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Subramanian, NanditaAnalysis of Rank Distance for Malware Classification
MS, University of Cincinnati, 2016, Engineering and Applied Science: Computer Science
Malicious Cyber Adversaries may compromise the security of a system by denying access to legitimate users. This is often coupled with immeasurable loss of confidential data, which leads to hefty losses in both financial and trustworthiness aspects of a corporation. Malware exploits key vulnerabilities in applications presenting problems such as identity theft, unapproved software installations, etc. Abundance in malware detection and removal techniques in the ever evolving field of computers, presently exhibit a lower level of efficiency in detecting malicious softwares. Techniques available currently enable detection of softwares that are embedded with known signatures. No doubt these methods are efficient. However, most malware writers, aware of signature-based detection methods are working towards bypassing them. Machine learning based systems for malware classification and detection have been tested and proved to be more efficient than standard signature-based systems. A vital reason and justification providing a strong foothold for using machine learning techniques is that even unseen malware can be detected, thus eliminating malware detection failures and providing very high success rates. Our method uses efficient machine learning techniques for classification and detection of portable executable (PE) files of various malware classes commonly found in computers running Windows operating systems. For malicious files, computation of the distance between two files should yield an indication of their similarity. Using this as a basis, this thesis analyses the different approaches which can be employed for classifying malicious files using a method known as rank distance. This distance measure has been combined with a feature extraction method known as mutual information which analyses the opcodes n-gram sequences extracted from the PE files and segregates the most relevant opcodes from these. The most relevant opcodes, thus obtained, are used as features to identify which class a given file belongs to. An opcode relevance profile generated based on mutual information and the unclassified file are compared and assigned the respective rank distances for every class. Using these ranks, a distance between the two files is obtained. The class which has the least distance to the file is concluded to be the class of the file under scrutiny.

Committee:

Anca Ralescu, Ph.D. (Committee Chair); Chia Han, Ph.D. (Committee Member); Dan Ralescu, Ph.D. (Committee Member)

Subjects:

Computer Science

Keywords:

Rank Distance;Malware Classification;Mutual Information;Text Mining;Similarity Measures;Windows Malware

Syed, Tamseel MahmoodPrecoder Design Based on Mutual Information for Non-orthogonal Amplify and Forward Wireless Relay Networks
Master of Science in Engineering, University of Akron, 2014, Electrical Engineering
Cooperative relaying is a promising technique to enhance the reliability and data-rate of wireless networks. Among different cooperative relaying schemes, the half-duplex non-orthogonal amplify-and-forward (NAF) protocol is popular due to its low implementation complexity and performance advantages. This thesis investigates precoder design for a cooperative half-duplex single-relay NAF system from an information theoretic point of view using a novel mutual information-based design criterion. The first part of the thesis considers the design of a 2x2 precoder for the NAF half duplex single relay network in the presence of the direct link using mutual information (MI) as the main performance metric. Different from precoder design methods using pairwise error probability (PEP) analysis, which are valid only at high signal-to-noise ratios (SNR), the proposed precoder design can apply to any SNR region, which is of more interest from both information-theoretic and practical points of view. A MI-based criterion is developed for a cooperative frame length of 2, which corresponds to the case of using a 2x2 precoder. The design criterion is established in a closed-form, which can be helpful in finding an optimal precoder. Then it is analytically shown that a good precoder should have all entries that are equal in magnitude, which is different from the optimal precoders obtained thus far using the conventional PEP criterion. Simulation results indicate that the proposed class of precoders outperform the existing precoders in terms of the mutual information performance. The second part of the thesis extends the precoder design to an arbitrary block length of 2T. In general, for this case, a precoder of size 2Tx2T needs to be considered to optimize the MI performance. Similar to the 2 × 2 precoder design, a MI-based design criterion is first established. While the criterion can be expressed in closed form, the design of optimal precoders in this case is not possible, due to the complexity of the optimization problem. As an alternative, a novel grouping technique, which is referred to as symbol grouping, in which a group of only P = 2 information symbols are pre-coded to improve the MI, is proposed. It is then demonstrated that the grouping technique yields a much simpler design criterion and an optimal 2x2 precoder can be developed. Numerical results show that the proposed precoding technique outperforms existing precoder designs while keeping the receiver complexity at a minimum.

