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Title: Data mining heuristic-¬based malware detection for android applications.
Creator: Peiravian, Naser, Zhu, Xingquan, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The Google Android mobile phone platform is one of the dominant smartphone operating systems on the market. The open source Android platform allows developers to take full advantage of the mobile operation system, but also raises significant issues related to malicious applications (Apps). The popularity of Android platform draws attention of many developers which also attracts the attention of cybercriminals to develop different kinds of malware to be inserted into the Google Android Market...
Show moreThe Google Android mobile phone platform is one of the dominant smartphone operating systems on the market. The open source Android platform allows developers to take full advantage of the mobile operation system, but also raises significant issues related to malicious applications (Apps). The popularity of Android platform draws attention of many developers which also attracts the attention of cybercriminals to develop different kinds of malware to be inserted into the Google Android Market or other third party markets as safe applications. In this thesis, we propose to combine permission, API (Application Program Interface) calls and function calls to build a Heuristic-Based framework for the detection of malicious Android Apps. In our design, the permission is extracted from each App’s profile information and the APIs are extracted from the packed App file by using packages and classes to represent API calls. By using permissions, API calls and function calls as features to characterize each of Apps, we can develop a classifier by data mining techniques to identify whether an App is potentially malicious or not. An inherent advantage of our method is that it does not need to involve any dynamic tracking of the system calls but only uses simple static analysis to find system functions from each App. In addition, Our Method can be generalized to all mobile applications due to the fact that APIs and function calls are always present for mobile Apps. Experiments on real-world Apps with more than 1200 malwares and 1200 benign samples validate the algorithm performance. Research paper published based on the work reported in this thesis: Naser Peiravian, Xingquan Zhu, Machine Learning for Android Malware Detection Using Permission and API Calls, in Proc. of the 25th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) – Washington D.C, November 4-6, 2013.
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Date Issued: 2013
PURL: http://purl.flvc.org/fau/fd/FA0004045
Subject Headings: Computer networks -- Security measures, Data encryption (Computer science), Data structures (Computer science), Internet -- Security measures
Format: Document (PDF)

Title: Neural network approach to Bayesian background modeling for video object segmentation.
Creator: Culibrk, Dubravko., Florida Atlantic University, Furht, Borko, Marques, Oge, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Object segmentation in a video sequence is an essential task in video processing and forms the foundation of content analysis, scene understanding, object-based video encoding (e.g. MPEG-4), various surveillance and 2D-to-pseudo-3D conversion applications. Popularization and availability of video sequences with increased spatial resolution requires development of new, more efficient algorithms for object detection and segmentation. This dissertation discusses a novel neural-network-based...
Show moreObject segmentation in a video sequence is an essential task in video processing and forms the foundation of content analysis, scene understanding, object-based video encoding (e.g. MPEG-4), various surveillance and 2D-to-pseudo-3D conversion applications. Popularization and availability of video sequences with increased spatial resolution requires development of new, more efficient algorithms for object detection and segmentation. This dissertation discusses a novel neural-network-based approach to background modeling for motion-based object segmentation in video sequences. In particular, we show how Probabilistic Neural Network (PNN) architecture can be extended to form an unsupervised Bayesian classifier for the domain of video object segmentation. The constructed Background Modeling Neural Network (BNN) is capable of efficiently handling segmentation in natural-scene sequences with complex background motion and changes in illumination. The weights of the proposed neural network serve as an exclusive model of the background and are temporally updated to reflect the observed background statistics. The proposed approach is designed to enable an efficient, highly-parallelized hardware implementation. Such a system would be able to achieve real-time segmentation of high-resolution image sequences.
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Date Issued: 2006
PURL: http://purl.flvc.org/fcla/dt/12214
Subject Headings: Neural networks (Computer science), Application software--Development, Data structures (Computer science), Bayesian field theory
Format: Document (PDF)

Title: Universal physical access control system (UPACS).
Creator: Carryl, Clyde, Alhalabi, Bassem A., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This research addresses the need for increased interoperability between the varied access control systems in use today, and for a secure means of providing access to remote physical devices over untrusted networks. The Universal Physical Access Control System (UPACS) is an encryption-enabled security protocol that provides a standard customizable device control mechanism that can be used to control the behavior of a wide variety of physical devices, and provide users the ability to securely...
Show moreThis research addresses the need for increased interoperability between the varied access control systems in use today, and for a secure means of providing access to remote physical devices over untrusted networks. The Universal Physical Access Control System (UPACS) is an encryption-enabled security protocol that provides a standard customizable device control mechanism that can be used to control the behavior of a wide variety of physical devices, and provide users the ability to securely access those physical devices over untrusted networks.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004354, http://purl.flvc.org/fau/fd/FA00004354
Subject Headings: Body area networks (Electronics), Computational complexity, Computer network protocols, Computer security, Cryptography, Data encryption (Computer science), Data structures (Computer science), Telecommunication -- Security measures
Format: Document (PDF)

