Engineering Commons^™

Simulation For Cybersecurity: State Of The Art And Future Directions, Hamdi Kavak, Jose J. Padilla, Daniele Vernon-Bido, Saikou Y. Diallo, Ross Gore, Sachin Shetty

VMASC Publications

In this article, we provide an introduction to simulation for cybersecurity and focus on three themes: (1) an overview of the cybersecurity domain; (2) a summary of notable simulation research efforts for cybersecurity; and (3) a proposed way forward on how simulations could broaden cybersecurity efforts. The overview of cybersecurity provides readers with a foundational perspective of cybersecurity in the light of targets, threats, and preventive measures. The simulation research section details the current role that simulation plays in cybersecurity, which mainly falls on representative environment building; test, evaluate, and explore; training and exercises; risk analysis and assessment; and humans …

Internet-Of-Things Devices In Support Of The Development Of Echoic Skills Among Children With Autism Spectrum Disorder, Krzysztof J. Rechowicz, John B. Stull, Michelle M. Hascall, Saikou Y. Diallo, Kevin J. O'Brien

VMASC Publications

A significant therapeutic challenge for people with disabilities is the development of verbal and echoic skills. Digital voice assistants (DVAs), such as Amazon’s Alexa, provide networked intelligence to billions of Internet-of-Things devices and have the potential to offer opportunities to people, such as those diagnosed with autism spectrum disorder (ASD), to advance these necessary skills. Voice interfaces can enable children with ASD to practice such skills at home; however, it remains unclear whether DVAs can be as proficient as therapists in recognizing utterances by a developing speaker. We developed an Alexa-based skill called ASPECT to measure how well the DVA …

Methods For Weighting Decisions To Assist Modelers And Decision Analysts: A Review Of Ratio Assignment And Approximate Techniques, Barry Ezell, Christopher J. Lynch, Patrick T. Hester

VMASC Publications

Computational models and simulations often involve representations of decision-making processes. Numerous methods exist for representing decision-making at varied resolution levels based on the objectives of the simulation and the desired level of fidelity for validation. Decision making relies on the type of decision and the criteria that is appropriate for making the decision; therefore, decision makers can reach unique decisions that meet their own needs given the same information. Accounting for personalized weighting scales can help to reflect a more realistic state for a modeled system. To this end, this article reviews and summarizes eight multi-criteria decision analysis (MCDA) techniques …

Human Factors, Ergonomics And Industry 4.0 In The Oil & Gas Industry: A Bibliometric Analysis, Francesco Longo, Antonio Padovano, Lucia Gazzaneo, Jessica Frangella, Rafael Diaz

VMASC Publications

Over the last few years, the Human Factors and Ergonomics (HF/E) discipline has significantly benefited from new human-centric engineered digital solutions of the 4.0 industrial age. Technologies are creating new socio-technical interactions between human and machine that minimize the risk of design-induced human errors and have largely contributed to remarkable improvements in terms of process safety, productivity, quality, and workers’ well-being. However, despite the Oil&Gas (O&G) sector is one of the most hazardous environments where human error can have severe consequences, Industry 4.0 aspects are still scarcely integrated with HF/E. This paper calls for a holistic understanding of the changing …

Developing An Artificial Intelligence Framework To Assess Shipbuilding And Repair Sub-Tier Supply Chains Risk, Rafael Diaz, Katherine Smith, Beatriz Acero, Francesco Longo, Antonio Padovano

VMASC Publications

The defense shipbuilding and repair industry is a labor-intensive sector that can be characterized by low-product volumes and high investments in which a large number of shared resources, technology, suppliers, and processes asynchronously converge into large construction projects. It is mainly organized by the execution of a complex combination of sequential and overlapping stages. While entities engaged in this large-scale endeavor are often knowledgeable about their first-tier suppliers, they usually do not have insight into the lower tiers suppliers. A sizable part of any supply chain disruption is attributable to instabilities in sub-tier suppliers. This research note conceptually delineates a …

Hidden Markov Model And Cyber Deception For The Prevention Of Adversarial Lateral Movement, Md Ali Reza Al Amin, Sachin Shetty, Laurent Njilla, Deepak K. Tosh, Charles Kamhoua

