Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network

Design Network Intrusion Detection System using hybrid Fuzzy-Neural Network

As networks grow both in importance and size, there is an increasing need for effective security monitors such as Network Intrusion Detection System to prevent such illicit accesses. Intrusion Detection Systems technology is an effective approach in dealing with the problems of network security. In this paper, we present an intrusion detection model based on hybrid fuzzy logic and neural networ...

Enhancing Network Intrusion Detection with Integrated Sampling and Filtering

The structure of many standalone network intrusion detection systems (NIDSs) centers around a chain of analysis that begins with packets captured by a packet filter, where the filter describes the protocols (TCP/UDP port numbers) and sometimes hosts or subnets to include or exclude from the analysis. In this work we argue for augmenting such analysis with an additional, separately filtered stre...

Improving Network Intrusion Detection with Growing Hierarchical Self-Organizing Maps

Nowadays, the growth of the computer networks and the expansion of the Internet have made the security to be a critical issue. In fact, many proposals for Intrusion Detection/Prevention Systems (IDS/IPS) have been proposed. These proposals try to avoid that corrupt or anomalous traffic reaches the user application or the operating system. Nevertheless, most of the IDS/IPS proposals only disting...

Hybrid Multi Agent-Neural Network Intrusion Detection with Mobile Visualization

A multiagent system that incorporates an Artificial Neural Networks based Intrusion Detection System (IDS) has been defined to guaranty an efficient computer network security architecture. The proposed system facilitates the intrusion detection in dynamic networks. This paper presents the structure of the Mobile Visualization Connectionist Agent-Based IDS, more flexible and adaptable. The propo...

An Improved Intrusion Detection Approach using Synthetic Minority Over-Sampling Technique and Deep Belief Network