BIOLOGICAL INSPIRED INTRUSION PREVENTION AND SELF-HEALING SYSTEM FOR CRITICAL SERVICES NETWORK

With the explosive development of the critical services network systems and Internet, the need for networks security systems have become even critical with the enlargement of information technology in everyday life. Intrusion Prevention System (IPS) provides an in-line mechanism focus on identifying and blocking malicious network activity in real time. This thesis presents new intrusion prevention and self-healing system (SH) for critical services network security. The design features of the proposed system are inspired by the human immune system, integrated with pattern recognition nonlinear classification algorithm and machine learning. Firstly, the current intrusions preventions systems, biological innate and adaptive immune systems, autonomic computing and self-healing mechanisms are studied and analyzed. The importance of intrusion prevention system recommends that artificial immune systems (AIS) should incorporate abstraction models from innate, adaptive immune system, pattern recognition, machine learning and self-healing mechanisms to present autonomous IPS system with fast and high accurate detection and prevention performance and survivability for critical services network system. Secondly, specification language, system design, mathematical and computational models for IPS and SH system are established, which are based upon nonlinear classification, prevention predictability trust, analysis, self-adaptation and self-healing algorithms. Finally, the validation of the system carried out by simulation tests, measuring, benchmarking and comparative studies. New benchmarking metrics for detection capabilities, prevention predictability trust and self-healing reliability are introduced as contributions for the IPS and SH system measuring and validation. Using the software system, design theories, AIS features, new nonlinear classification algorithm, and self-healing system show how the use of presented systems can ensure safety for critical services networks and heal the damage caused by intrusion. This autonomous system improves the performance of the current intrusion prevention system and carries on system continuity by using self-healing mechanism

Techniques and Emerging Trends for State of the Art Equipment Maintenance Systems - A Bibliometric Analysis

The increasing interconnection of machines in industrial production on one hand, and the improved capabilities to store, retrieve, and analyze large amounts of data on the other, offer promising perspectives for maintaining production machines. Recently, predictive maintenance has gained increasing attention in the context of equipment maintenance systems. As opposed to other approaches, predictive maintenance relies on machine behavior models, which offer several advantages. In this highly interdisciplinary field, there is a lack of a literature review of relevant research fields and realization techniques. To obtain a comprehensive overview on the state of the art, large data sets of relevant literature need to be considered and, best case, be automatically partitioned into relevant research fields. A proper methodology to obtain such an overview is the bibliometric analysis method. In the presented work, we apply a bibliometric analysis to the field of equipment maintenance systems. To be more precise, we analyzed clusters of identified literature with the goal to obtain deeper insight into the related research fields. Moreover, cluster metrics reveal the importance of a single paper and an investigation of the temporal cluster development indicates the evolution of research topics. In this context, we introduce a new measure to compare results from different time periods in an appropriate way. In turn, among others, this simplifies the analysis of topics, with a vast amount of subtopics. Altogether, the obtained results particularly provide a comprehensive overview of established techniques and emerging trends for equipment maintenance systems

New Approaches to Smart Grid Security with SCADA Systems

The use of information technology in electric power grid introduces the vulnerability problem looming the future smart grid. The supervisory control and data acquisition (SCADA)is the first defense, which itself is undermined by potential malicious attacks. This dissertation studies two particular security threats facing the smart grid and SCADA systems: the unobservable attack and the replay attack. The former is well known in fault detection of the power grid and has received renewed interest in the past a few years, while the latter is motivated by the Stuxnet worm allegedly used against the nuclear facilities in Iran. For unobservable attacks, this dissertation adopts the dynamic state estimation approach and treats each bus of the power grid as a dynamic agent. A consensus estimation strategy is proposed to estimate the dynamic states of the power grid, based on which unobservable attacks can be effectively detected. Detection of replay attacks is harder. Two different approaches are proposed in this dissertation. The first is the whitening filter approach that converts the detection of the replay attack into an equivalent white noise detection through whitening a feedback signal. However this approach is less effective, if the replay attack does not change much the whiteness of the filtered feedback signal. Hence a second approach termed as spectrum estimation is proposed. It is shown that the spectrum of the feedback signal in presence of the replay attack can be very different from the case when the replay attack is absent. This approach improves the detection results of the former one. Both are illustrated and examined by the simulation studies