Computational Modeling & Simulation Engineering Faculty Publications

Advanced persistent threats (APTs) have emerged as multi-stage attacks that have targeted nation-states and their associated entities, including private and corporate sectors. Cyber deception has emerged as a defense approach to secure our cyber infrastructure from APTs. Practical deployment of cyber deception relies on defenders' ability to place decoy nodes along the APT path optimally. This paper presents a cyber deception approach focused on predicting the most likely sequence of attack paths and deploying decoy nodes along the predicted path. Our proposed approach combines reactive (graph analysis) and proactive (cyber deception technology) defense to thwart the adversaries' lateral movement. The …

Secure And Trustworthy Hardware And Machine Learning Systems For Internet Of Things, Shayan Taheri

Electronic Theses and Dissertations, 2020-2023

The advancements on the Internet have enabled connecting more devices into this technology every day. This great connectivity has led to the introduction of the internet of things (IoTs) that is a great bed for engagement of all new technologies for computing devices and systems. Nowadays, the IoT devices and systems have applications in many sensitive areas including military systems. These challenges target hardware and software elements of IoT devices and systems. Integration of hardware and software elements leads to hardware systems and software systems in the IoT platforms, respectively. A recent trend for the hardware systems is making them …

Network Function Virtualization Technology Adoption Strategies, Abdlrazaq Ayodeji Adeyi Shittu

Walden Dissertations and Doctoral Studies

Network function virtualization (NFV) is a novel system adopted by service providers and organizations, which has become a critical organizational success factor. Chief information officers (CIOs) aim to adopt NFV to consolidate and optimize network processes unavailable in conventional methods. Grounded in the diffusion of innovation theory (DOI), the purpose of this multiple case research study was to explore strategies chief information officers utilized to adopt NFV technology. Participants include two CIOs, one chief security information officer (CSIO), one chief technical officer (CTO), and two senior information technology (IT) executives. Data were collected through semi-structured telephone interviews and eight organizational …

Artificial Intelligence And Soft Computing In Smart Structural Systems, Sajad Javadinasab Hormozabad

Theses and Dissertations--Civil Engineering

Next-generation smart cities are the key feature in the next chapter of human life. Cities that employ innovative and technology-driven solutions to improve the sustainability, resilience, prosperity, and amenity of the community are considered smart cities. Development of smart cities requires fundamental innovations in many technical and technological aspects including those contributing to smart structures. Smart technologies improve the structural performance against natural disasters like earthquakes, hurricanes, tornados, and promote the sustainability of structural systems. Next-generation smart structures encompass a variety of technologies including Structural Control (SC) and Structural Health Monitoring (SHM). SC covers methodologies and technologies that modify the …

Fpga-Augmented Secure Crash-Consistent Non-Volatile Memory, Yu Zou

Electronic Theses and Dissertations, 2020-2023

Emerging byte-addressable Non-Volatile Memory (NVM) technology, although promising superior memory density and ultra-low energy consumption, poses unique challenges to achieving persistent data privacy and computing security, both of which are critically important to the embedded and IoT applications. Specifically, to successfully restore NVMs to their working states after unexpected system crashes or power failure, maintaining and recovering all the necessary security-related metadata can severely increase memory traffic, degrade runtime performance, exacerbate write endurance problem, and demand costly hardware changes to off-the-shelf processors. In this thesis, we summarize and expand upon two of our innovative works, ARES and HERMES, to design …

Light Field Compression And Manipulation Via Residual Convolutional Neural Network, Eisa Hedayati

Dissertations, Master's Theses and Master's Reports

Light field (LF) imaging has gained significant attention due to its recent success in microscopy, 3-dimensional (3D) displaying and rendering, augmented and virtual reality usage. Postprocessing of LF enables us to extract more information from a scene compared to traditional cameras. However, the use of LF is still a research novelty because of the current limitations in capturing high-resolution LF in all of its four dimensions. While researchers are actively improving methods of capturing high-resolution LF's, using simulation, it is possible to explore a high-quality captured LF's properties. The immediate concerns following the LF capture are its storage and processing …

A New Hybrid Genetic Algorithm For Protein Structure Prediction On The 2dtriangular Lattice, Bouroubi Sadek, Nabil Boumedine

Turkish Journal of Electrical Engineering and Computer Sciences

The flawless functioning of the protein is essentially related to its three-dimensional structure. Therefore,predicting protein structure from its amino acid sequence is a fundamental problem that draws researchers' attentionin many areas. The protein structure prediction problem (PSP) can be formulated as a combinatorial optimization problem based on simplified lattice models such as the hydrophobic-polar model. In this paper, we propose a new hybridalgorithm that combines three different known heuristic algorithms: the genetic algorithm, the tabu search strategy,and the local search algorithm to solve the PSP problem. Regarding the evaluation of the proposed approach, wepresent an experimental study, where we consider …

