Computer Sciences Commons^™

Forensic State Acquisition From Internet Of Things (Fsaiot): A General Framework And Practical Approach For Iot Forensics Through Iot Device State Acquisition, Christopher S. Meffert, Devon R. Clark, Ibrahim Baggili, Frank Breitinger

Electrical & Computer Engineering and Computer Science Faculty Publications

IoT device forensics is a difficult problem given that manufactured IoT devices are not standardized, many store little to no historical data, and are always connected; making them extremely volatile. The goal of this paper was to address these challenges by presenting a primary account for a general framework and practical approach we term Forensic State Acquisition from Internet of Things (FSAIoT). We argue that by leveraging the acquisition of the state of IoT devices (e.g. if an IoT lock is open or locked), it becomes possible to paint a clear picture of events that have occurred. To this end, …

Information Theoretic Study Of Gaussian Graphical Models And Their Applications, Ali Moharrer

LSU Doctoral Dissertations

In many problems we are dealing with characterizing a behavior of a complex stochastic system or its response to a set of particular inputs. Such problems span over several topics such as machine learning, complex networks, e.g., social or communication networks; biology, etc. Probabilistic graphical models (PGMs) are powerful tools that offer a compact modeling of complex systems. They are designed to capture the random behavior, i.e., the joint distribution of the system to the best possible accuracy. Our goal is to study certain algebraic and topological properties of a special class of graphical models, known as Gaussian graphs. First, …

Effect Of Label Noise On The Machine-Learned Classification Of Earthquake Damage, Jared Frank, Umaa Rebbapragada, James Bialas, Thomas Oommen, Timothy C. Havens

Michigan Tech Publications, Part 1

Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due to the use of imprecise digital labeling tools and crowdsourced volunteers who are not adequately trained on or invested in the task. The spatial nature of remote sensing classification leads to the consistent mislabeling of classes that occur in close proximity to rubble, which is a major byproduct of earthquake damage in urban areas. In this study, …

Web-Based Interactive Social Media Visual Analytics, Diego Rodríguez-Baquero, Jiawei Zhang, David S. Ebert, Sorin A. Matei

The Summer Undergraduate Research Fellowship (SURF) Symposium

Real-time social media platforms enable quick information broadcasting and response during disasters and emergencies. Analyzing the massive amount of generated data to understand the human behavior requires data collection and acquisition, parsing, filtering, augmentation, processing, and representation. Visual analytics approaches allow decision makers to observe trends and abnormalities, correlate them with other variables and gain invaluable insight into these situations. In this paper, we propose a set of visual analytic tools for analyzing and understanding real-time social media data in times of crisis and emergency situations. First, we model the degree of risk of individuals’ movement based on evacuation zones …

Breaking Into The Vault: Privacy, Security And Forensic Analysis Of Android Vault Applications, Xiaolu Zhang, Ibrahim Baggili, Frank Breitinger

Electrical & Computer Engineering and Computer Science Faculty Publications

In this work we share the first account for the forensic analysis, security and privacy of Android vault applications. Vaults are designed to be privacy enhancing as they allow users to hide personal data but may also be misused to hide incriminating files. Our work has already helped law enforcement in the state of Connecticut to reconstruct 66 incriminating images and 18 videos in a single criminal case. We present case studies and results from analyzing 18 Android vault applications (accounting for nearly 220 million downloads from the Google Play store) by reverse engineering them and examining the forensic artifacts …

Data Extraction From Web Tables: The Devil Is In The Details, George Nagy, Sharad C. Seth, Dongpu Jin, David W. Embley, Spencer Machado, Mukkai Krishnamoorthy

School of Computing: Conference and Workshop Papers

We present a method based on header paths for efficient and complete extraction of labeled data from tables meant for humans. Although many table configurations yield to the proposed syntactic analysis, some require access to semantic knowledge. Clicking on one or two critical cells per table, through a simple interface, is sufficient to resolve most of these problem tables. Header paths, a purely syntactic representation of visual tables, can be transformed (“factored”) into existing representations of structured data such as category trees, relational tables, and RDF triples. From a random sample of 200 web tables from ten large statistical web …

End-To-End Conversion Of Html Tables For Populating A Relational Database, George Nagy, David W. Embley, Sharad C. Seth

School of Computing: Conference and Workshop Papers

Automating the conversion of human-readable HTML tables into machine-readable relational tables will enable end-user query processing of the millions of data tables found on the web. Theoretically sound and experimentally successful methods for index-based segmentation, extraction of category hierarchies, and construction of a canonical table suitable for direct input to a relational database are demonstrated on 200 heterogeneous web tables. The methods are scalable: the program generates the 198 Access compatible CSV files in ~0.1s per table (two tables could not be indexed).