Title: Evolutionary Methods for Mining Data with Class Imbalance.
Creator: Drown, Dennis J., Khoshgoftaar, Taghi M., Florida Atlantic University
Abstract/Description: Class imbalance tends to cause inferior performance in data mining learners, particularly with regard to predicting the minority class, which generally imposes a higher misclassification cost. This work explores the benefits of using genetic algorithms (GA) to develop classification models which are better able to deal with the problems encountered when mining datasets which suffer from class imbalance. Using GA we evolve configuration parameters suited for skewed datasets for three different...
Show moreClass imbalance tends to cause inferior performance in data mining learners, particularly with regard to predicting the minority class, which generally imposes a higher misclassification cost. This work explores the benefits of using genetic algorithms (GA) to develop classification models which are better able to deal with the problems encountered when mining datasets which suffer from class imbalance. Using GA we evolve configuration parameters suited for skewed datasets for three different learners: artificial neural networks, 0 4.5 decision trees, and RIPPER. We also propose a novel technique called evolutionary sampling which works to remove noisy and unnecessary duplicate instances so that the sampled training data will produce a superior classifier for the imbalanced dataset. Our GA fitness function uses metrics appropriate for dealing with class imbalance, in particular the area under the ROC curve. We perform extensive empirical testing on these techniques and compare the results with seven exist ing sampling methods.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012515
Subject Headings: Combinatorial group theory, Data mining, Machine learning, Data structure (Computer science)
Format: Document (PDF)

Title: Enabling access for mobile devices to the web services resource framework.
Creator: Mangs, Jan Christian., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The increasing availability of Web services and grid computing has made easier the access and reuse of different types of services. Web services provide network accessible interfaces to application functionality in a platform-independent manner. Developments in grid computing have led to the efficient distribution of computing resources and power through the use of stateful web services. At the same time, mobile devices as a platform of computing have become a ubiquitous, inexpensive, and...
Show moreThe increasing availability of Web services and grid computing has made easier the access and reuse of different types of services. Web services provide network accessible interfaces to application functionality in a platform-independent manner. Developments in grid computing have led to the efficient distribution of computing resources and power through the use of stateful web services. At the same time, mobile devices as a platform of computing have become a ubiquitous, inexpensive, and powerful computing resource. Concepts such as cloud computing has pushed the trend towards using grid concepts in the internet domain and are ideally suited for internet-supported mobile devices. Currently, there are a few complete implementations that leverage mobile devices as a member of a grid or virtual organization. This thesis presents a framework that enables the use of mobile devices to access stateful Web services on a Globus-based grid. To illustrate the presented framework, a user-friendly mobile application has been created that utilizes the framework libraries do to demonstrate the various functionalities that are accessible from any mobile device that supports Java ME.
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Date Issued: 2008
PURL: http://purl.flvc.org/FAU/186290
Subject Headings: User interfaces (Computer systems), Data structures (Computer science), Mobile computing, Security measures, Mobile communication systems, Computational grids (Computer systems)
Format: Document (PDF)

Title: An Empirical Study of Ordinal and Non-ordinal Classification Algorithms for Intrusion Detection in WLANs.
Creator: Gopalakrishnan, Leelakrishnan, Khoshgoftaar, Taghi M., Florida Atlantic University
Abstract/Description: Ordinal classification refers to an important category of real world problems, in which the attributes of the instances to be classified and the classes are linearly ordered. Many applications of machine learning frequently involve situations exhibiting an order among the different categories represented by the class attribute. In ordinal classification the class value is converted into a numeric quantity and regression algorithms are applied to the transformed data. The data is later...
Show moreOrdinal classification refers to an important category of real world problems, in which the attributes of the instances to be classified and the classes are linearly ordered. Many applications of machine learning frequently involve situations exhibiting an order among the different categories represented by the class attribute. In ordinal classification the class value is converted into a numeric quantity and regression algorithms are applied to the transformed data. The data is later translated back into a discrete class value in a postprocessing step. This thesis is devoted to an empirical study of ordinal and non-ordinal classification algorithms for intrusion detection in WLANs. We used ordinal classification in conjunction with nine classifiers for the experiments in this thesis. All classifiers are parts of the WEKA machinelearning workbench. The results indicate that most of the classifiers give similar or better results with ordinal classification compared to non-ordinal classification.
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Date Issued: 2006
PURL: http://purl.flvc.org/fau/fd/FA00012521
Subject Headings: Wireless LANs--Security measures, Computer networks--Security measures, Data structures (Computer science), Multivariate analysis
Format: Document (PDF)

