Nonlinear optical effects manifested by electromagnetic induced transparency in cold atoms

This dissertation reports experimental studies of nonlinear optical effects manifested by electromagnetically induced transparency (EIT) in cold Rb atoms. The cold Rb atoms are confined in a magneto-optic trap (MOT) obtained with the standard laser cooling and trapping technique. Because of the near zero Doppler shift and a high phase density, the cold Rb sample is well suited for studies of atomic coherence and interference and related applications, and the experiments can be compared quantitatively with theoretical calculations. It is shown that with EIT induced in the multi-level Rb system by laser fields, the linear absorption is suppressed and the nonlinear susceptibility is enhanced, which enables studies of nonlinear optics in the cold atoms with slow photons and at low light intensities. Three independent experiments are described and the experimental results are presented. First, an experimental method that can produce simultaneously co-propagating slow and fast light pulses is discussed and the experimental demonstration is reported. Second, it is shown that in a three-level Rb system coupled by multi-color laser fields, the multi-channel two-photon Raman transitions can be manipulated by the relative phase and frequency of a control laser field. Third, a scheme for all-optical switching near single photon levels is developed. The scheme is based on the phase-dependent multi-photon interference in a coherently coupled four-level system. The phase dependent multi-photon interference is observed and switching of a single light pulse by a control pulse containing ∼20 photons is demonstrated. These experimental studies reveal new phenomena manifested by quantum coherence and interference in cold atoms, contribute to the advancement of fundamental quantum optics and nonlinear optics at ultra-low light intensities, and may lead to the development of new techniques to control quantum states of atoms and photons, which will be useful for applications in quantum measurements and quantum photonic devices.

Ensemble fuzzy belief intrusion detection design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Ensemble Fuzzy Belief Intrusion Detection Design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.