Labeled data sets are necessary to train and evaluate anomaly-based network intrusion
detection systems. This work provides a focused literature survey of data sets for network …

This survey presents a comprehensive overview of machine learning methods for
cybersecurity intrusion detection systems, with a specific focus on recent approaches based …

In network intrusion detection research, one popular strategy for finding attacks is monitoring
a network's activity for anomalies: deviations from profiles of normality previously learned …

Imagine a database packed with sensitive information about many people. Perhaps this
database helps a hospital track its patients, a school its students, or a bank its customers …

Connectivity in today's enterprise networks is regulated by a combination of complex routing
and bridging policies, along with various interdiction mechanisms such as ACLs, packet …

Social networking sites such as Facebook, LinkedIn, and **ng have been reporting
exponential growth rates and have millions of registered users. In this paper, we introduce a …

Publicly available datasets are an indispensable tool for researchers, as they allow testing
new algorithms on a wide range of different scenarios and making scientific experiments …

Advances in technology have made it possible to collect data about individuals and the
connections between them, such as email correspondence and friendships. Agencies and …

Support Vector Machines (SVM) represent one of the most promising Machine Learning
(ML) tools that can be applied to the problem of traffic classification in IP networks. In the …

It is increasingly common for enterprises and other organizations to outsource network
processing to the cloud. For example, enterprises may outsource firewalling, caching, and …