Title: An Empirical Study of Performance Metrics for Classifier Evaluation in Machine Learning.
Creator: Bruhns, Stefan, Khoshgoftaar, Taghi M., Florida Atlantic University
Abstract/Description: A variety of classifiers for solving classification problems is available from the domain of machine learning. Commonly used classifiers include support vector machines, decision trees and neural networks. These classifiers can be configured by modifying internal parameters. The large number of available classifiers and the different configuration possibilities result in a large number of combinatiorrs of classifier and configuration settings, leaving the practitioner with the problem of...
Show moreA variety of classifiers for solving classification problems is available from the domain of machine learning. Commonly used classifiers include support vector machines, decision trees and neural networks. These classifiers can be configured by modifying internal parameters. The large number of available classifiers and the different configuration possibilities result in a large number of combinatiorrs of classifier and configuration settings, leaving the practitioner with the problem of evaluating the performance of different classifiers. This problem can be solved by using performance metrics. However, the large number of available metrics causes difficulty in deciding which metrics to use and when comparing classifiers on the basis of multiple metrics. This paper uses the statistical method of factor analysis in order to investigate the relationships between several performance metrics and introduces the concept of relative performance which has the potential to case the process of comparing several classifiers. The relative performance metric is also used to evaluate different support vector machine classifiers and to determine if the default settings in the Weka data mining tool are reasonable.
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Date Issued: 2008
PURL: http://purl.flvc.org/fau/fd/FA00012508
Subject Headings: Machine learning, Computer algorithms, Pattern recognition systems, Data structures (Computer science), Kernel functions, Pattern perception--Data processing
Format: Document (PDF)

Title: Knowledge-based expert system for selection and design of retaining structures.
Creator: Sreenivasan, Giri., Florida Atlantic University, Arockiasamy, Madasamy, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description: This thesis describes the development of a microcomputer based prototype expert system, RETAININGEARTH, for the selection and design of earth retaining structures. RETAININGEARTH is an interactive menu-driven system and consists of two modules--the selection module, SELECTWALL and the design module. SELECTWALL is developed using the rule-based M.1 knowledge engineering shell and it makes a choice of the most appropriate retaining structure from a list of ten typical walls. The design module...
Show moreThis thesis describes the development of a microcomputer based prototype expert system, RETAININGEARTH, for the selection and design of earth retaining structures. RETAININGEARTH is an interactive menu-driven system and consists of two modules--the selection module, SELECTWALL and the design module. SELECTWALL is developed using the rule-based M.1 knowledge engineering shell and it makes a choice of the most appropriate retaining structure from a list of ten typical walls. The design module consists of five independent design programs which performs detailed designs of the concrete gravity and cantilever walls, gabions, reinforced earth and sheetpile structures. The SELECTWALL and the design module are linked by the M.1 external code EXT through a control program CALL. All the design procedures are coded using the C programming language.
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Date Issued: 1991
PURL: http://purl.flvc.org/fcla/dt/14718
Subject Headings: Retaining walls--Data processing, Structural design--Computer programs, Expert systems (Computer science), Earthwork--Data processing
Format: Document (PDF)

Title: The future will be better tomorrow: a novel of apocalyptic sarcasm.
Creator: Irving, Christopher J., Bucak, Ayse Papatya, Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, Department of English
Abstract/Description: The Future Will Be Better Tomorrow is a satirical post-apocalyptic novel that examines the personal and social ironies that occur in a society that is unbalanced by an unexplained apocalyptic event. Working with a combination of dark humor and the terrifying realities of an apocalyptic event – in this case: a blackout – the novel aims to challenge the machinery established by this particular subset of the science fiction genre.
Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004124, http://purl.flvc.org/fau/fd/FA00004124
Subject Headings: Computer science., Computer communication systems., Data structures (Computer science)., Database management., Information storage and retrieval., Artificial intelligence., Computer Science., Data Structures, Cryptology and Information Theory., Database Management., Information Storage and Retrieval.
Format: Document (PDF)

