Phishing and spear phishing are typical examples of masquerade attacks since trust is built
up through impersonation for the attack to succeed. Given the prevalence of these attacks …

In federated learning, multiple client devices jointly learn a machine learning model: each
client device maintains a local model for its local training dataset, while a master device …

In the realm of cybersecurity, phishing stands as a prevalent cyber attack, where attackers
employ various tactics to deceive users into gathering their sensitive information, potentially …

Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …

Machine learning is enabling a myriad innovations, including new algorithms for cancer
diagnosis and self-driving cars. The broad use of machine learning makes it important to …

The incremental diffusion of machine learning algorithms in supporting cybersecurity is
creating novel defensive opportunities but also new types of risks. Multiple researches have …

Despite an extensive anti-phishing ecosystem, phishing attacks continue to capitalize on
gaps in detection to reach a significant volume of daily victims. In this paper, we isolate and …

Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning
(ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where …

Recently, many studies have demonstrated deep neural network (DNN) classifiers can be
fooled by the adversarial example, which is crafted via introducing some perturbations into …

Many of today's machine learning (ML) systems are built by reusing an array of, often pre-
trained, primitive models, each fulfilling distinct functionality (eg, feature extraction). The …