Security in Mission Critical Communication Systems

Author:

Medhat Karen1ORCID,Ramadan Rabie A.1ORCID,Talkhan Ihab1

Affiliation:

1. Cairo University, Egypt

Abstract

This chapter introduces two different algorithms to detect intrusions in mission critical communication systems to guarantee their security. The first algorithm is a classification algorithm which applies the concept of supervised learning. The second algorithm is a clustering algorithm which applies the concept of unsupervised learning. The algorithms detect intrusions using a set of detection rules that are structured in the form of decision trees. The algorithms are described in details and their results on well-known dataset are introduced. An enhancement for the J48algorithm is also introduced, where the decision tree for the algorithm is changed to a binary tree. The change enhances the complexity to reach a decision. The chapter includes a brief introduction about the security in Mission critical systems and the reason behind securing such systems. It introduces different methodologies that were introduced to detect intrusions in wireless communications.

Publisher

IGI Global

Reference34 articles.

1. Ajenjo, A. D., & Wietgrefe, H. (2003). Minimal-Intrusion Traffic Monitoring And Analysis In Mission-Critical Communication Networks. Journal of Systemics, Cybernetics and Informatics, 1(5).

2. Efficacy of Hidden Markov Models Over Neural Networks in Anomaly Intrusion Detection

3. Alcatel-Lucent. (2013). Mission-critical Communications Networks for Public Safety Highly reliable converged IP/MPLS-based backhaul Application Note. Retrieved from http://www.tmcnet.com/tmc/whitepapers/documents/whitepapers/2013/9270-mission-critical-communications-networks-public-safet.pdf

4. Ambady, B. (2012). A Guide to Security Methods for Mission-Critical Communications Networks. Mission Critical Communications has more details about security options for utilities and smart-grid applications. Retrieved from http://www.radioresourcemag.com/Features/FeaturesDetails/FID/341

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