With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

The selection of word embedding and deep learning models for better outcomes is vital.
Word embeddings are an n-dimensional distributed representation of a text that attempts to …

With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

A feature-based model explanation denotes how much each input feature contributes to a
model's output for a given data point. As the number of proposed explanation functions …

Despite decades of research in network traffic analysis and incredible advances in artificial
intelligence, network intrusion detection systems based on machine learning (ML) have yet …

Unsupervised Deep Learning (DL) techniques have been widely used in various security-
related anomaly detection applications, owing to the great promise of being able to detect …

Malicious applications (particularly those targeting the Android platform) pose a serious
threat to developers and end-users. Numerous research efforts have been devoted to …

Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine
learning (ML) pipelines. We systematize the increasingly growing (but fragmented) …

Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security
risks to many software systems. Given the limited resources in practice, SV assessment and …

EXplainable AI (XAI) methods have been proposed to interpret how a deep neural network
predicts inputs through model saliency explanations that highlight the input parts deemed …