In recent years, machine learning (ML) has become an important part to yield security and
privacy in various applications. ML is used to address serious issues such as real-time …

Today's world is highly network interconnected owing to the pervasiveness of small personal
devices (eg, smartphones) as well as large computing devices or services (eg, cloud …

The majority of machine learning methodologies operate with the assumption that their
environment is benign. However, this assumption does not always hold, as it is often …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

Most existing machine learning classifiers are highly vulnerable to adversarial examples. An
adversarial example is a sample of input data which has been modified very slightly in a way …

With the increasing sophistication of cyber threats, the integration of machine learning (ML)
techniques in network security has become imperative for detecting and mitigating evolving …

Machine learning systems have had enormous success in a wide range of fields from
computer vision, natural language processing, and anomaly detection. However, such …

Adversarial machine learning (AML) is a recent research field that investigates potential
security issues related to the use of machine learning (ML) algorithms in modern artificial …

In recent years, artificial intelligence technology represented by deep learning has achieved
remarkable results in image recognition, semantic analysis, natural language processing …