Robust Image Hashing Based On Structural And Perceptual Features Forauthentication Of Color Images, Muhammad Farhan Khan, Syed Muhammad Monir, Imran Naseem

Turkish Journal of Electrical Engineering and Computer Sciences

Image hashing is one of the most celebrated techniques regarding the discipline of image forensics, imageretrieval, image indexing, content verification, and zero watermarking. For such sensitive and complex problems,generation of a unique and robust image hash is an utmost prerequisite for an image identifier driven from the perceptualcontents of an image. As a design perspective, it is essential for an image hash to have robustness and optimizeddiscriminative capability. We propose a robust image hashing technique by acquiring perceptual features based on anovel distance magnitude profile utilizing color pixel incongruity among the contiguous pixels, as well as producing astructural image for …

Dynamic Distributed Trust Management Scheme For The Internet Of Things, Syed Wasif Abbas Hamdani, Abdul Waheed Khan, Naima Iltaf, Javed Iqbal Bangash, Yawar Abbas Bangash, Asfandyar Khan

Turkish Journal of Electrical Engineering and Computer Sciences

The Internet of Things (IoT) comprises of a diverse network of homogeneous and heterogeneous nodesthat can be accessed through network ubiquitously. In unattended environments, the IoT devices are prone to variousattacks including ballot-stu?ing, bad-mouthing, self-promotion, on-off, opportunistic behavior attacks, etc. The on-offattack is di?icult to detect as nodes switch their behavior from normal to malicious alternatively. A trust managementmodel is a tool to defend the IoT system against malicious activities and provide reliable data exchange. The majorityof existing IoT trust management techniques are based on static reward and punishment values in pursuit of trustcomputation thereby allowing the misbehaving nodes to …

Sliding Mode Pll-Pdm Controller For Induction Heating System, Harun Özbay, Aki̇f Karafi̇l, Seli̇m Öncü

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a sliding mode controlled phase locked loop (SMC-PLL) was developed for induction heating(IH) applications with a resonant inverter. PLL applications are widely used in induction heating applications to achievezero voltage switching and zero current switching. In many PLL applications, the frequency tracking is too slow andunreliable. Therefore, a sliding mode controller was developed to provide robust and fast PLL. Furthermore, a pulsedensity modulation (PDM) control strategy was developed to work at the resonant frequency for all power levels. ThePDM power control is a good solution for the design of high-frequency inverters because of a great reduction of …

An Efficient Deep Learning Based Fog Removal Model For Multimedia Applications, Gaurav Saxena, Sarita Singh Bhadauria

Turkish Journal of Electrical Engineering and Computer Sciences

In the present era of technology, several applications such as surveillances systems, security and object recognitions mainly depend on the contents of an image. In this context, the hazy/foggy environment and/or other adverse climatic conditions degrade the image contents that severely influences the result of related applications. The effective haze removal from a single image decides the reliability of these systems. The convolutional neural network (CNN) based techniques are widely used among the available image dehazing methods. However, in CNN based image dehazing techniques, the robustness and accuracy of the learning models are based on the improvement of transmission estimation …

Determining And Evaluating New Store Locations Using Remote Sensing Andmachine Learning, Berkan Höke, Zeynep Zerri̇n Turgay, Cem Ünsalan, Hande Küçükaydin

Turkish Journal of Electrical Engineering and Computer Sciences

Decision making for store locations is crucial for retail companies as the profit depends on the location. The key point for correct store location is profit approximation, which is highly dependent on population of the corresponding region, and hence, the volume of the residential area. Thus, estimating building volumes provides insight about the revenue if a new store is about to be opened there. Remote sensing through stereo/tri-stereo satellite images provides wide area coverage as well as adequate resolution for three dimensional reconstruction for volume estimation. We reconstruct 3D map of corresponding region with the help of semiglobal matching and …

A New Design Algorithm For The Pmhs Motor Considering The Combination Ratio, Ali Behniafar, Ahmad Darabi

Turkish Journal of Electrical Engineering and Computer Sciences

Recently, the hysteresis motors have a special significance in the nuclear industries. This is because these motors have some advantages such as low noise, high mechanical strength and group feeding ability. They also have some disadvantages that make some limitations for related industries. These disadvantages include low synchronization torque, low power factor, low efficiency, and hunting. One solution to reduce these disadvantages is to combine the hysteresis motor with the PM motor. This however requires a correct and flexible design procedure as well as an appropriate choice of the machine structure. Accordingly, this paper aims to present a new design …

