The technique of sensor fusion addresses the issues relating to the optimality of decision-making in the multiple-sensor framework. The advances in sensor fusion enable to perform intrusion detection for both rare and new attacks. This thesis discusses this assertion in detail, and describes the theoretical and experimental work done to show its validity. The attack-detector relationship is initially modeled and validated to understand the detection scenario. The different metrics available for the evaluation of intrusion detection systems are also introduced. The usefulness of the data set used for experimental evaluation has been demonstrated. The issues connected with intrusion detection systems are analyzed and the need for incorporating multiple detectors and their fusion is established in this work. Sensor fusion provides advantages with respect to reliability and completeness, in addition to intuitive and meaningful results. The goal for this work is to investigate how to combine data from diverse intrusion detection systems in order to improve the detection rate and reduce the false-alarm rate. The primary objective of the proposed thesis work is to develop a theoretical and practical basis for enhancing the performance of intrusion detection systems using advances in sensor fusion with easily available intrusion detection systems. This thesis introduces the mathematical basis for sensor fusion in order to provide enough support for the acceptability of sensor fusion in performance enhancement of intrusion detection systems. The thesis also shows the practical feasibility of performance enhancement using advances in sensor fusion and discusses various sensor fusion algorithms, its characteristics and related design and implementation is-sues. We show that it is possible to build performance enhancement to intrusion detection systems by setting proper threshold bounds and also by rule-based fusion. We introduce an architecture called the data-dependent decision fusion as a framework for building intrusion detection systems using sensor fusion based on data-dependency. Furthermore, we provide information about the types of data, the data skewness problems and the most effective algorithm in detecting different types of attacks. This thesis also proposes and incorporates a modified evidence theory for the fusion unit, which performs very well for the intrusion detection application. The future improvements in individual IDSs can also be easily incorporated in this technique in order to obtain better detection capabilities. Experimental evaluation shows that the proposed methods have the capability of detecting a significant percentage of rare and new attacks. The improved performance of the IDS using the algorithms that has been developed in this thesis, if deployed fully would contribute to an enormous reduction of the successful attacks over a period of time. This has been demonstrated in the thesis and is a right step towards making the cyber space safer.

Emerging low power, embedded, wireless sensor devices are useful for wide range of applications, yet have very limited processing storage and especially energy resources. Sensor networks have a wide variety of applications in medical monitoring, environmental sensing and military surveillance. Due to the large number of sensor nodes that may be deployed and the required long system lifetimes, replacing the battery is not an option. Sensor systems must utilize the minimal possible energy while operating over a wide range of operating scenarios. The most of the efforts in the energy management in sensor networks have concentrated on minimizing energy consumption in the communication subsystem. Some researchers have also dealt with the issue of minimizing the energy in computing subsystem of a sensor network node. Some proposals using energy aware software have also been made. Relatively little work has been done on compiler controlled energy management in sensor networks. In this thesis, we present our investigations on how compiler techniques can be used to minimize CPU energy consumption in sensor network nodes. One effectively used energy management technique in general purpose processors, is dynamic voltage scaling. In this thesis we implement and evaluate a compiler assisted DVS algorithm and show its usefulness for a small sensor node processor. We were able to achieve an energy saving of 29% with a little performance slowdown. Scratchpad memories have been widely used for improving performance. In this thesis we show that if the scratchpad size for the system is chosen carefully, then large energy savings can be achieved by using a compiler assisted scratchpad allocation policy. With a small size of 512 byte scratchpad memory we were able to achieve 50% of energy savings. We also studied the behavior of dynamic voltage scaling in presence of scratchpad memory. Our results show that in presence of scratchpad memory less opportunities are found for applying dynamic voltage scaling techniques. The sensor network community lacks a comprehensive benchmark suite, for our study we also implemented a set of applications, representative of computational workload on sensor network nodes. The techniques studied in this thesis can easily be integrated with existing energy management techniques in sensor networks, yielding in additional energy savings.