WorldCat Identities

Mississippi State University Department of Electrical and Computer Engineering

Overview
Works: 54 works in 56 publications in 1 language and 58 library holdings
Genres: Academic theses 
Classifications: HE5614.3.M7,
Publication Timeline
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Most widely held works by Mississippi State University
Automated accident detection at intersections by Yunlong Zhang( Book )

2 editions published in 2004 in English and held by 3 WorldCat member libraries worldwide

This research aims to provide a timely and accurate accident detection method at intersections, which is very important for the Traffic Management System(TMS). This research uses acoustic signals to detect accident at intersections. A system is constructed that can be operated in two modes: two-class and multiclass. The input to the system is a three-second segment of audio signal. The output of the two-class mode is a label of "crash" or "non-crash". In the multi-class mode of operation, the system identifies crashes as well as several types of non-crash incidents, including normal traffic and construction sounds. The system is composed of three main signal processing stages: feature extraction, feature reduction, and feature classification. Five methods of feature extraction are investigated and compared; these are based on the discrete wavelet transform, fast Fourier transform, discrete cosine transform, real cepstral transform, and mel frequency cepstral transform. Statistical methods are used for feature optimization and classification. Three types of classifiers are investigated and compared: the nearest mean, maximum likelihood, and nearest neighbor methods. This study focuses on the detection algorithm development. Lab testing of the algorithm showed that the selected algorithm can detect intersection accidents with very high accuracy
Target discrimination/classification radar by B. Jeffrey Skinner( Book )

1 edition published in 1995 in English and held by 1 WorldCat member library worldwide

Stochastic and robust optimal operation of energy-efficient building with combined heat and power systems by Ping Liu( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

Energy efficiency and renewable energy become more attractive in smart grid. In order to efficiently reduce global energy usage in building energy systems and to improve local environmental sustainability, it is essential to optimize the operation and the performance of combined heat and power (CHP) systems. In addition, intermittent renewable energy and imprecisely predicted customer loads have introduced great challenges in energy-efficient buildings' optimal operation. In the deterministic optimal operation, we study the modeling of components in building energy systems, including the power grid interface, CHP and boiler units, energy storage devices, and appliances. The mixed energy resources are applied to collaboratively supply both electric and thermal loads. The results show that CHP can effectively improve overall energy efficiency by coordinating electric and thermal power supplies. Through the optimal operation of all power sources, the daily operation cost of building energy system for generating energy can be significantly reduced. In order to address the risk due to energy consumption and renewable energy production volatility, we conduct studies on both stochastic programming and robust optimizations to operate energy-efficient building systems under uncertainty. The multistage stochastic programming model is introduced so that the reliable operation of building energy systems would be probabilistically guaranteed with stochastic decisions. The simulation results show that the stochastic operation of building systems is a promising strategy to account for the impact of uncertainties on power dispatch decisions of energy-efficient buildings. In order to provide absolute guarantee for the reliable operation of building energy systems, a robust energy supply to electric and thermal loads is studied by exploring the influence of energy storage on energy supply and accounting for uncertainties in the energy-efficient building. The robustness can be adjusted to control the conservativeness of the proposed robust operation model. For the purpose of achieving adaptability in the robust optimal operation and attaining robustness in the stochastic optimal operation of building energy systems, we also develop an innovative robust stochastic optimization (RSO) model. The proposed RSO model not only overcomes the conservativeness in the robust operation model, but also circumvents the curse of dimensionality in the stochastic operation model
Spectral band selection for ensemble classification of hyperspectral images with applications to agriculture and food safety by Sathishkumar Samiappan( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

In this dissertation, an ensemble non-uniform spectral feature selection and a kernel density decision fusion framework are proposed for the classification of hyperspectral data using a support vector machine classifier. Hyperspectral data has more number of bands and they are always highly correlated. To utilize the complete potential, a feature selection step is necessary. In an ensemble situation, there are mainly two challenges: (1) Creating diverse set of classifiers in order to achieve a higher classification accuracy when compared to a single classifier. This can either be achieved by having different classifiers or by having different subsets of features for each classifier in the ensemble. (2) Designing a robust decision fusion stage to fully utilize the decision produced by individual classifiers. This dissertation tests the efficacy of the proposed approach to classify hyperspectral data from different applications. Since these datasets have a small number of training samples with larger number of highly correlated features, conventional feature selection approaches such as random feature selection cannot utilize the variability in the correlation level between bands to achieve diverse subsets for classification. In contrast, the approach proposed in this dissertation utilizes the variability in the correlation between bands by dividing the spectrum into groups and selecting bands from each group according to its size. The intelligent decision fusion proposed in this approach uses the probability density of training classes to produce a final class label. The experimental results demonstrate the validity of the proposed framework that results in improvements in the overall, user, and producer accuracies compared to other state-of-the-art techniques. The experiments demonstrate the ability of the proposed approach to produce more diverse feature selection over conventional approaches
Transmission shift map optimization for reduced electrical energy consumption in a pre-transmission parallel plug-in hybrid electric vehicle by Jonathan Dean Moore( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

