Enabling Use Of Signal In A Disconnected Village Environment,
2022
San Jose State University
Enabling Use Of Signal In A Disconnected Village Environment, Evan Chopra
Master's Projects
A significant portion of the world still does not have a stable internet connection. Those people should have the ability to communicate with their loved ones who may not live near by or to share ideas with friends. To power this achievable reality, our lab has set out on making infrastructure for enabling delay tolerant applications. This network will communicate using existing smartphones that will relay the information to a connected environment. The proof of concept application our lab is using is Signal as it offers end to end encryption messaging and an open source platform our lab can develop.
5g Mobility Management Using Satellites In Leo (Low Earth Orbit),
2022
San Jose State University
5g Mobility Management Using Satellites In Leo (Low Earth Orbit), Peter Knight
Master's Projects
As technology moves forward we find ourselves demanding faster and better quality internet. Towards that end, 5G was developed. While cell towers are already being built with 5G in mind, some of the built-in limitations of 5G mean that we may be seeing satellites with it in the near future. This development would require a drastic change in the current algorithms used for handovers. These algorithms fail to take into account the high relative velocity of the satellites and result in many unnecessary handovers which increases the likelihood of a dropped connection. This paper will propose and evaluate the effectiveness …
Hard Real-Time Linux On A Raspberry Pi For 3d Printing,
2022
San Jose State University
Hard Real-Time Linux On A Raspberry Pi For 3d Printing, Alvin Nguyen
Master's Projects
The project presents how a Raspberry Pi with hard real-time enabled Linux can control stepper motors to operate the kinematics of a 3D (three-dimensional) printer. The consistent performance of the Raspberry Pi with the PREEMPT-RT (real-time) patch can satisfy real hard-time requirements for 3D printing kinematics, without introducing dedicated microcontrollers. The Klipper 3D printer firmware enables one of the Raspberry Pi processors to act as the Klipper MCU, the primary controller for the hardware components. This project introduces a software implementation of the control logic for controlling the stepper motors, which utilizes the PCA9685 pwm driver and TB6612 motor drivers …
Digital Forensics For Mobility As A Service Platform: Analysis Of Uber Application On Iphone And Cloud,
2022
Purdue University
Digital Forensics For Mobility As A Service Platform: Analysis Of Uber Application On Iphone And Cloud, Nina Matulis, Umit Karabiyik
Annual ADFSL Conference on Digital Forensics, Security and Law
Uber is a ride-hailing smartphone application (app) that allows users to order a ride in a highly efficient manner. The Uber app provides Mobility as a Service and allows users to easily order a ride in a private car with just a few clicks. Uber stores large amounts of data on both the mobile device the app is being used on, and in the cloud. Examples of this data include geolocation data, date/time, origin/destination addresses, departure/arrival times, and distance. Uber geolocation data has been previously researched to investigate the privacy of the Uber app; however, there is minimal research relating …
Smart Home Forensics: Identifying Ddos Attack Patterns On Iot Devices,
2022
Purdue University
Smart Home Forensics: Identifying Ddos Attack Patterns On Iot Devices, Samuel Ho, Hope Greeson, Umit Karabiyik
Annual ADFSL Conference on Digital Forensics, Security and Law
Smart homes are becoming more common as more people integrate IoT devices into their home environment. As such, these devices have access to personal data on their homeowners’ networks. One of the advantages of IoT devices is that they are compact. However, this limits the incorporation of security measures in their hardware. Misconfigured IoT devices are commonly the target of malicious attacks. Additionally, distributed denial-of-service attacks are becoming more common due to applications and software that provides users with easy-to-use user interfaces. Since one vulnerable device is all an attacker needs to launch an attack on a network, in regards …
Microsoft Defender Will Be Defended: Memoryranger Prevents Blinding Windows Av,
2022
Bachelor of Information Security, MEPhI; Moscow, Russia
Microsoft Defender Will Be Defended: Memoryranger Prevents Blinding Windows Av, Denis Pogonin, Igor Korkin, Phd
Annual ADFSL Conference on Digital Forensics, Security and Law
Windows OS is facing a huge rise in kernel attacks. An overview of popular techniques that result in loading kernel drivers will be presented. One of the key targets of modern threats is disabling and blinding Microsoft Defender, a default Windows AV. The analysis of recent driver-based attacks will be given, the challenge is to block them. The survey of user- and kernel-level attacks on Microsoft Defender will be given. One of the recently published attackers’ techniques abuses Mandatory Integrity Control (MIC) and Security Reference Monitor (SRM) by modifying Integrity Level and Debug Privileges for the Microsoft Defender via syscalls. …
Detection Of Overlapping Passive Manipulation Techniques In Image Forensics,
2022
Purdue University
Detection Of Overlapping Passive Manipulation Techniques In Image Forensics, Gianna S. Lint, Umit Karabiyik
Annual ADFSL Conference on Digital Forensics, Security and Law
With a growing number of images uploaded daily to social media sites, it is essential to understand if an image can be used to trace its origin. Forensic investigations are focusing on analyzing images that are uploaded to social media sites resulting in an emphasis on building and validating tools. There has been a strong focus on understanding active manipulation or tampering techniques and building tools for analysis. However, research on manipulation is often studied in a vacuum, involving only one technique at a time. Additionally, less focus has been placed on passive manipulation, which can occur by simply uploading …
Taming The Data In The Internet Of Vehicles,
2022
California State University, Fresno
Taming The Data In The Internet Of Vehicles, Shahab Tayeb
Mineta Transportation Institute
As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize …
Automating App Review Response Generation Based On Contextual Knowledge,
2022
Singapore Management University
Automating App Review Response Generation Based On Contextual Knowledge, Cuiyun Gao, Wenjie Zhou, Xin Xia, David Lo, Qi Xie, Michael R. Lyu
Research Collection School Of Computing and Information Systems
User experience of mobile apps is an essential ingredient that can influence the user base and app revenue. To ensure good user experience and assist app development, several prior studies resort to analysis of app reviews, a type of repository that directly reflects user opinions about the apps. Accurately responding to the app reviews is one of the ways to relieve user concerns and thus improve user experience. However, the response quality of the existing method relies on the pre-extracted features from other tools, including manually labelled keywords and predicted review sentiment, which may hinder the generalizability and flexibility of …
Contextualized Knowledge-Aware Attentive Neural Network: Enhancing Answer Selection With Knowledge,
2022
Singapore Management University
Contextualized Knowledge-Aware Attentive Neural Network: Enhancing Answer Selection With Knowledge, Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Wai Lam, Ying Shen
Research Collection School Of Computing and Information Systems
Answer selection, which is involved in many natural language processing applications, such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge. In this article, we extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG). First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network, which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information. Then, we develop two kinds of knowledge-aware …
Machine Learning-Based Event Generator For Electron-Proton Scattering,
2022
Old Dominion University
Machine Learning-Based Event Generator For Electron-Proton Scattering, Y. Alanazi, P. Ambrozewicz, M. Battaglieri, A.N. Hiller Blin, M. P. Kuchera, Y. Li, T. Liu, R. E. Mcclellan, W. Melnitchouk, E. Pritchard, M. Robertson, N. Sato, R. Strauss, L. Velasco
Computer Science Faculty Publications
We present a new machine learning-based Monte Carlo event generator using generative adversarial networks (GANs) that can be trained with calibrated detector simulations to construct a vertex-level event generator free of theoretical assumptions about femtometer scale physics. Our framework includes a GAN-based detector folding as a fast-surrogate model that mimics detector simulators. The framework is tested and validated on simulated inclusive deep-inelastic scattering data along with existing parametrizations for detector simulation, with uncertainty quantification based on a statistical bootstrapping technique. Our results provide for the first time a realistic proof of concept to mitigate theory bias in inferring vertex-level event …
Detecting The Presence Of Electronic Devices In Smart Homes Using Harmonic Radar,
2022
Dartmouth College
Detecting The Presence Of Electronic Devices In Smart Homes Using Harmonic Radar, Beatrice Perez, Gregory Mazzaro, Timothy J. Pierson, David Kotz
Dartmouth Scholarship
Data about users is collected constantly by phones, cameras, Internet websites, and others. The advent of so-called ‘Smart Things' now enable ever-more sensitive data to be collected inside that most private of spaces: the home. The first step in helping users regain control of their information (inside their home) is to alert them to the presence of potentially unwanted electronics. In this paper, we present a system that could help homeowners (or home dwellers) find electronic devices in their living space. Specifically, we demonstrate the use of harmonic radars (sometimes called nonlinear junction detectors), which have also been used in …
Achieving Fairness Through Load-Balancing In Social Cloud Computing Networks,
2021
San Jose State University
Achieving Fairness Through Load-Balancing In Social Cloud Computing Networks, Kaiyi Huang
Master's Projects
Cloud-based computing networks have taken over the digital landscape. From small non-profits to large multinational corporations, more and more entities have been offloading computing effort to the cloud in order to take advantage of the increased cost-efficiency and scalability of cloud computing. One of the new types of cloud that have emerged is the P2P cloud, which disengages from a traditional datacenter setup by allowing users to instead share their own computing hardware into a cloud to take advantage of cloud computing’s advantages at an even lower cost. However, this new paradigm comes with a slew of challenges, notably, security …
Dynamic Resource Management Of Fog-Cloud Computing For Iot Support,
2021
San Jose State University
Dynamic Resource Management Of Fog-Cloud Computing For Iot Support, Mariia Surmenok
Master's Projects
The internet of things (IoT) is an integrated part of contemporary life. It includes wearable devices, such as smart watches and cell phones, as well as sensors for Smart City. Fog computing can improve the efficiency and battery life of IoT devices by offloading tasks to fog cloud. It is important to have fog clusters near the IoT device for faster data offload. The goal of this project is to develop dynamic resource allocation for on-demand fog computing cluster to efficiently deploy tasks from IoT. This report studies the different research papers about the current state of resource management in …
Comparison Of Major Cloud Providers,
2021
Harrisburg University of Science and Technology
Comparison Of Major Cloud Providers, Justin Berman
Other Student Works
This paper will compare the following major cloud providers: Microsoft Azure, Amazon AWS, Google Cloud, and IBM Cloud. An introduction to the companies and their history, fundamentals and services, strengths and weaknesses, costs, and their security will be discussed throughout this writing.
Infinite Time Horizon Safety Of Bayesian Neural Networks,
2021
Singapore Management University
Infinite Time Horizon Safety Of Bayesian Neural Networks, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. Henzinger
Research Collection School Of Computing and Information Systems
Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network’s prediction. We consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with infinite time horizon systems. Compared to the existing sampling-based approaches, which are inapplicable to the infinite time horizon setting, we train a separate deterministic neural network that serves as an infinite time horizon safety certificate. In particular, we show that the certificate network guarantees the safety of the system over a subset of the BNN weight posterior’s support. …
Ggnb: Graph-Based Gaussian Naive Bayes Intrusion Detection System For Can Bus,
2021
University of Maryland - Baltimore County
Ggnb: Graph-Based Gaussian Naive Bayes Intrusion Detection System For Can Bus, Riadul Islam, Maloy K. Devnath, Manar D. Samad, Syed Md Jaffrey Al Kadry
Computer Science Faculty Research
The national highway traffic safety administration (NHTSA) identified cybersecurity of the automobile systems are more critical than the security of other information systems. Researchers already demonstrated remote attacks on critical vehicular electronic control units (ECUs) using controller area network (CAN). Besides, existing intrusion detection systems (IDSs) often propose to tackle a specific type of attack, which may leave a system vulnerable to numerous other types of attacks. A generalizable IDS that can identify a wide range of attacks within the shortest possible time has more practical value than attack-specific IDSs, which is not a trivial task to accomplish. In this …
System Design And Optimization For Efficient Flash-Based Caching In Data Centers,
2021
Louisiana State University and Agricultural and Mechanical College
System Design And Optimization For Efficient Flash-Based Caching In Data Centers, Jian Liu
LSU Doctoral Dissertations
Modern data centers are the backbone of today’s Internet-based services and applications. With the explosive growth of the Internet data and a wider range of data-intensive applications being deployed, it is increasingly challenging for data centers to satisfy the ever-increasing demand for high-quality data services. To relieve the heavy burden on data center systems and accelerate data processing, a popular cost-efficient solution is to deploy high-speed, large-capacity flash-based cache systems. However, we are facing multiple critical challenges from device hardware, systems, to application workloads. In this dissertation, we focus on designing highly efficient caching solutions to cope with the explosive …
Representation Learning On Multi-Layered Heterogeneous Network,
2021
Singapore Management University
Representation Learning On Multi-Layered Heterogeneous Network, Delvin Ce Zhang, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Network data can often be represented in a multi-layered structure with rich semantics. One example is e-commerce data, containing user-user social network layer and item-item context layer, with cross-layer user-item interactions. Given the dual characters of homogeneity within each layer and heterogeneity across layers, we seek to learn node representations from such a multi-layered heterogeneous network while jointly preserving structural information and network semantics. In contrast, previous works on network embedding mainly focus on single-layered or homogeneous networks with one type of nodes and links. In this paper we propose intra- and cross-layer proximity concepts. Intra-layer proximity simulates propagation along …
Sofi: Reflection-Augmented Fuzzing For Javascript Engines,
2021
Singapore Management University
Sofi: Reflection-Augmented Fuzzing For Javascript Engines, Xiaoyu He, Xiaofei Xie, Yuekang Li, Jianwen Sun, Feng Li, Wei Zou, Yang Liu, Lei Yu, Jianhua Zhou, