Sensor networks security based on sensitive robots agents. A conceptual model

Multi-agent systems are currently applied to solve complex problems. The security of networks is an eloquent example of a complex and difficult problem. A new model-concept Hybrid Sensitive Robot Metaheuristic for Intrusion Detection is introduced in the current paper. The proposed technique could be used with machine learning based intrusion detection techniques. The new model uses the reaction of virtual sensitive robots to different stigmergic variables in order to keep the tracks of the intruders when securing a sensor network.Comment: 5 page

A Survey of Distributed Intrusion Detection Approaches

Distributed intrustion detection systems detect attacks on computer systems by analyzing data aggregated from distributed sources. The distributed nature of the data sources allows patterns in the data to be seen that might not be detectable if each of the sources were examined individually. This paper describes the various approaches that have been developed to share and analyze data in such systems, and discusses some issues that must be addressed before fully decentralized distributed intrusion detection systems can be made viable

Adding X-security to Carrel: security for agent-based healthcare applications

The high growth of Multi-Agent Systems (MAS) in Open Networks with initiatives such as Agentcities1 requires development in many different areas such as scalable and secure agent platforms, location services, directory services, and systems management. In our case we have focused our effort on security for agent systems. The driving force of this paper is provide a practical vision of how security mechanisms could be introduced for multi-agent applications. Our case study for this experiment is Carrel [9]: an Agent-based application in the Organ and Tissue transplant domain. The selection of this application is due to its characteristics as a real scenario and use of high-risk data for example, a study of the 21 most visited health-related web sites on the Internet discovered that personal information provided at many of the sites was being inadvertently leaked for unauthorized persons. These factors indicate to us that Carrel would be a suitable environment in order to test existing security safeguards. Furthermore, we believe that the experience gathered will be useful for other MAS. In order to achieve our purpose we describe the design, architecture and implementation of security elements on MAS for the Carrel System.Postprint (published version

A consensus based network intrusion detection system

Network intrusion detection is the process of identifying malicious behaviors that target a network and its resources. Current systems implementing intrusion detection processes observe traffic at several data collecting points in the network but analysis is often centralized or partly centralized. These systems are not scalable and suffer from the single point of failure, i.e. attackers only need to target the central node to compromise the whole system. This paper proposes an anomaly-based fully distributed network intrusion detection system where analysis is run at each data collecting point using a naive Bayes classifier. Probability values computed by each classifier are shared among nodes using an iterative average consensus protocol. The final analysis is performed redundantly and in parallel at the level of each data collecting point, thus avoiding the single point of failure issue. We run simulations focusing on DDoS attacks with several network configurations, comparing the accuracy of our fully distributed system with a hierarchical one. We also analyze communication costs and convergence speed during consensus phases.Comment: Presented at THE 5TH INTERNATIONAL CONFERENCE ON IT CONVERGENCE AND SECURITY 2015 IN KUALA LUMPUR, MALAYSI