The use of an automatic transmission in pre-transmission parallel hybrid electric vehicles provides greater potential for powertrain optimization than conventional vehicles. By modifying the shift map, the transmission's gear selection can be adjusted to reduce the energy consumption of the vehicle. A method for determining the optimal shift map for this hybrid vehicle has been implemented using global optimization and software-in-the-loop vehicle simulation. An analysis of the optimization has been performed using software-in-the-loop and hardware-in-the-loop simulation and evaluates two vehicle modes: regenerative braking active and regenerative braking disabled. The results of these two modes illustrate the successful implementation of the global optimization algorithm. However, the evaluation results raise practical concerns about implementing the optimized shift maps in a vehicle and illustrate a problem which must be overcome for future development
Bandwidth based methodology for designing a hybrid energy storage system for a series hybrid electric vehicle with limited all electric mode by Masood Shahverdi( )

1 edition published in 2015 in English and held by 1 WorldCat member library worldwide

The cost and fuel economy of hybrid electrical vehicles (HEVs) are significantly dependent on the power-train energy storage system (ESS). A series HEV with a minimal all-electric mode (AEM) permits minimizing the size and cost of the ESS. This manuscript, pursuing the minimal size tactic, introduces a bandwidth based methodology for designing an efficient ESS. First, for a mid-size reference vehicle, a parametric study is carried out over various minimal-size ESSs, both hybrid (HESS) and non-hybrid (ESS), for finding the highest fuel economy. The results show that a specific type of high power battery with 4.5 kWh capacity can be selected as the winning candidate to study for further minimization. In a second study, following the twin goals of maximizing Fuel Economy (FE) and improving consumer acceptance, a sports car class Series-HEV (SHEV) was considered as a potential application which requires even more ESS minimization. The challenge with this vehicle is to reduce the ESS size compared to 4.5 kWh, because the available space allocation is only one fourth of the allowed battery size in the mid-size study by volume. Therefore, an advanced bandwidth-based controller is developed that allows a hybridized Subaru BRZ model to be realized with a light ESS. The result allows a SHEV to be realized with 1.13 kWh ESS capacity. In a third study, the objective is to find optimum SHEV designs with minimal AEM assumption which cover the design space between the fuel economies in the mid-size car study and the sports car study. Maximizing FE while minimizing ESS cost is more aligned with customer acceptance in the current state of market. The techniques applied to manage the power flow between energy sources of the power-train significantly affect the results of this optimization. A Pareto Frontier, including ESS cost and FE, for a SHEV with limited AEM, is introduced using an advanced bandwidth-based control strategy teamed up with duty ratio control. This controller allows the series hybrid’s advantage of tightly managing engine efficiency to be extended to lighter ESS, as compared to the size of the ESS in available products in the market
Meta-heuristic optimization of antennas for biomedical applications by Aaron Zachary Hood( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