Harmonic Distortion Minimization In Power Grids With Wind And Electric Vehicles, Ritam Misra, Sumit Paudyal, Oguzhan Ceylan, Paras Mandal

Michigan Tech Publications, Part 1

Power-electronic interfacing based devices such as wind generators (WGs) and electrical vehicles (EVs) cause harmonic distortions on the power grid. Higher penetration and uncoordinated operation of WGs and EVs can lead to voltage and current harmonic distortions, which may exceed IEEE limits. It is interesting to note that WGs and EVs have some common harmonic profiles. Therefore, when EVs are connected to the grid, the harmonic pollution EVs impart onto the grid can be reduced to some extent by the amount of wind power injecting into the grid and vice versa. In this context, this work studies the impact of …

Speech Based Machine Learning Models For Emotional State Recognition And Ptsd Detection, Debrup Banerjee

Electrical & Computer Engineering Theses & Dissertations

Recognition of emotional state and diagnosis of trauma related illnesses such as posttraumatic stress disorder (PTSD) using speech signals have been active research topics over the past decade. A typical emotion recognition system consists of three components: speech segmentation, feature extraction and emotion identification. Various speech features have been developed for emotional state recognition which can be divided into three categories, namely, excitation, vocal tract and prosodic. However, the capabilities of different feature categories and advanced machine learning techniques have not been fully explored for emotion recognition and PTSD diagnosis. For PTSD assessment, clinical diagnosis through structured interviews is a …

College Of Engineering Senior Design Competition Spring 2017, University Of Nevada, Las Vegas

Fred and Harriet Cox Senior Design Competition Projects

Part of every UNLV engineering student’s academic experience, the senior design project stimulates engineering innovation and entrepreneurship. Each student in their senior year chooses, plans, designs, and prototypes a product in this required element of the curriculum. A capstone to the student’s educational career, the senior design project encourages the student to use everything learned in the engineering program to create a practical, real world solution to an engineering challenge. The senior design competition helps focus the senior students in increasing the quality and potential for commercial application for their design projects. Judges from local industry evaluate the projects on …

Deployment Strategies For A Fleet Of Unmanned Aerial Vehicles Providing Cellular And Data Services, Nima Mohseni

UNLV Theses, Dissertations, Professional Papers, and Capstones

Wireless voice and data communications have become an essential part of our day to day lives. In order to provide these services to as many people as possible, a great infrastructure has been put in place over the last two decades throughout the world. The current infrastructure is mainly consists of cellular towers with gateways to the telecommunication backbone. The wireless infrastructure is doing an adequate job of providing voice and data services, getting more powerful and efficient every day. However, because wireless infrastructure is mainly based on fixed cell towers, it lacks the flexibility and dynamism that may be …

Hexarray: A Novel Self-Reconfigurable Hardware System, Fady Hussein

Boise State University Theses and Dissertations

Evolvable hardware (EHW) is a powerful autonomous system for adapting and finding solutions within a changing environment. EHW consists of two main components: a reconfigurable hardware core and an evolutionary algorithm. The majority of prior research focuses on improving either the reconfigurable hardware or the evolutionary algorithm in place, but not both. Thus, current implementations suffer from being application oriented and having slow reconfiguration times, low efficiencies, and less routing flexibility. In this work, a novel evolvable hardware platform is proposed that combines a novel reconfigurable hardware core and a novel evolutionary algorithm.

The proposed reconfigurable hardware core is a …

A Manufacturer Design Kit For Multi-Chip Power Module Layout Synthesis, Jonathan Main

Electrical Engineering Undergraduate Honors Theses

The development of Multi-Chip Power Modules (MCPMs) has been a key factor in recent advancements in power electronics technologies. MCPMs achieve higher power density by combining multiple power semiconductor devices into one package. The work detailed in this thesis is part of an ongoing project to develop a computer-aided design software tool known as PowerSynth for MCPM layout synthesis and optimization. This thesis focuses on the definition and design of a Manufacturer Design Kit (MDK) for PowerSynth, which enables the designer to design an MCPM for a manufacturer’s fabrication process.

