Zero-day polymorphic worms pose a serious threat to the Internet security. With their ability
to rapidly propagate, these worms increasingly threaten the Internet hosts and services. Not …

With the rapid evolution and proliferation of botnets, large-scale cyber attacks such as
DDoS, spam emails are also becoming more and more dangerous and serious cyber …

The enormous increase in the use of the Internet in daily life has provided an opportunity for
the intruder attempt to compromise the security principles of availability, confidentiality, and …

Network traffic anomaly may indicate a possible intrusion in the network and therefore
anomaly detection is important to detect and prevent the security attacks. The early research …

Most research in the field of network intrusion detection heavily relies on datasets. Datasets
in this field, however, are scarce and difficult to reproduce. To compare, evaluate, and test …

In this study, we set up a scalable framework for large-scale data processing and analytics
using the big data framework. The popular classification methods are implemented, tuned …

During the last decade, various machine learning and data mining techniques have been
applied to Intrusion Detection Systems (IDSs) which have played an important role in …

Our benchmark data consist of the following 24 statistical features; 14 conventional features
and 10 additional features. Among them, the first 14 features were extracted based on KDD …

One of the key challenges of machine learning-based anomaly detection relies on the
difficulty of obtaining anomaly data for training, which is usually rare, diversely distributed …

Analyzing network traffic data to detect suspicious network activities (ie, intrusions) requires
tremendous effort due to the variability of the data and constant changes in network traffic …