Given the proper conditions, antennas applied in medicine can offer improved quality of life to patients. However the human body proves hostile to typical, analytical antenna design techniques as it is composed entirely of frequency- and temperature-dependent lossy media. By combining optimization techniques with numerical methods, many of these challenges may be overcome. Particle swarm optimization (PSO) models the solution process after the natural movement of groups such as swarms of bees as they search for food sources. This meta-heuristic procedure has proven adept at overcoming many challenging problems in the electromagnetics literature. Therefore, this dissertation explores PSO and some of its variants in the solution of two biomedical antenna problems. Recent advances in biosensor technology have led to miniaturized devices that are suitable for in vivo operation. While these sensors hold great promise for medical treatment, they demand a wireless installation for maximum patient benefit, which in turn demands quite specific antenna requirements. The antennas must be composed of biocompatible materials, and must be very small (no more than a few square centimeters) to minimize invasiveness. Here PSO is applied to design a 22.5 mm x 22.5 mm x 2.5 mm implantable serpentine planar inverted-F antenna for dual-band MedRadio and ISM operation. Measurements reveal the accuracy of the models. Hyperthermia is the process of elevating a patient’s temperature for therapeutic gain. Since the ancient Egyptians, physicians have employed hyperthermia in the destruction of cancerous tumors. Modern implementations typically apply electromagnetic radiation at radio and microwave frequencies to induce local or regional heating. In this dissertation PSO is used to evaluate candidate antennas for inclusion in an array of antennas with the aim of local adjuvant hyperthermia for breast cancer treatment. The near-field of the array is then optimized to induce a uniform specific absorption rate throughout the breast
Ultra-wideband microwave ablation applicators by Mustafa Asili( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

The increasing demand for efficient cancer treatment inspired the researchers for new investigations about an alternative treatment of cancer. Microwave ablation is the newest ablation technique to cure cancer. This method is minimally noninvasive and inexpensive compared to the other methods. However, current microwave ablation systems suffer due to narrowband nature of the antenna (dipole or slot) placed at the tip of the probe. Therefore, this study developed an ultra-wideband ablation probe that operates from 300MHz to 10 GHz. For this purpose, a small wideband antenna is designed to place at the tip of the probe and fabricated. These probes are tested at ISM frequencies (2.4 GHz and 5.8GHz) in skin mimicking gels and pig liver. Microwave ablation probe design, simulation results, and experiment results are provided in this thesis
Electrical properties degradation of Photovoltaic modules caused by lightning induced voltage by Taosha Jiang( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

Lightning is one of the main factors that cause Photovoltaic (PV) systems to fail. The PV modules inside PV systems, like any other electric equipment, will be degraded under electrical stress. The effect of electrical degradation of the PV modules caused by lightning induced voltage has been rarely reported. In the dissertation, the electrical properties degradation of a polycrystalline silicon module was studied. Firstly, lightning impulse voltages of positive polarity ranging from low to high are applied on different groups of the testing modules. All these lightning impulse voltage tests are conducted in the same experimental condition except for their stress voltage magnitudes. The maximum power output, I-V characteristics, and dark forward I-V curve are measured and reported periodically during the lightning impulse voltage tests. By comparing the maximum output power and changes in the internal electrical properties, it could be concluded that lightning impulse voltages, even medium voltage levels, will cause degradation to the sample. The relationship of the maximum output power and the number of applied impulses for different testing voltage levels are compared. An analysis of the electrical property changes caused by the lightning impulse voltages is presented. Secondly, a group of samples are tested with lightning impulse voltage of negative polarity. A comparison of the impulse voltage aging effects at the same voltage level with positive polarity is made. The maximum power output drop caused by positive and negative lightning impulses are compared. Laboratory results revealed that positive and negative lightning impulses will not only influence the degree of degradation, but also lead to different electrical property changes. Finally, a comparison of the effect of lightning impulses combined with other stress factors are discussed. The study simulates a field-aged sample's behavior at lightning impulse voltage testing conditions. The result suggests that the degradation caused by lightning impulse voltage is greatly accelerated when the sample has bubbles and delamination. Electrical breakdown of the module is caused by the failure of the insulation
Secure cloud computing for solving large-scale linear systems of equations by Xuhui Chen( )

1 edition published in 2015 in English and held by 1 WorldCat member library worldwide

Solving large-scale linear systems of equations (LSEs) is one of the most common and fundamental problems in big data. But such problems are often too expensive to solve for resource-limited users. Cloud computing has been proposed as an efficient and cost effective way of solving such tasks. Nevertheless, one critical concern in cloud computing is data privacy. Many previous works on secure outsourcing of LSEs have high computational complexity and share a common serious problem, i.e., a huge number of external memory I/O operations, which may render those outsourcing schemes impractical. We develop a practical secure outsourcing algorithm for solving large-scale LSEs, which has both low computational complexity and low memory I/O complexity and can protect clients privacy well. We implement our algorithm on a real-world cloud server and a laptop. We find that the proposed algorithm offers significant time savings for the client (up to 65%) compared to previous algorithms
Model-based autonomic performance management of distributed enterprise systems and applications by Rajat Mehrotra( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

