The rise of online transactions has led to a corresponding increase in online criminal
activities. Account takeover attacks, in particular, are challenging to detect, and novel …

Deep learning-based autoencoders represent a promising technology for use in network-
based attack detection systems. They offer significant benefits in managing unknown …

Tensor based methods have gained popularity for being able to represent multi-aspect real
world data in a lower dimensional space. Among them, methods with orthogonal factors …

We propose an efficient distributed out-of-memory implementation of the non-negative
matrix factorization (NMF) algorithm for heterogeneous high-performance-computing …

Supervisory Control and Data Acquisition (SCADA) systems often serve as the nervous
system for substations within power grids. These systems facilitate real-time monitoring, data …

Malware continues to be one of the most dangerous and costly cyber threats to national
security. As of last year, over 1.3 billion malware specimens have been documented …

Accurate labeling is important for detecting malware and building reference datasets which
can be used for evaluating machine learning (ML) based malware classification and …

The digital era generates enormous amounts of unstructured data such as images and
documents, requiring specific processing methods to extract value from them. Textual data …

The evolving cyber-attack landscape demands flexible and precise protection for information
and networks. Network anomaly detection (NAD) systems play a crucial role in preventing …

Lateral movement is a crucial phase of advanced cyberattacks, during which attackers
propagate from host to host within the targeted network. State-of-the-art methods for …