Computer Sciences Commons^™

Iomt Malware Detection Approaches: Analysis And Research Challenges, Mohammad Wazid, Ashok Kumar Das, Joel J.P.C. Rodrigues, Sachin Shetty, Youngho Park

VMASC Publications

The advancement in Information and Communications Technology (ICT) has changed the entire paradigm of computing. Because of such advancement, we have new types of computing and communication environments, for example, Internet of Things (IoT) that is a collection of smart IoT devices. The Internet of Medical Things (IoMT) is a specific type of IoT communication environment which deals with communication through the smart healthcare (medical) devices. Though IoT communication environment facilitates and supports our day-to-day activities, but at the same time it has also certain drawbacks as it suffers from several security and privacy issues, such as replay, man-in-the-middle, impersonation, …

A Machine Learning Assessment To Predict The Sediment Transport Rate Under Oscillating Sheet Flow Conditions, Huy Vu

Senior Honors Theses

The two-phase flow approach has been the conventional method designed to study the sediment transport rate. Due to the complexity of sediment transport, the precisely numerical models computed from that approach require initial assumptions and, as a result, may not yield accurate output for all conditions. This research work proposes that Machine Learning algorithms can be an alternative way to predict the processes of sediment transport in two-dimensional directions under oscillating sheet flow conditions, by utilizing the available dataset of the SedFoam multidimensional two-phase model. The assessment utilized linear regression and gradient boosting algorithm to analyze the lowest average mean …

Ldakm-Eiot: Lightweight Device Authentication And Key Management Mechanism For Edge-Based Iot Deployment, Mohammad Wazid, Ashok Kumar Das, Sachin Shetty, Joel J. P. C. Rodrigues, Youngho Park

VMASC Publications

In recent years, edge computing has emerged as a new concept in the computing paradigm that empowers several future technologies, such as 5G, vehicle-to-vehicle communications, and the Internet of Things (IoT), by providing cloud computing facilities, as well as services to the end users. However, open communication among the entities in an edge based IoT environment makes it vulnerable to various potential attacks that are executed by an adversary. Device authentication is one of the prominent techniques in security that permits an IoT device to authenticate mutually with a cloud server with the help of an edge node. If authentication …

Series Of Divergence Measures Of Type K, Information Inequalities And Particular Cases, R. N. Saraswat, Ajay Tak

Applications and Applied Mathematics: An International Journal (AAM)

Information and Divergence measures deals with the study of problems concerning information processing, information storage, information retrieval and decision making. The purpose of this paper is to find a new series of divergence measures and their applications, discuss the mathematical tools for finding convexity of the functions. Applications of convex functions in information theory, relationship between new and well-known divergence measures are discussed. Also some new bounds have been established for divergence measures using new f divergence measures and its properties.

Detection And Mitigation Of Attacks In Nonlinear Stochastic System Using Modified Detector, Chandreyee Bhowmick, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

A novel attack detection method is presented for a nonlinear system with known dynamics using the measured output in the presence of additive process and measurement noise. False data injection (FDI) and replay attacks are considered using a modified fault detector. The difference between the measured and the estimated output from an adaptive observer, often known as the innovation signal, is generated and shown to have a Gaussian distribution with non-zero mean. This innovation signal in conjunction with the modified detector is utilized to detect attacks under a stable controller using the estimated state vector. Unlike FDI attack, where the …

Feasibility And Acceptability Of A Rural, Pragmatic, Telemedicine‐ Delivered Healthy Lifestyle Programme, John A. Batsis, Auden C. Mcclure, Aaron B. Weintraub, David F. Kotz, Sivan Rotenberg, Summer B. Cook, Diane Gilbert-Diamond, Kevin Curtis, Courtney J. Stevens, Diane Sette, Richard I. Rothstein