Distributed computing systems (DCS) host a wide variety of enterprise applications in dynamic and uncertain operating environments. These applications require stringent reliability, availability, and quality of service (QoS) guarantee to maintain their service level agreements (SLAs). Due to the growing size and complexity of DCS, an autonomic performance management system is required to maintain SLAs of these applications. A model-based autonomic performance management structure is developed in this dissertation for applications hosted in DCS. A systematic application performance modeling approach is introduced in this dissertation to define the dependency relationships among the system parameters, which impact the application performance. The developed application performance model is used by a model-based predictive controller for managing multi-dimensional QoS objectives of the application. A distributed control structure is also developed to provide scalability for performance management and to eliminate the requirement of approximate behavior modeling in the hierarchical arrangement of DCS. A distributed monitoring system is also introduced in this dissertation to keep track of computational resources utilization, application performance statistics, and scientific application execution in a DCS, with minimum latency and controllable resource overhead. The developed monitoring system is self-configuring, self-aware, and fault-tolerant. It can also be deployed for monitoring of DCS with heterogeneous computing systems. A configurable autonomic performance management system is developed using model-integrated computing methodologies, which allow administrators to define the initial settings of the application, QoS objectives, system components' placement, and interaction among these components in a graphical domain specific modeling environment. This configurable performance management system facilitates reusability of the same components, algorithms, and application performance models in different deployment settings
Insights and characterization of l1-norm based sparsity learning of a lexicographically encoded capacity vector for the Choquet Integral by Titilope Adeola Adeyeba( )

1 edition published in 2015 in English and held by 1 WorldCat member library worldwide

This thesis aims to simultaneously minimize function error and model complexity for data fusion via the Choquet integral (CI). The CI is a generator function, i.e., it is parametric and yields a wealth of aggregation operators based on the specifics of the underlying fuzzy measure. It is often the case that we desire to learn a fusion from data and the goal is to have the smallest possible sum of squared error between the trained model and a set of labels. However, we also desire to learn as “simple’’ of solutions as possible. Herein, L1-norm regularization of a lexicographically encoded capacity vector relative to the CI is explored. The impact of regularization is explored in terms of what capacities and aggregation operators it induces under different common and extreme scenarios. Synthetic experiments are provided in order to illustrate the propositions and concepts put forth
Cybersecurity testing and intrusion detection for cyber-physical power systems by Shengyi Pan( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

Power systems will increasingly rely on synchrophasor systems for reliable and high-performance wide area monitoring and control (WAMC). Synchrophasor systems greatly use information communication technologies (ICT) for data exchange which are vulnerable to cyber-attacks. Prior to installation of a synchrophasor system a set of cyber security requirements must be developed and new devices must undergo vulnerability testing to ensure that proper security controls are in place to protect the synchrophasor system from unauthorized access. This dissertation describes vulnerability analysis and testing performed on synchrophasor system components. Two network fuzzing frameworks are proposed; for the IEEE C37.118 protocol and for an energy management system (EMS). While fixing the identified vulnerabilities in information infrastructures is imperative to secure a power system, it is likely that successful intrusions will still occur. The ability to detect intrusions is necessary to mitigate the negative effects from a successful attacks. The emergence of synchrophasor systems provides real-time data with millisecond precision which makes the observation of a sequence of fast events feasible. Different power system scenarios present different patterns in the observed fast event sequences. This dissertation proposes a data mining approach called mining common paths to accurately extract patterns for power system scenarios including disturbances, control and protection actions and cyber-attacks from synchrophasor data and logs of system components. In this dissertation, such a pattern is called a common path, which is represented as a sequence of critical system states in temporal order. The process of automatically discovering common paths and building a state machine for detecting power system scenarios and attacks is introduced. The classification results show that the proposed approach can accurately detect these scenarios even with variation in fault locations and load conditions. This dissertation also describes a hybrid intrusion detection framework that employs the mining common path algorithm to enable a systematic and automatic IDS construction process. An IDS prototype was validated on a 2-line 3-bus power transmission system protected by the distance protection scheme. The result shows the IDS prototype accurately classifies 25 power system scenarios including disturbances, normal control operations, and cyber-attacks
Energy cost optimization for strongly stable multi-hop green cellular networks by Weixian Liao( )

1 edition published in 2015 in English and held by 1 WorldCat member library worldwide