The MDK is comprised of a layer stack and technology …

Java Server Reliability In The Presence Of Failures, Rich Coe

Master's Theses (2009 -)

A design for the separation of a server interface and work processing. Numerous sources, Tanenbaum (Tanenbaum Modern Operating Systems, 493), Goscinski (Goscinski Distributed operating systems, 203), and Birman (Birman Reliable distributed systems, 265), all discuss the concept of Two-Phase Commit, where a coordinator directs one or more processes to perform a transaction. If the transaction or any of the processes fail, the coordinator can decide how to proceed by either retrying or aborting the request. The popular web browser Chrome utilizes a separate process for each tab displayed. Should the rendering and display of a web page cause a crash, …

Multi-Valued Sequences Generated By Power Residue Symbols Over Odd Characteristic Fields, Begum Nasima, Yasuyuki Nogami, Satoshi Uehara, Robert Morelos-Zaragoza

Faculty Publications

This paper proposes a new approach for generating pseudo random multi-valued (including binary-valued) sequences. The approach uses a primitive polynomial over an odd characteristic prime field $\f{p}$, where p is an odd prime number. Then, for the maximum length sequence of vectors generated by the primitive polynomial, the trace function is used for mapping these vectors to scalars as elements in the prime field. Power residue symbol (Legendre symbol in binary case) is applied to translate the scalars to k-value scalars, where k is a prime factor of p-1. Finally, a pseudo random k-value sequence is obtained. Some important properties …

Special Issue Of Best Papers From The 11th International Conference On Systematic Approaches To Digital Forensic Engineering (Sadfe 2016)

Journal of Digital Forensics, Security and Law

The SADFE series feature the different editions of the International Conference on Systematic Approaches to Digital Forensics Engineering. Now in its eleventh edition, SADFE has established itself as the premier conference for researchers and practitioners working in Systematic Approaches to Digital Forensics Engineering.

SADFE 2016, the eleventh international conference on Systematic Approaches to Digital Forensic Engineering was held in Kyoto, Japan, September 20 - 22, 2016.

Digital forensics engineering and the curation of digital collections in cultural institutions face pressing and overlapping challenges related to provenance, chain of custody, authenticity, integrity, and identity. The generation, analysis and sustainability of digital …

Compression Of Virtual-Machine Memory In Dynamic Malware Analysis, James E. Fowler Ph.D.

Journal of Digital Forensics, Security and Law

Lossless compression of memory dumps from virtual machines that run malware samples is considered with the goal of significantly reducing archival costs in dynamic-malware-analysis applications. Given that, in such dynamic-analysis scenarios, malware samples are typically run in virtual machines just long enough to activate any self-decryption or other detection- avoidance maneuvers, the virtual-machine memory typically changes little from that of the baseline state, with the difference being attributable in large degree to the loading of additional executables and libraries. Consequently, delta coding is proposed to compress the current virtual-machine memory dump by coding its differences with respect to a predicted …

Front Matter

Journal of Digital Forensics, Security and Law

No abstract provided.

Find Me If You Can: Mobile Gps Mapping Applications Forensic Analysis & Snavp The Open Source, Modular, Extensible Parser, Jason Moore, Ibrahim Baggili, Frank Breitinger

Journal of Digital Forensics, Security and Law

The use of smartphones as navigation devices has become more prevalent. The ubiquity of hand-held navigation devices such as Garmins or Toms Toms has been falling whereas the ownership of smartphones and their adoption as GPS devices is growing. This work provides a comprehensive study of the most popular smartphone mapping applications, namely Google Maps, Apple Maps, Waze, MapQuest, Bing, and Scout, on both Android and iOS. It details what data was found, where it was found, and how it was acquired for each application. Based on the findings, the work allowed for the construction of a tool capable of …

A Forensic Email Analysis Tool Using Dynamic Visualization, Johannes Stadlinger, Andreas Dewald

Journal of Digital Forensics, Security and Law

Communication between people counts to the most important information of today’s business. As a result, in case of forensic investigations in big companies, analysis of communication data in general and especially email, as the still most widely used business communication platform with an immense and still growing volume, is a typical task in digital forensics. One of the challenges is to identify the relevant communication partners and structures in the suspects surrounding as quickly as possible in order to react appropriately and identify further targets of evaluation. Due to the amount of emails in typical inboxes, reading through all the …

Table Of Contents

Journal of Digital Forensics, Security and Law

No abstract provided.

Autoprov: An Automated File Provenance Collection Tool, Ryan A. Good

Theses and Dissertations

A file's provenance is a detailing of its origins and activities. There are tools available that are useful in maintaining the provenance of a file. Unfortunately for digital forensics, these tools require prior installation on the computer of interest while provenance generating events happen. The presented tool addresses this by reconstructing a file's provenance from several temporal artifacts. It identifies relevant temporal and user correlations between these artifacts, and presents them to the user. A variety of predefined use cases and real world data are tested against to demonstrate that this software allows examiners to draw useful conclusions about the …

A Framework For Categorization Of Industrial Control System Cyber Training Environments, Evan G. Plumley