Title: An Empirical Study of Random Forests for Mining Imbalanced Data.
Creator: Golawala, Moiz M., Khoshgoftaar, Taghi M., Florida Atlantic University
Abstract/Description: Skewed or imbalanced data presents a significant problem for many standard learners which focus on optimizing the overall classification accuracy. When the class distribution is skewed, priority is given to classifying examples from the majority class, at the expense of the often more important minority class. The random forest (RF) classification algorithm, which is a relatively new learner with appealing theoretical properties, has received almost no attention in the context of skewed...
Show moreSkewed or imbalanced data presents a significant problem for many standard learners which focus on optimizing the overall classification accuracy. When the class distribution is skewed, priority is given to classifying examples from the majority class, at the expense of the often more important minority class. The random forest (RF) classification algorithm, which is a relatively new learner with appealing theoretical properties, has received almost no attention in the context of skewed datasets. This work presents a comprehensive suite of experimentation evaluating the effectiveness of random forests for learning from imbalanced data. Reasonable parameter settings (for the Weka implementation) for ensemble size and number of random features selected are determined through experimentation oil 10 datasets. Further, the application of seven different data sampling techniques that are common methods for handling imbalanced data, in conjunction with RF, is also assessed. Finally, RF is benchmarked against 10 other commonly-used machine learning algorithms, and is shown to provide very strong performance. A total of 35 imbalanced datasets are used, and over one million classifiers are constructed in this work.
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Date Issued: 2007
PURL: http://purl.flvc.org/fau/fd/FA00012520
Subject Headings: Data mining--Case studies, Machine learning--Case studies, Data structure (Computer science), Trees (Graph theory)--Case studies
Format: Document (PDF)

Title: An Android approach to web services resource framework.
Creator: Garcia-Kunzel, Adriana., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Web services have become increasingly important over the past decades. Versatility and platform independence are just some of their advantages. On the other hand, grid computing enables the efficient distribution of computing resources. Together, they provide a great source of computing power that can be particularly leveraged by mobile devices. Mobile computing enables information creation, processing, storage and communication without location constraints [63], not only improving business'...
Show moreWeb services have become increasingly important over the past decades. Versatility and platform independence are just some of their advantages. On the other hand, grid computing enables the efficient distribution of computing resources. Together, they provide a great source of computing power that can be particularly leveraged by mobile devices. Mobile computing enables information creation, processing, storage and communication without location constraints [63], not only improving business' operational efficiency [63] but actually changing a way of life. However, the convenience of anytime and anywhere communication is counterbalanced by small screens, limited computing power and battery life. Despite these limitations, mobile devices can extend grid functionality by bringing to the mix not only mobile access but sensing capabilities as well, gathering information from their surroundings through built in mechanisms, such as microphone, camera, GPS and even accelerometers. Prior work has already demonstrated the possibility of enabling Web Services Resource Framework (WSRF) access to grid resources from mobile device clients in the WSRF-ME project [39], where a representative Nokia S60 Smartphone application was created on a framework, which extends the JSR-172 functionality to achieve WSRF compliance. In light of today's mobile phone market diversity, this thesis extends the solution proposed by WSRF-ME to non-Java ME phones and to Android devices in particular. Android-based device numbers have grown considerably over the past couple of years despite its recent creation and reduced availability of mature software tools., Therefore, Android's web service capabilities are studied and the original framework is analyzed in order to propose a modified framework version that achieves and documents WSRF compliant communication form Android for the first time. As a case study, an illustrative mobile File Explorer application is developed to match the mod framework' functionality to the original WSRF-ME's use case. An additional case study, the LIGO Monitor application, shows the viability of mobile web services for monitoring purposes in the Laser Interferometer Gravitational Observatory (LIGO) grid environment for the first time. The context that an actual application implementation such as LIGO provides, allows some of the challenges of real mobile grid clients to surface. As a result, the observations made during this development give way to the drafting of a preliminary set of guidelines for Globus service implementation suitable for Android consumption that still remain open for proof in future works.
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Date Issued: 2010
PURL: http://purl.flvc.org/FAU/2684891
Subject Headings: Application software, Development, Mobile communication systems, User interfaces (Computer systems), Computational grids (Computer systems), Data structures (Computer science)
Format: Document (PDF)