Last decade witnessed the explosive growth in mobile devices and their traffic demand, and hence the significant increase in the energy cost of the cellular service providers. One major component of energy expenditure comes from the operation of base stations. How to reduce energy cost of base stations while satisfying users’ soaring demands has become an imperative yet challenging problem. In this dissertation, we investigate the minimization of the long-term time-averaged expected energy cost while guaranteeing network strong stability. Specifically, considering flow routing, link scheduling, and energy constraints, we formulate a time-coupling stochastic Mixed-Integer Non-Linear Programming (MINLP) problem, which is prohibitively expensive to solve. We reformulate the problem by employing Lyapunov optimization theory and develop a decomposition based algorithm which ensures network strong stability. We obtain the bounds on the optimal result of the original problem and demonstrate the tightness of the bounds and the efficacy of the proposed scheme
Leveraging PLC ladder logic for signature based IDS rule generation by Drew Jackson Richey( )

1 edition published in 2016 in English and held by 1 WorldCat member library worldwide

Industrial Control Systems (ICS) play a critical part in our world’s economy, supply chain and critical infrastructure. Securing the various types of ICS is of the utmost importance and has been a focus of much research for the last several years. At the heart of many defense in depth strategies is the signature based intrusion detection system (IDS). The signatures that define an IDS determine the effectiveness of the system. Existing methods for IDS signature creation do not leverage the information contained within the PLC ladder logic file. The ladder logic file is a rich source of information about the PLC control system. This thesis describes a method for parsing PLC ladder logic to extract address register information, data types and usage that can be used to better define the normal operation of the control system which will allow for rules to be created to detect abnormal activity
Automatic K-Expectation-Maximization (K-EM) clustering algorithm for data mining applications by Archit Harsh( )

1 edition published in 2016 in English and held by 1 WorldCat member library worldwide

A non-parametric data clustering technique for achieving efficient data-clustering and improving the number of clusters is presented in this thesis. K-Means and Expectation-Maximization algorithms have been widely deployed in data-clustering applications. Result findings in related works revealed that both these algorithms have been found to be characterized with shortcomings. K-Means was established not to guarantee convergence and the choice of clusters heavily influenced the results. Expectation-Maximization’s premature convergence does not assure the optimality of results and as with K-Means, the choice of clusters influence the results. To overcome the shortcomings, a fast automatic K-EM algorithm is developed that provide optimal number of clusters by employing various internal cluster validity metrics, providing efficient and unbiased results. The algorithm is implemented on a wide array of data sets to ensure the accuracy of the results and efficiency of the algorithm
The multi-stress aging of 15 kV EPR power cables by Linfeng Cao( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

This research is focused on the multi-stress aging phenomena and lifetime estimation of 15 kV EPR cable. In order to gain the suitable parameters for the lifetime estimation, the aging study on the EPR cable samples as well as on the cable layers' dielectrics samples was carried out at the High Voltage Laboratory of Mississippi State University. During the multi-stress aging study of 15 kV EPR cable samples, the EPR cable samples underwent electrical stress, thermal stress, and environmental effects. The aging time for the EPR cables varied from 650 hrs to 1300 hrs. An empirical aging model describing the cables' lifetime was derived from the partial discharge measurements results. The aging study on the EPR cable layers' dielectrics was achieved as well. The EPR insulation material samples were aged by combined electrical and thermal stress, while the material samples of inner semi-conducting layer, outer semi-conducting layer, and outer low-density polyethylene (LDPE) jacket were aged by thermal stress. The measurement data was used for the newly proposed lifetime estimation method. A new lifetime estimation method was introduced for the EPR cables. The method assumed that the failures of cables results from the expansion of voids/cavities initiated from the defects in the EPR insulation layer. The proposed lifetime estimation method applied the finite element method (FEM) to solve the electric field distribution inside the EPR cable with the existence of voids/cavities. The parameters were derived from the aging study on the EPR insulation material samples. Assuming the voids/cavities would expand in the direction of the maximum electric field stress, the lifetime of the EPR cables was then estimated through the iteration. The introduced method helped to establish a relationship between the aging study of insulation material samples and the aging of EPR cable samples, which was long missing in the past studies. It also provided a new way to assess the reliability of the EPR cable
Earthen levee slide detection via automated analysis of synthetic aperture radar imagery by Lalitha Dabbiru( )