Dartmouth Scholarship

Background: The public health crisis of obesity leads to increasing morbidity that are even more profound in certain populations such as rural adults. Live, two‐way video‐conferencing is a modality that can potentially surmount geographic barriers and staffing shortages. Methods: Patients from the Dartmouth‐Hitchcock Weight and Wellness Center were recruited into a pragmatic, single‐arm, nonrandomized study of a remotely delivered 16‐week evidence‐based healthy lifestyle programme. Patients were provided hardware and appropriate software allowing for remote participation in all sessions, outside of the clinic setting. Our primary outcomes were feasibility and acceptability of the telemedicine intervention, as well as potential effectiveness on …

Exploring The State-Of-Receptivity For Mhealth Interventions, Florian Künzler, Varun Mishra, Jan-Niklas Kramer, David Kotz, Elgar Fleisch, Tobias Kowatsch

Dartmouth Scholarship

Recent advancements in sensing techniques for mHealth applications have led to successful development and deployments of several mHealth intervention designs, including Just-In-Time Adaptive Interventions (JITAI). JITAIs show great potential because they aim to provide the right type and amount of support, at the right time. Timing the delivery of a JITAI such as the user is receptive and available to engage with the intervention is crucial for a JITAI to succeed. Although previous research has extensively explored the role of context in users’ responsiveness towards generic phone notiications, it has not been thoroughly explored for actual mHealth interventions. In this …

Applying Formal Methods For Integrating Advanced Algorithms In Safety Critical Systems, Milton Stafford

Theses and Dissertations

In software engineering it is essential that updates are deployed for continual improvement. While software updates bring new functionality, updates also may introduce instability. This leads to failures of various kinds. This is especially problematic in safety-critical systems where there is a potential for injury or loss of life. However, newer and more sophisticated software carries potential advantages, including higher performance and reliability. Therefore, there are benefits in adopting newer software if the integration process is assured. In this thesis, I present a framework for assured integration; one that links requirements, design, and implementation. The proposed framework includes a new …

Building Datasets From Publicly Accessible Social Media Images For Biometric Analysis, Giordano Roberto Benitez Torres

Theses and Dissertations

The world is developing at a rapid pace, and some of its advancement can be accredited to technological innovations that are affecting many aspects of society. A field within computer science that is increasingly reaching many industries is Biometrics, specifically the area face recognition. Researchers, scientists, and organizations actively try to improve the performance of tools and algorithms. Nevertheless, for it to occur, there is a need for high-quality datasets to test and develop new techniques. Never had humanity, in the course of the history of civilization, produced massive amounts of data as it currently does. Social media networks play …

Comparison Of Rl Algorithms For Learning To Learn Problems, Adolfo Gonzalez Iii

Theses and Dissertations

Machine learning has been applied to many different problems successfully due to the expressiveness of neural networks and simplicity of first order optimization algorithms. The latter being a vital piece needed for training large neural networks efficiently. Many of these algorithms were produced with behavior produced by experiments and intuition. An interesting question that comes to mind is that rather than observing and then designing algorithms with beneficial behaviors, can these algorithms be learned through a reinforcement learning by modeling optimization as a game. This paper explores several reinforcement learning algorithms which are applied to learn policies suited for optimization.

Detecting Phone-Related Pedestrian Behavior Using A Two-Branch Convolutional Neural Network, Humberto Saenz

Theses and Dissertations

With the wide use of smart phones, distraction has become a major safety concern to roadway users. The distracted phone-use behaviors among pedestrians, like Texting, Game Playing and Phone Calls, have caused increasing fatalities and serious injuries. With the increasing usage of driver monitor systems on intelligent vehicles, distracted driver behaviors can be efficiently detected and warned. However, the research of phone-related distracted behavior by pedestrians has not been systemically studied. It is desired to improve both the driving and pedestrian safety by automatically discovering the phone-related pedestrian distracted behaviors. In this thesis, we propose a new computer vision-based method …

Strongly Secure Authenticated Key Exchange From Supersingular Isogenies, Xiu Xu, Haiyang Xue, Kunpeng Wang, Ho Man Au, Song Tian