Committee:

Nghi H Tran, Dr. (Advisor); Alexis De Abreu Garcia, Dr. (Committee Member); Arjuna Madanayake, Dr. (Committee Member)

Subjects:

Computer Engineering; Design; Electrical Engineering; Engineering; Literature; Systems Design; Systems Science; Theoretical Mathematics

Keywords:

Cooperative Communication; wireless; network; reliability; data-rate; NAF; space-time code; non-orthogonal amplify and forward; information theory; mutual information; PEP; precoder; SNR; closed form; symbol grouping; receiver complexity; Tamseel Syed

Theeranaew, WanchatSTUDY ON INFORMATION THEORY: CONNECTION TO CONTROL THEORY, APPROACH AND ANALYSIS FOR COMPUTATION
Doctor of Philosophy, Case Western Reserve University, 2015, EECS - System and Control Engineering
This thesis consists of various studies in information theory, including its connection with control theory and the computational aspects of information measures. The first part of the research investigates the connection between control theory and information theory. This part extends previous results that mainly focused on this connection in the context of state estimation and feedback control. For linear systems, mutual information, along with the concepts of controllability and observability, is used to derive a tight connection between control theory and information theory. For nonlinear systems, a weaker statement of this connection is established. Some explicit calculations for linear systems and interesting observations about these calculations are presented. The second part investigates the computation of mutual information. An innovative method to compute the mutual information between two collections of time series data based on a Hidden Markov Model (HMM) is proposed. For continuous-valued data, a HMM with Gaussian emission is used to estimate the underlying dynamics of the original data. Mutual information is computed based on the approximate dynamics provided by the HMM. This work improves the estimation of the upper and lower bounds of entropy for Gaussian mixtures, which is one of the key components in this proposed method. This improvement of these bounds are shown to be robust compared to existing methods in all of the synthetic data experiments conducted. In addition, this research includes the study of the computation of Shannon mutual information in which the strong assumptions of independence and identical distribution (i.i.d.) are imposed. This research shows that even if this assumption is violated, the results process a meaningful interpretation. The study of the computation of Shannon mutual information for continuous-valued random variables is included in this research. Three coupled chaotic systems are used as exemplars to show that the computation of normalized mutual information is relatively insensitive to the number of quantized states, although quantization resolution does significantly affect the unnormalized mutual information. The same coupled chaotic systems are used to show that the quantization method also does not significantly affect the normalized mutual information. Simulations from these chaotic systems also show that normalized Shannon mutual information can be used to detect the different (fixed) coupling strengths between two subsystems. Two modified information measures, which enforce sensitivity to time permutation, are compared on these three systems. By using piecewise constant coupling and monotonically decaying coupling, the simulation results show that normalized mutual information can track time-varying changes in coupling strength for these chaos systems to a certain degree.

Committee:

Kenneth Loparo (Advisor); Vira Chankong (Committee Member); Marc Buchner (Committee Member); Richard Kolacinski (Committee Member)

Subjects:

Engineering; Mathematics

Keywords:

connection, Control Theory, Information theory, entropy, mutual information, computation, gaussian mixtures, hidden Markov model

Guo, YujunMedical Image Registration and Application to Atlas-Based Segmentation
PHD, Kent State University, 2007, College of Arts and Sciences / Department of Computer Science

A fundamental problem in medical image analysis is image registration, which is the task of finding geometric relationships between corresponding points in multiple images of the same scene. Various registration methods have been proposed over recent years, among which registration strategies based on maximization of mutual information have been widely used in multi-modality image registration.

However, applying mutual information (MI) to original intensities only takes statistical information into consideration, while spatial information is completely neglected. In the first part of this dissertation, a novel approach is proposed to incorporate spatial information into MI through gradient vector flow (GVF). With this approach, MI now is calculated from the GVF-intensity (GVFI) map of the original images instead of their intensity values. The algorithm is implemented and applied to multi-modality brain image registration to test the accuracy and robustness of the proposed method. Experimental results show that the success rate of our method is higher than that of traditional MI-based registration.

In many applications, a rigid transformation is insufficient to describe the spatial relationship between two images. Thus, elastic transformations, or non-rigid transformations are often required in image registration. In the second part of this dissertation, we present a generalized gradient-guided non-rigid registration strategy. The derivation procedure is similar to that by Lucas and Kanade, but in a more general manner. In experiments, we compare the proposed method and other gradient-guided methods in the literature, using both synthetic and real images. It is shown that methods combining gradients from both source and target images usually perform better.

In the third part, we apply previously described registration methods to atlas-based brain magnetic resonance (MR) image segmentation. A pre-labeled image or atlas is first registered to the subject image to be segmented, and the deformation field for each voxel is derived. Then the structures delineated in the atlas are projected onto the subject image by applying the deformation field to the atlas mask. We validate our results using the datasets from IBSR. Quantitative comparisons using various criteria show that the proposed method is better than or comparable to published methods.

