Spam emails have been traditionally seen as just annoying and unsolicited emails
containing advertisements, but they increasingly include scams, malware or phishing. In …

Reliable deployment of machine learning models such as neural networks continues to be
challenging due to several limitations. Some of the main shortcomings are the lack of …

Purpose Purveyors of fake news perpetuate information that can harm society, including
businesses. Social media's reach quickly amplifies distortions of fake news. Research has …

Real-world adversarial attacks on machine learning models often feature an asymmetric
structure wherein adversaries only attempt to induce false negatives (eg, classify a spam …

The field of adversarial textual attack has significantly grown over the past few years, where
the commonly considered objective is to craft adversarial examples (AEs) that can …

Natural Language Processing (NLP) models based on Machine Learning (ML) are
susceptible to adversarial attacks--malicious algorithms that imperceptibly modify input text …

One of the stratagems used to deceive spam filters is to substitute vocables with synonyms
or similar words that turn the message unrecognisable by the detection algorithms. In this …

This article proposes an improved Bayesian scheme by focusing on the region in which
Bayesian may fail to correctly identify labels and improve classification performance by …

Phishing is a major cyber threat to organizations that can cause financial and reputational
damage, threatening their existence. The technical measures against phishing should be …

In recent years, the widespread adoption of Machine Learning (ML) at the core of complex IT
systems has driven researchers to investigate the security and reliability of ML techniques. A …