Title: Video transcoding using machine learning.
Creator: Holder, Christopher., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The field of Video Transcoding has been evolving throughout the past ten years. The need for transcoding of video files has greatly increased because of the new upcoming standards which are incompatible with old ones. This thesis takes the method of using machine learning for video transcoding mode decisions and discusses ways to improve the process of generating the algorithm for implementation in different video transcoders. The transcoding methods used decrease the complexity in the mode...
Show moreThe field of Video Transcoding has been evolving throughout the past ten years. The need for transcoding of video files has greatly increased because of the new upcoming standards which are incompatible with old ones. This thesis takes the method of using machine learning for video transcoding mode decisions and discusses ways to improve the process of generating the algorithm for implementation in different video transcoders. The transcoding methods used decrease the complexity in the mode decision inside the video encoder. Also methods which automate and improve results are discussed and implemented in two different sets of transcoders: H.263 to VP6 , and MPEG-2 to H.264. Both of these transcoders have shown a complexity loss of almost 50%. Video transcoding is important because the quantity of video standards have been increasing while devices usually can only decode one specific codec.
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Date Issued: 2008
PURL: http://purl.flvc.org/FAU/166451
Subject Headings: Coding theory, Image transmission, Technological innovations, File conversion (Computer science), Data structures (Computer science), MPEG (Video coding standard), Digital media, Video compression
Format: Document (PDF)

Title: Low complexity H.264 video encoder design using machine learning techniques.
Creator: Carrillo, Paula., Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: H.264/AVC encoder complexity is mainly due to variable size in Intra and Inter frames. This makes H.264/AVC very difficult to implement, especially for real time applications and mobile devices. The current technological challenge is to conserve the compression capacity and quality that H.264 offers but reduce the encoding time and, therefore, the processing complexity. This thesis applies machine learning technique for video encoding mode decisions and investigates ways to improve the...
Show moreH.264/AVC encoder complexity is mainly due to variable size in Intra and Inter frames. This makes H.264/AVC very difficult to implement, especially for real time applications and mobile devices. The current technological challenge is to conserve the compression capacity and quality that H.264 offers but reduce the encoding time and, therefore, the processing complexity. This thesis applies machine learning technique for video encoding mode decisions and investigates ways to improve the process of generating more general low complexity H.264/AVC video encoders. The proposed H.264 encoding method decreases the complexity in the mode decision inside the Inter frames. Results show, at least, a 150% average reduction of complexity and, at most, 0.6 average increases in PSNR for different kinds of videos and formats.
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Date Issued: 2008
PURL: http://purl.flvc.org/FAU/166448
Subject Headings: Code division multiple access, Digital media, Technological innovations, Image transmission, Technological innovations, Coding theory, Data structures (Computer science)
Format: Document (PDF)

Data structures (Computer science) (9)	+ -
Coding theory (2)	+ -
Computational grids (Computer systems) (2)	+ -
Data encryption (Computer science) (2)	+ -
Data structure (Computer science) (2)	+ -

Digital media (2)	+ -
Image transmission (2)	+ -
Machine learning (2)	+ -
Mobile communication systems (2)	+ -
Technological innovations (2)	+ -
User interfaces (Computer systems) (2)	+ -
Application software (1)	+ -
Application software--Development (1)	+ -
Artificial intelligence. (1)	+ -
Bayesian field theory (1)	+ -
Body area networks (Electronics) (1)	+ -
Code division multiple access (1)	+ -
Combinatorial group theory (1)	+ -
Computational complexity (1)	+ -
Computer Science. (1)	+ -
Computer algorithms (1)	+ -
Computer communication systems. (1)	+ -
Computer network protocols (1)	+ -
Computer networks -- Security measures (1)	+ -
Computer networks--Security measures (1)	+ -
Computer science. (1)	+ -
Computer security (1)	+ -
Cryptography (1)	+ -
Data Structures, Cryptology and Information Theory. (1)	+ -
Data mining (1)	+ -

College of Engineering and Computer Science (8)	+ -
Florida Atlantic University (8)	+ -
Department of Computer and Electrical Engineering and Computer Science (7)	+ -
Khoshgoftaar, Taghi M. (4)	+ -
Alhalabi, Bassem A. (1)	+ -

Arockiasamy, Madasamy (1)	+ -
Bruhns, Stefan (1)	+ -
Bucak, Ayse Papatya (1)	+ -
Carrillo, Paula. (1)	+ -
Carryl, Clyde (1)	+ -
Culibrk, Dubravko. (1)	+ -
Department of English (1)	+ -
Department of Ocean and Mechanical Engineering (1)	+ -
Dorothy F. Schmidt College of Arts and Letters (1)	+ -
Drown, Dennis J. (1)	+ -
Furht, Borko (1)	+ -
Garcia-Kunzel, Adriana. (1)	+ -
Golawala, Moiz M. (1)	+ -
Gopalakrishnan, Leelakrishnan (1)	+ -
Holder, Christopher. (1)	+ -
Irving, Christopher J. (1)	+ -
Mangs, Jan Christian. (1)	+ -
Marques, Oge (1)	+ -
Peiravian, Naser (1)	+ -
Sreenivasan, Giri. (1)	+ -
Zhu, Xingquan (1)	+ -

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