cybersecurity. Particularly, we investigate the existing literature from two perspectives: the applications of XAI to cybersecurity (e.g., intrusion detection, malware classification), and the security of XAI (e.g., attacks on XAI pipelines, potential countermeasures). We characterize the security of XAI with several security properties that have been discussed in the literature. We also formulate open questions that are either unanswered or insufficiently addressed in the literature, and discuss future directions of research. 

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Explainable artificial intelligence for cybersecurity: a literature survey

Seasonal Forecast of Winter Precipitation Over China Using Machine Learning Models

The wellbore stability is an important issue in the drilling operation. Wellbore instability can interrupt the drilling and waste lots of time and money. The drilling mud is used to keep up the stability of the wellbore. Therefore, selecting the proper mud weight is an important issue in the drilling industry. The goal of this research is presenting an efficient mud weight estimator using deep learning techniques. To obtain this goal, a relatively big dataset (contained more than half-million samples) has been compiled from 116 wells of two fields in the United Kingdom and Norway. Our main contributions are assembling this large dataset, and applying the deep learning techniques to obtain efficient mud weight estimators. Our estimator is an artificial neural network with five hidden layers and 256 nodes in each layer that is able to estimate the mud weight for new wells and depths with the mean absolute error (MAE) of less than ±0.04 pound per gallon (ppg). In various experiments, the presented model has been challenged and the real-world conditions have been simulated. The results have shown that our model can be reliable and efficient in the real world. 

Determination of drilling mud weight using deep learning techniques

Improving the Detection of Malefactors Cyberattacks Using Interpretable Artificial Intelligence Models

Machine learning (ML) models are often adopted in malware detection systems. To ensure the detection performance in such ML-based systems, updating ML models with new data is crucial for minimizing the influence of data variation over time. After an update, validating the new model is commonly done using the detection accuracy as a metric. However, the accuracy does not include detailed information, such as changes in the features used for prediction. Such information is beneficial for avoiding unexpected updates, such as overfitting or noneffective updates. We, therefore, propose a method for understanding ML model updates in malware detection systems by using a feature attribution method called Shapley additive explanations (SHAP), which interprets the output of an ML model by assigning an importance value called a SHAP value to each feature. In our method, we identify patterns of feature attribution changes that cause a change in the prediction. In this method, we first obtain the feature attributions for each sample, which change before and after the update. Then, we obtain the patterns of the changes in the feature attributions that are common for multiple samples by clustering the changes in the feature attributions. In this study, we conduct experiments using an open dataset of Android malware and demonstrate that our method can identify the causes of performance changes, such as overfitting or noneffective updates.

Understanding Update of Machine-Learning-Based Malware Detection by Clustering Changes in Feature Attributions.

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Understanding Update of Machine-Learning-Based Malware Detection by Clustering Changes in Feature Attributions.