Welcome on the homepage of the chair "Internet Technologies and Systems" of Prof. Dr. Christoph Meinel and his team. We like to inform you about our teaching and ongoing research activities in security, knowledge engineering, innovation and design thinking research.
The chair of Prof. Dr. Christoph Meinel offers courses in the following disciplines: Internet and Web Technologies, (Discrete) Mathematics and Logic, IT Security and Internet Security, Complexity Theory and Information Security as well as Design Thinking.
In Security and Trust Engineering our research and development work is mainly focused on: Network & Internet Security, Cloud and SOA-Security (SOA - Service Oriented Architectures) and Security Awareness.
The research of the team of Prof. Dr. Christoph Meinel in the field of knowledge management and engineering focus on the challenging question, how to manage the mass of digital data, so-called "big data", from Internet and other sources in order to generate new knowledge.
Complete List of Conference Papers of the chair of Prof. Dr. Christoph Meinel
Here you can find all our peer-reviewed conference papers:
A Hybrid Machine Learning Method for Intrusion Detection
Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implementations. In this research work, we present a hybrid approach which is based on the “linear discernment analysis” and the “extreme learning machine” to build a tool for intrusion detection. In the proposed method, the linear discernment analysis is used to reduce the dimensions of data and the extreme learning machine neural network is used for data classification. This idea allowed us to benefit from the advantages of both methods. We implemented the proposed method on a microcomputer with core i5 1.6 GHz processor by using machine learning toolbox. In order to evaluate the performance of the proposed method, we run it on a comprehensive data set concerning intrusion detection. The data set is called KDD, which is a version of the data set DARPA presented by MIT Lincoln Labs. The experimental results were organized in related tables and charts. Analysis of the results show meaningful improvements in intrusion detection. In general, compared to the existing methods, the proposed approach works faster with higher accuracy.