1 edition published in 2015 in English and held by 1 WorldCat member library worldwide

The main focus of this research is to detect vulnerabilities on the Mississippi river levees using remotely sensed Synthetic Aperture Radar (SAR) imagery. Unstable slope conditions can lead to slump slides, which weaken the levees and increase the likelihood of failure during floods. On-site inspection of levees is expensive and time-consuming, so there is a need to develop efficient automated techniques based on remote sensing technologies to identify levees that are more vulnerable to failure under flood loading. Synthetic Aperture Radar technology, due to its high spatial resolution and potential soil penetration capability, is a good choice to identify problem areas along the levee so that they can be treated to avoid possible catastrophic failure. This research analyzes the ability of detecting the slump slides on the levee with different frequency bands of SAR data. The two SAR datasets used in this study are: (1) the L-band airborne radar data from NASA JPL’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), and (2) the X-band satellite-based radar data from DLR’s TerraSAR-X (TSX). The main contribution of this research is the development of a machine learning framework to 1) provide improved knowledge of the status of the levees, 2) detect anomalies on the levee sections, and 3) provide early warning of impending levee failures. Polarimetric and textural features have been computed and utilized in the classification tasks to achieve efficient levee characterization. Various approaches of image analysis methods for characterizing levee segments within the study area have been implemented and tested. The RX anomaly detector, a training-free unsupervised classification algorithm, detected the active slump slides on the levee at the time of image acquisition and also flagged some areas as “anomalous”, where new slides appeared at a later date. This technique is very fast and does not depend on ground truth information, so these results guide levee managers to investigate the areas shown as anomalies in the classification map. The support vector machine (SVM) supervised learning algorithm with grey level co-occurrence matrix (GLCM) features provided excellent results in identifying slump slides on the levee
Low rank and sparse representation for hyperspectral imagery analysis by Alex Hendro Sumarsono( )

1 edition published in 2015 in English and held by 1 WorldCat member library worldwide

This dissertation develops new techniques employing the Low-rank and Sparse Representation approaches to improve the performance of state-of-the-art algorithms in hyperspectral image analysis. The contributions of this dissertation are outlined as follows. 1) Low-rank and sparse representation approaches, i.e., low-rank representation (LRR) and low-rank subspace representation (LRSR), are proposed for hyperspectral image analysis, including target and anomaly detection, estimation of the number of signal subspaces, supervised and unsupervised classification. 2) In supervised target and unsupervised anomaly detection, the performance can be improved by using the LRR sparse matrix. To further increase detection accuracy, data is partitioned into several highly-correlated groups. Target detection is performed in each group, and the final result is generated from the fusion of the output of each detector. 3) In the estimation of the number of signal subspaces, the LRSR low-rank matrix is used in conjunction with direct rank calculation and soft-thresholding. Compared to the state-of-the-art algorithms, the LRSR approach delivers the most accurate and consistent results across different datasets. 4) In supervised and unsupervised classification, the use of LRR and LRSR low-rank matrices can improve classification accuracy where the improvement of the latter is more significant. The investigation on state-of-the-art classifiers demonstrate that, as a pre-preprocessing step, the LRR and LRSR produce low-rank matrices with fewer outliers or trivial spectral variations, thereby enhancing class separability
Toward autonomic security for industrial control systems by Madhulika Trivedi( )

1 edition published in 2015 in English and held by 1 WorldCat member library worldwide

Supervisory control and data acquisition systems are extensively used in the critical infrastructure domain for controlling and managing large-scale industrial applications. This thesis presents a security management structure developed to protect ICS networks from security intrusions. This structure is formed by a combination of several modules for monitoring system-utilization parameters, data processing, detection of known attacks, forensic analysis to support against unknown attacks, estimation of control system-specific variables, and launch of appropriate protection methods. The best protection method to launch in case of an attack is chosen by a multi-criteria analysis controller based on operational costs and efficiency. A time-series ARIMA model is utilized to estimate the future state of the system and to protect it against cyber intrusions. Signature and performance based detection techniques assist in real-time identification of attacks with little or no human intervention. Simulation results for Scanning, Denial of Service and Injection attacks are provided
 
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Alternative Names

controlled identityMississippi State University

controlled identityMississippi State University. Department of Electrical Engineering

Mississippi State University. Dept. of Electrical and Computer Engineering

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English (24)