Committee:

Cheng-Chang Lu (Advisor)

Keywords:

medical image registration; mutual information (MI); similarity measure; spatial information; gradient vector flow (GVF); gradient vector flow intensity; demons algorithm; non-rigid transformation; generalized gradient-guided non-rigid registration

Kuguoglu, Akin FahrettinFramework and Analysis of Rate one and Turbo Coded MIMO-CDMA Communication Systems
Doctor of Philosophy, University of Akron, 2006, Engineering-Applied Mathematics
In recent years, the demand for wireless communication systems with high data rates and improved link quality for a variety of applications has dramatically increased. To keep up with the demand, new concepts that could mitigate the channel impairments and optimally exploit the limited communication resources are necessary. One of such new concepts is the multiple input multiple output (MIMO) communication system, which has been found to be efficient in increasing the performance. To utilize the huge potential benefits of multiple antennas, it is necessary to develop new transceiver strategies. Hence, in this dissertation, we combine the concept of MIMO and code division multiple access (CDMA), and coding, into a robust communication system. We develop the analytical framework, architectures, algorithms and performance analyis. Both analytical and simulated results are presented. The performance of the MIMO-CDMA system is analyzed by taking into account advanced signal processing techniques, in the presence of mutual information coupling. The performance of the system is evaluated in terms of complexity, signal-to-noise ratio, bit error rate, outage probability and mutual information capacity.

Committee:

Okechukwu Ugweje (Advisor)

Keywords:

MIMO; CDMA; Space-Time code; Turbo Space-time code; BER; Mutual Information; Performance analysis

Li, JianchunDESIGN OF AN FPGA-BASED COMPUTING PLATFORM FOR REAL-TIME 3D MEDICAL IMAGING
Doctor of Philosophy, Case Western Reserve University, 2005, Computer Engineering
Real-time 3D medical imaging requires very high computational capability that is beyond most of the general computing platforms. Although application specific integrated circuits (ASIC) can provide solutions for a particular algorithm, they are too expensive to develop and most of them are not flexible enough to adapt to the evolution of existing algorithms or the emergence of new problems. FPGA-based reconfigurable architectures combined with general-purpose processors exhibit a good tradeoff in performance and flexibility, and are affordable for practical applications. To address the problems in designing such a system, including long designing and testing time, complex data manipulation and high performance requirement etc., we designed a new computing platform to accelerate a broad range of local operation-based 3D medical imaging algorithms. This platform is composed of a new data caching scheme, called brick caching scheme and a reconfigurable System-on-Chip (SoC) architecture targeted to Xilinx Virtex-II Pro FPGAs. The brick caching scheme exploits spatial locality of reference in three dimensions with 3D block caching; it enables data prefetching by obtaining input data block information through input-output space mapping; it also supports multiple data accesses with data duplication. An intelligent data caching system is built around a PowerPC processor core in the SoC architecture to support the brick caching scheme. A multiple pipeline execution unit that is reconfigurable to different algorithms is designed to perform vectorized computation. Two algorithms are implemented and tested on this platform, one is the FDK cone-beam CT reconstruction algorithm and the other is the mutual information-based 3D registration algorithm. Our simulation results demonstrate that a speed-up of about 30 can be achieved for both of the algorithms.

Committee:

Christos Papachristou (Advisor)

Keywords:

Medical imaging; FPGA; Computing platform; FDK reconstruction; mutual information; 3D registration

Castro Pareja, Carlos RaulReal-time 3D elastic image registration
Doctor of Philosophy, The Ohio State University, 2004, Electrical Engineering
Real-time elastic image registration is potentially an enabling technology for the effective and efficient use of many image-guided diagnostic and treatment procedures relying on multimodality image fusion or serial image comparison. Mutual information is currently the best-known image similarity measure for multimodality image registration. A well-known problem with elastic registration algorithms is their high computational cost, with common elastic registration times in the order of hours. This complexity is due both to the large number of image similarity calculations required to converge to the optimal transformation and the time required to calculate image similarity. This dissertation presents an algorithm for elastic image registration that is optimized to minimize the execution time, and a hardware architecture for algorithm acceleration. Novel features of the algorithm include the use of a priori information to limit the search space for possible transformations, linear bound-based grid folding prevention, and adaptive optimization algorithm tolerance. The hardware architecture accelerates mutual information calculation, which is a memory-intensive task that does not benefit from cache-based memory architecture in standard software implementations, but can be efficiently implemented in a pipeline using parallel memory access techniques. Its calculation is performed in two steps, namely mutual histogram calculation and entropy accumulation. The main focus of acceleration is on mutual histogram calculation, which corresponds to about 99% of the overall mutual information calculation time. The architecture employs parallel, independent access to the image and mutual histogram memories and includes a mutual histogram partitioning scheme that allows multiple parallel accesses to the mutual histogram memory. Entropy calculation and accumulation is performed using a novel variable segment size piecewise polynomial approximation implemented using look-up tables. A proof-of-concept implementation of the architecture achieved speedups of 30x for linear registration and 100x for elastic registration against a 3.2 GHz Pentium III Xeon workstation, achieving total elastic registration times in the order of minutes. The total speedup can be increased by using several modules in parallel, thus allowing real-time performance (in the order of seconds). The architecture presented in this dissertation will be a significant tool in enabling the use of elastic image registration outside of research environments.

Committee:

Jogikal Jagadeesh (Advisor)

Keywords:

Image registration; Mutual information; Digital systems; Entropy calculation; Image processing