Distributed Intrusion Detection Systems – MetalDS case study

Distributed Intrusion detection Systems: Issues and Challenges

In Distributed Intrusion Detection Systems (DIDS) data is collected from various sources, correlated, and used to determine if systems are under attack or have been compromised. The use of DIDS allows for increased security as there is a broader view of incidents transpiring across the entire network. With data coming from different sources, the problem of correlating and analyzing such large a...

Information sharing for distributed intrusion detection systems

In this paper, we present an information sharing model for distributed intrusion detection systems. The typical challenges faced by distributed intrusion detection systems is what information to share and how to share information. We address these problems by using the Cumulative Sum algorithm to collect statistics at each local system, and use a machine learning approach to coordinate the info...

GP Ensemble for Distributed Intrusion Detection Systems

In this paper an intrusion detection algorithm based on GP ensembles is proposed. The algorithm runs on a distributed hybrid multiisland model-based environment to monitor security-related activity within a network. Each island contains a cellular genetic program whose aim is to generate a decision-tree predictor, trained on the local data stored in the node. Every genetic program operates coop...

A State Transition Model Case Study for Intrusion Detection Systems

A state transition model consisting of a physical layer, a communications sequencing layer and a state transition layer, is proposed for intrusion detection systems. A case study demonstrating the applicability of this approach is described. The case study concentrates on a port scan: states and state transitions based on the underlying layers are detailed. A series of screen shot captures illu...

Intrusion Detection Systems and Intrusion Prevention Systems