Research Collection School Of Computing and Information Systems

This paper aims to address the open problem, namely, to find new techniques to design and prove security of supersingular isogeny-based authenticated key exchange (AKE) protocols against the widest possible adversarial attacks, raised by Galbraith in 2018. Concretely, we present two AKEs based on a double-key PKE in the supersingular isogeny setting secure in the sense of CK+, one of the strongest security models for AKE. Our contributions are summarised as follows. Firstly, we propose a strong OW-CPA secure PKE, 2PKEsidh, based on SI-DDH assumption. By applying modified Fujisaki-Okamoto transformation, we obtain a [OW-CCA, OW-CPA] secure KEM, 2KEMsidh. Secondly, we …

Hybrid Recommender Systems Via Spectral Learning And A Random Forest, Alyssa Williams

Electronic Theses and Dissertations

We demonstrate spectral learning can be combined with a random forest classifier to produce a hybrid recommender system capable of incorporating meta information. Spectral learning is supervised learning in which data is in the form of one or more networks. Responses are predicted from features obtained from the eigenvector decomposition of matrix representations of the networks. Spectral learning is based on the highest weight eigenvectors of natural Markov chain representations. A random forest is an ensemble technique for supervised learning whose internal predictive model can be interpreted as a nearest neighbor network. A hybrid recommender can be constructed by first …

Objective Sleep Quality As A Predictor Of Mild Cognitive Impairment In Seniors Living Alone, Brian Chen, Hwee-Pink Tan, Irus Rawtaer, Hwee Xian Tan

Research Collection School Of Computing and Information Systems

Singapore has the fastest ageing population in the Asia Pacific region, with an estimated 82,000 seniors living with dementia. These figures are projected to increase to more than 130,000 by 2030. The challenge is to identify more community dwelling seniors with Mild Cognitive Impairment (MCI), a prodromal state, as it provides an opportunity for evidence-based early intervention to delay the onset of dementia. In this paper, we explore the use of Internet of Things (IoT) systems in detecting MCI symptoms in seniors who are living alone, and accurately grouping them into MCI positive and negative subjects. We present feature extraction …

The Information Disclosure Trilemma: Privacy, Attribution And Dependency, Ping Fan Ke

Research Collection School Of Computing and Information Systems

Information disclosure has been an important mechanism to increase transparency and welfare in various contexts, from rating a restaurant to whistleblowing the wrongdoing of government agencies. Yet, the author often needs to be sacrificed during information disclosure process – an anonymous disclosure will forgo the reputation and compensation whereas an identifiable disclosure will face the threat of retaliation. On the other hand, the adoption of privacy-enhancing technologies (PETs) lessens the tradeoff between privacy and attribution while introducing dependency and potential threats. This study will develop the desirable design principles and possible threats of an information disclosure system, and discuss how …

Treecaps: Tree-Structured Capsule Networks For Program Source Code Processing, Vinoj Jayasundara, Duy Quoc Nghi Bui, Lingxiao Jiang, David Lo

Research Collection School Of Computing and Information Systems

Program comprehension is a fundamental task in software development and maintenance processes. Software developers often need to understand a large amount of existing code before they can develop new features or fix bugs in existing programs. Being able to process programming language code automatically and provide summaries of code functionality accurately can significantly help developers to reduce time spent in code navigation and understanding, and thus increase productivity. Different from natural language articles, source code in programming languages often follows rigid syntactical structures and there can exist dependencies among code elements that are located far away from each other through …

When Keystroke Meets Password: Attacks And Defenses, Ximing Liu

Dissertations and Theses Collection (Open Access)

Password is a prevalent means used for user authentication in pervasive computing environments since it is simple to be deployed and convenient to use. However, the use of password has intrinsic problems due to the involvement of keystroke. Keystroke behaviors may emit various side-channel information, including timing, acoustic, and visual information, which can be easily collected by an adversary and leveraged for the keystroke inference. On the other hand, those keystroke-related information can also be used to protect a user's credentials via two-factor authentication and biometrics authentication schemes. This dissertation focuses on investigating the PIN inference due to the side-channel …

Gesture-Based Profiling Of Commonplace Lifestyle And Physical Activity Behaviors, Meeralakshmi Radhakrishnan

Dissertations and Theses Collection (Open Access)

The widespread availability of sensors on personal devices (e.g., smartphones, smartwatches) and other cheap, commoditized IoT devices in the environment has opened up the opportunity for developing applications that capture and enhance various lifestyle-driven daily activities of individuals. Moreover, there is a growing trend of leveraging ubiquitous computing technologies to improve physical health and wellbeing. Several of the lifestyle monitoring applications rely primarily on the capability of recognizing contextually relevant human movements, actions and gestures. As such, gesture recognition techniques, and gesture-based analytics have emerged as a fundamental component for realizing personalized lifestyle applications.