Visual Object Detection For Autonomous Transport Vehicles In Smart Factories, Nazlican Gengeç, Onur Eker, Hakan Çevi̇kalp, Ahmet Yazici, Hasan Serhan Yavuz

Turkish Journal of Electrical Engineering and Computer Sciences

Autonomous transport vehicles (ATVs) are one of the most substantial components of smart factories of Industry 4.0. They are primarily considered to transfer the goods or perform some certain navigation tasks in the factory with self driving. The recent developments on computer vision studies allow the vehicles to visually perceive the environment and the objects in the environment. There are numerous applications especially for smart traffic networks in outdoor environments but there is lack of application and databases for autonomous transport vehicles in indoor industrial environments. There exist some essential safety and direction signs in smart factories and these signs …

Field-Programmable Gate Array (Fpga) Hardware Design And Implementation Ofa New Area Efficient Elliptic Curve Crypto-Processor, Muhammad Kashif, İhsan Çi̇çek

Turkish Journal of Electrical Engineering and Computer Sciences

Elliptic curve cryptography provides a widely recognized secure environment for information exchange in resource-constrained embedded system applications, such as Internet-of-Things, wireless sensor networks, and radio frequency identification. As the elliptic-curve cryptography (ECC) arithmetic is computationally very complex, there is a need for dedicated hardware for efficient computation of the ECC algorithm in which scalar point multiplication is the performance bottleneck. In this work, we present an ECC accelerator that computes the scalar point multiplication for the NIST recommended elliptic curves over Galois binary fields by using a polynomial basis. We used the Montgomery algorithm with projective coordinates for the scalar …

A Hybrid Technique Using Modified Icp Algorithm For Faster And Automatic 2d &3d Microscopic Image Stitching In Cytopathologic Examination, Hülya Doğan, Eli̇f Baykal Kablan, Murat Eki̇nci̇, Mustafa Emre Erci̇n, Şafak Ersöz

Turkish Journal of Electrical Engineering and Computer Sciences

Due to the limitations of the light microscopic system such as limited depth of field and narrow field of view, entire sample areas are invisible and pathologists move the light microscope stage along the X - Y - Z axes with eye-hand coordination. In order to reduce the dependence on the pathologist and to allow whole sample areas to be examined in a short time without any control (without eye-hand coordination), this study creates 2D & 3D panoramic images with wide-view of sample in the light microscopic systems. According to our literature research, there is no study that creates 2D …

Performance Evaluation Of Hht And Wt For Detection Of Hif And Ct Saturationin Smart Grids, Saeid Heidari, Saeed Asgharigovar, Pouya Pourghasem, Heresh Seyedi, Ömer Usta

Turkish Journal of Electrical Engineering and Computer Sciences

Hilbert-Huang transform (HHT), continuous wavelet transform (CWT) and discrete wavelet transform (DWT) are well-known signal processing methods that are widely utilized for feature extraction and fault detection by protection systems in smart grids. In this paper, we assess the performances of these methods encountering challenging situations in distribution networks, i.e. high impedance arcing fault (HIF) and current transformer (CT) saturation. Low fault current amplitude in HIF case causes the overcurrent protection, which is the predominant protection method in distribution grids, to fail. Furthermore, some faults may lead to CT saturation, which may result in delayed operation of the relay. To …

Prediction Of Long-Term Physical Properties Of Low Density Polyethylene (Ldpe)Cable Insulation Materials By Artificial Neural Network Modeling Approach Underenvironmental Constraints, Ferhat Slimani, Abdallah Hedir, Mustapha Moudoud, Ali̇ Durmuş, Mounir Amir, Mohamed Megherbi

Turkish Journal of Electrical Engineering and Computer Sciences

This study quantifies long-term physical properties of low density polyethylene (LDPE) cables insulations exposed to environmental constraints such as UV radiation and temperature via both experimental measurements and mathematical modeling approach. For this purpose, tensile test and electrical breakdown test were carried out to determine elongation at break, tensile strength, and dielectric strength of unaged and aged specimens, respectively. Experimental results showed that both UV and temperature exposures affected the LDPE properties, significantly. A supervised artificial neural network (ANN) trained by the Levenberg?Marquardt algorithm was designed for predicting the long-term characteristics of specimens and also for minimizing the experimental procedures. …

A New Distributed Anomaly Detection Approach For Log Ids Management Based Ondeep Learning, Murat Koca, Muhammed Ali̇ Aydin, Ahmet Sertbaş, Abdül Hali̇m Zai̇m

Turkish Journal of Electrical Engineering and Computer Sciences

Today, with the rapid increase of data, the security of big data has become more important than ever for managers. However, traditional infrastructure systems cannot cope with increasingly big data that is created like an avalanche. In addition, as the existing database systems increase licensing costs per transaction, organizations using information technologies are shifting to free and open source solutions. For this reason, we propose an anomaly attack detection model on Apache Hadoop distributed file system (HDFS), which stands out in open source big data analytics, and Apache Spark, which stands out with its speed performance in analysis to reduce …

Learning Prototypes For Multiple Instance Learning, Özgür Emre Si̇vri̇kaya, Mert Yüksekgönül, Mustafa Gökçe Baydoğan

Turkish Journal of Electrical Engineering and Computer Sciences

Multiple instance learning (MIL) is a weakly supervised learning method that works on the labeled bag of instances data. A prototypical network is a popular embedding approach in MIL. They overcome the common problems that other MIL approaches may have to deal with including dimensionality, loss of instance-level information, and complexity. They demonstrate competitive performance in classification. This work proposes a simple model that provides a permutation invariant prototype generator from a given MIL data set. We aim to find out prototypes in the feature space to map the collection of instances (i.e. bags) to a distance feature space and …

Automated Waterloo Rubric For Concept Map Grading, Shresht Bhatia, Sajal Bhatia, Irfan Ahmed

School of Computer Science & Engineering Faculty Publications

Concept mapping is a well-known pedagogical tool to help students organize, represent, and develop an understanding of a topic. The grading of concept maps is typically manual, time-consuming, and tedious, especially for a large class. Existing research mostly focuses on topological scoring based-on structural features of concept maps. However, the scoring does not achieve comparable accuracy to well-defined rubrics for manual analysis on the quality of content in a concept map. This paper presents Kastor, a new method to automate the Waterloo Rubric of scoring concept maps by quantifying the rubric’s quality assessment parameters. The evaluation is performed on a …

Improving A Network Intrusion Detection System’S Efficiency Using Model-Based Data Augmentation, Vinicius Waterkemper Lodetti

Dissertations

A network intrusion detection system (NIDS) is one important element to mitigate cybersecurity risks, the NIDS allow for detecting anomalies in a network which may be a cyberattack to a corporate network environment. A NIDS can be seen as a classification problem where the ultimate goal is to distinguish between malicious traffic among a majority of benign traffic. Researches on NIDS are often performed using outdated datasets that don’t represent the actual cyberspace. Datasets such as the CICIDS2018 address this gap by being generated from attacks and an infrastructure that reflects an up-to-date scenario.

A problem may arise when machine …

An Evaluation On The Performance Of Code Generated With Webassembly Compilers, Raymond Phelan

Dissertations

WebAssembly is a new technology that is revolutionizing the web. Essentially it is a low-level binary instruction set that can be run on browsers, servers or stand-alone environments. Many programming languages either currently have, or are working on, compilers that will compile the language into WebAssembly. This means that applications written in languages like C++ or Rust can now be run on the web, directly in a browser or other environment. However, as we will highlight in this research, the quality of code generated by the different WebAssembly compilers varies and causes performance issues. This research paper aims to evaluate …

Requirements Engineering Education Slr Data Set 1988-2020, Marian Daun, Alicia M. Grubb, Bastian Tenbergen

Data

Requirements Engineering (RE) has established itself as a core software engineering discipline. It is well acknowledged that good RE leads to higher quality software and considerably reduces the risk of failure or exceeding budgets of software development projects. Therefore, it is of vital importance to train future software engineers in RE and educate future requirements engineers to adequately manage requirements in various projects. However, to date there exists no central dataset for RE Education articles. To lay the foundation for this important mission, we conducted a systematic literature review. In this dataset, we present 152 articles from the Requirements Engineering …

A Hybrid Neural Network For Stock Price Direction Forecasting, Daniel Devine

Dissertations

The volatility of stock markets makes them notoriously difficult to predict and is the reason that many investors sell out at the wrong time. Contrary to the efficient market hypothesis (EMH) and the random walk theory, contribution to the study of machine learning models for stock price forecasting has shown evidence of stock markets predictability with varying degrees of success. Contemporary approaches have sought to use a hybrid of convolutional neural network (CNN) for its feature extraction capabilities and long short-term memory (LSTM) neural network for its time series prediction. This comparative study aims to determine the predictability of stock …