In this paper, we provide an approach to detect network dictionary attacks using a data set
collected as flows based on which a clustered graph is resulted. These flows provide an …

The amount of network traffic has grown exponentially. Within this torrent of data, attacks are
plentiful. It is no longer possible to rely on methods of deep packet inspection to detect these …

This paper describes the novel network-based approach to a dictionary attack detection with
the ability to recognize successful attack. We analyzed SSH break-in attempts at a flow level …

Data throughput of current high-speed networks makes it prohibitively expensive to detect
attacks using conventional means of deep packet inspection. The network behavior analysis …

In this paper we propose a dictionary learning scheme to extract network traffic pattern
templates for different types of anomalies and the normal traffic via nonnegative matrix …

SSH services are run on many hosts with various scopes other than just operation, so
dictionary attack against the service is a common security threat. SANS has reported the …

In this article we present the use of sparse representation of a signal and incoherent
dictionary learning method for the purpose of network traffic analysis. In learning process we …

How to timely and precisely identify attack behaviors in network without dealing with a large
number of traffic features and historical data, such as training data, is an important research …

As a use of network increases for critical transaction, so huge damages are caused by
intrusion attacks hence there is the need of Computer network security. To protect network …

This article presents the use of sparse representation of signal and incoherent dictionary
learning for solving the problem of anomaly detection. The signals which are analysed in the …