Theses and Dissertations

First responders and professionals in hazardous occupations undergo training and evaluations for the purpose of mitigating risk and damage. For example, helicopter pilots train with multiple categorized simulations that increase in complexity before flying a real aircraft. However in the industrial control cyber incident response domain, where incident response professionals help detect, respond and recover from cyber incidents, no official categorization of training environments exist. To address this gap, this thesis provides a categorization of industrial control training environments based on realism. Four levels of environments are proposed and mapped to Blooms Taxonomy. This categorization will help organizations determine which …

Applying Cyber Threat Intelligence To Industrial Control Systems, Matthew P. Sibiga

Theses and Dissertations

A cybersecurity initiative known as cyber threat intelligence (CTI) has recently been developed and deployed. The overall goal of this new technology is to help protect network infrastructures. Threat intelligence platforms (TIPs) have also been created to help facilitate CTI effectiveness within organizations. There are many benefits that both can achieve within the information technology (IT) sector. The industrial control system (ICS) sector can also benefit from these technologies as most ICS networks are connected to IT networks. CTI and TIPs become resourceful when using indicators of compromise (IOCs) from known ICS malware attacks and an open source intrusion detection …

Power-Aware Datacenter Networking And Optimization, Qing Yi

Dissertations and Theses

Present-day datacenter networks (DCNs) are designed to achieve full bisection bandwidth in order to provide high network throughput and server agility. However, the average utilization of typical DCN infrastructure is below 10% for significant time intervals. As a result, energy is wasted during these periods. In this thesis we analyze traffic behavior of datacenter networks using traces as well as simulated models. Based on the insight developed, we present techniques to reduce energy waste by making energy use scale linearly with load. The solutions developed are analyzed via simulations, formal analysis, and prototyping. The impact of our work is significant …

Women In It: Be The Change, Marcia L. Dority Baker

Information Technology Services: Publications

The influence of established women in IT — specifically Florence Hudson and Melissa Woo — encouraged a librarian to apply for a position in Information Technology Services at the University of Nebraska–Lincoln.

A hands-on approach to planning the IT Leadership conference developed a strong collaborative network that also helped grow attendance.

Paying attention to the pros and cons of the 2016 conference guided the planners in modifying their approach to the upcoming October 2017 conference and its focus on diversity and inclusion.

A main goal for future IT Leadership conferences focusing on women and diversity in IT is providing attendees …

Drop (Drone Open Source Parser) Your Drone: Forensic Analysis Of The Dji Phantom Iii, Devon R. Clark, Christopher S. Meffert, Ibrahim Baggili, Frank Breitinger

Electrical & Computer Engineering and Computer Science Faculty Publications

The DJI Phantom III drone has already been used for malicious activities (to drop bombs, remote surveillance and plane watching) in 2016 and 2017. At the time of writing, DJI was the drone manufacturer with the largest market share. Our work presents the primary thorough forensic analysis of the DJI Phantom III drone, and the primary account for proprietary file structures stored by the examined drone. It also presents the forensically sound open source tool DRone Open source Parser (DROP) that parses proprietary DAT files extracted from the drone's nonvolatile internal storage. These DAT files are encrypted and encoded. The …

An Ensemble Learning Framework For Anomaly Detection In Building Energy Consumption, Daniel B. Araya, Katarina Grolinger, Hany F. Elyamany, Miriam Am Capretz, Girma T. Bitsuamlak

Electrical and Computer Engineering Publications

During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework …

A Gamification Framework For Sensor Data Analytics, Alexandra L'Heureux, Katarina Grolinger, Wilson A. Higashino, Miriam A. M. Capretz

Electrical and Computer Engineering Publications

The Internet of Things (IoT) enables connected objects to capture, communicate, and collect information over the network through a multitude of sensors, setting the foundation for applications such as smart grids, smart cars, and smart cities. In this context, large scale analytics is needed to extract knowledge and value from the data produced by these sensors. The ability to perform analytics on these data, however, is highly limited by the difficulties of collecting labels. Indeed, the machine learning techniques used to perform analytics rely upon data labels to learn and to validate results. Historically, crowdsourcing platforms have been used to …

Deep Neural Networks With Confidence Sampling For Electrical Anomaly Detection, Norman L. Tasfi, Wilson A. Higashino, Katarina Grolinger, Miriam A. M. Capretz

Electrical and Computer Engineering Publications

The increase in electrical metering has created tremendous quantities of data and, as a result, possibilities for deep insights into energy usage, better energy management, and new ways of energy conservation. As buildings are responsible for a significant portion of overall energy consumption, conservation efforts targeting buildings can provide tremendous effect on energy savings. Building energy monitoring enables identification of anomalous or unexpected behaviors which, when corrected, can lead to energy savings. Although the available data is large, the limited availability of labels makes anomaly detection difficult. This research proposes a deep semi-supervised convolutional neural network with confidence sampling for …