Synthetically generated benchmark datasets are vitally important for machine learning and
network intrusion research. When producing intrusion datasets for research, providers make …

Ensuring the security and integrity of computer and network systems is of utmost importance
in today's digital landscape. Network intrusion detection systems (NIDS) play a critical role in …

Many security applications require unsupervised anomaly detection, as malicious data are
extremely rare and often only unlabeled normal data are available for training (ie, zero …

Network Intrusion Detection Systems (NIDSs) which use machine learning (ML) models
achieve high detection performance and accuracy while avoiding dependence on fixed …

The fast and efficient failure detection and localization is essential for stable network
transmission. Unfortunately, existing schemes suffer from a few drawbacks such as …

Deploying machine learning (ML) models in programmable switch data planes facilitates
low latency and high throughput traffic inference at line speed. However, data planes pose …

Vulnerability identification has been broadly studied as a way to improve cybersecurity.
Internet of Things (IoT) ecosystems are considered particularly vulnerable as a whole, due to …

The correct functionality (can also be called as functional integrity) from a smart device is
essential towards ensuring their safe and secure operations. The functional integrity of a …

Anomaly-based network intrusion detection systems (A-NIDS) use unsupervised models to
detect unforeseen attacks. However, existing A-NIDS solutions suffer from low throughput …

This paper proposes a general network fingerprinting framework, Seqnature, that uses
packet sequences as its basic data unit and that makes it simple to implement any …