This thesis explores how such wealth …

Testing Isomorphism Of Graded Algebras, Peter A. Brooksbank, James B. Wilson, Eamonn A. O'Brien

Faculty Journal Articles

We present a new algorithm to decide isomorphism between finite graded algebras. For a broad class of nilpotent Lie algebras, we demonstrate that it runs in time polynomial in the order of the input algebras. We introduce heuristics that often dramatically improve the performance of the algorithm and report on an implementation in Magma.

Falcon: Framework For Anomaly Detection In Industrial Control Systems, Subin Sapkota

Boise State University Theses and Dissertations

Industrial Control Systems (ICS) are used to control physical processes in the nation's critical infrastructures. They are composed of subsystems that control physical processes by analyzing the information received from the sensors. Based on the state of the process, the controller issues control commands to the actuators. These systems are utilized in a wide variety of operations such as water treatment plants, power, and manufacturing, etc. While the safety and security of these systems are of high concern, recent reports have shown an increase in targeted attacks that are aimed at manipulating the physical processes to cause catastrophic consequences. This …

Event Reconstruction In The Advanced Particle-Astrophysics Telescope, Emily Ramey

McKelvey School of Engineering Graduate Student Theses & Dissertations

The Advanced Particle-Astrophysics Telescope (APT) is a concept for a gamma-ray space telescope operating in the keV to MeV energy range. Due to the nature of the telescope and the physics of detection, reconstructing initial photon trajectories can be very computationally complex. This is a barrier to the real-time detection of astrophysical transient phenomena such as Gamma Ray Bursts (GRBs), and a faster reconstruction algorithm is needed in order to effectively study them. In this project, we develop such an algorithm based on Boggs & Jean (2000) and discuss the effects of certain algorithmic parameters on computational performance. For testing, …

Multilingual Information Retrieval: A Representation Building Perspective, Ion Madrazo

Boise State University Theses and Dissertations

Information Retrieval (IR) has changed the way we access digital resources and satisfy our daily information needs. Popular IR tools like Search Engines, Recommendation Systems, and Automatic Question Answering sites, act as a deterrent for information overload while fostering (at least in theory) the democratization of access to resources. Yet, in their majority, IR tools are built with a traditional user in mind. This causes users who deviate from the norm, e.g., users with low educational background, visually-impaired users, or users who speak different languages, to be undeserved and thus struggle to find the information they require. In this manuscript, …

Detection And Countermeasure Of Saturation Attacks In Software-Defined Networks, Samer Yousef Khamaiseh

Boise State University Theses and Dissertations

The decoupling of control and data planes in software-defined networking (SDN) facilitates orchestrating the network traffic. However, SDN suffers from critical security issues, such as DoS saturation attacks on the data plane. These attacks can exhaust the SDN component resources, including the computational resources of the control plane, create a high packet loss rate and a long delay in delivering the OpenFlow messages due to the bandwidth consumption of the OpenFlow connection channel, and exhausting the buffer memory of the data plane.

Currently, most of the existing machine learning detection methods rely on a predefined time-window to start analyzing the …

Bullynet: Unmasking Cyberbullies On Social Networks, Aparna Sankaran

Boise State University Theses and Dissertations

Social media has changed the way people communicate with each other, and consecutively affected people's ability to empathize in both positive and negative ways. One of the most harmful consequences of social media is the rise of cyberbullying, which tends to be more sinister than traditional bullying given that online records typically live on the internet for quite a long time and are hard to control. In this thesis, we present a three-phase algorithm, called BullyNet, for detecting cyberbullies on Twitter social network. We exploit bullying tendencies by proposing a robust method for constructing a cyberbullying signed network. BullyNet analyzes …

Social Groups Characterization And Dynamics: A Network Science Approach, Josemar Faustino Da Cruz

Theses and Dissertations

Over the course of our lives, we tend to transition through many social groups. From an early age, our first social group is our family, then we have our schoolmates, later in life, our coworkers, and likely another family. As we move from one group to another, we embed ourselves in the dynamics of social groups’ losses and gains of new members. These dynamics are a complex system of interactions, which at scale, form the structural basis of our societies. In many years of sociological research, the details of social group interactions remained poorly understood. Certainly, not because of lack …

An Ai Approach To Measuring Financial Risk, Lining Yu, Wolfgang Karl Hardle, Lukas Borke, Thijs Benschop

Sim Kee Boon Institute for Financial Economics

AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here, we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (λ" role="presentation" style="box-sizing: border-box; display: inline; font-style: normal; font-weight: normal; line-height: normal; font-size: 18px; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">λλ) of a linear quantile lasso regression. The FRM is calculated by taking the average …

Improving The Classification Of Tiny Images For Forensic Analysis, Roba Jafar Alharbi

Theses and Dissertations

Forensics can be defined as the approach that connects with and uses in governments and different organizations in order to detect any malicious activity. Digital forensics has become an essential approach to cyber investigation. Image forensics is one of the most beneficial ways that are used in digital forensics in order to help investigators in cybercrimes. Therefore, investigators can discover some new evidence besides what is already available on their systems when they use some digital forensics techniques. This thesis focuses on identifying an image based on its contents, especially tiny images. We investigated ways to improve the performance of …

Pieces Of Contextual Information Suitable For Predicting Co-Changes? An Empirical Study, Igor Scaliante Wiese, Rodrigo Takashi Kuroda, Igor Steinmacher, Gustavo A. Oliva, Reginaldo Ré, Christoph Treude, Marco Aurélio Gerosa

Research Collection School Of Computing and Information Systems

Models that predict software artifact co-changes have been proposed to assist developers in altering a software system and they often rely on coupling. However, developers have not yet widely adopted these approaches, presumably because of the high number of false recommendations. In this work, we conjecture that the contextual information related to software changes, which is collected from issues (e.g., issue type and reporter), developers’ communication (e.g., number of issue comments, issue discussants and words in the discussion), and commit metadata (e.g., number of lines added, removed, and modified), improves the accuracy of co-change prediction. We built customized prediction models …

Efficient Meta Learning Via Minibatch Proximal Update, Pan Zhou, Xiao-Tong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng

Research Collection School Of Computing and Information Systems

We address the problem of meta-learning which learns a prior over hypothesis from a sample of meta-training tasks for fast adaptation on meta-testing tasks. A particularly simple yet successful paradigm for this research is model-agnostic meta-learning (MAML). Implementation and analysis of MAML, however, can be tricky; first-order approximation is usually adopted to avoid directly computing Hessian matrix but as a result the convergence and generalization guarantees remain largely mysterious for MAML. To remedy this deficiency, in this paper we propose a minibatch proximal update based meta-learning approach for learning to efficient hypothesis transfer. The principle is to learn a prior …

Finding Needles In A Haystack: Leveraging Co-Change Dependencies To Recommend Refactorings, Marcos César De Oliveira, Davi Freitas, Rodrigo Bonifacio, Gustavo Pinto, David Lo

Research Collection School Of Computing and Information Systems

A fine-grained co-change dependency arises when two fine-grained source-code entities, e.g., a method,change frequently together. This kind of dependency is relevant when considering remodularization efforts (e.g., to keep methods that change together in the same class). However, existing approaches forrecommending refactorings that change software decomposition (such as a move method) do not explorethe use of fine-grained co-change dependencies. In this paper we present a novel approach for recommending move method and move field refactorings, which removes co-change dependencies and evolutionary smells, a particular type of dependency that arise when fine-grained entities that belong to different classes frequently change together. First …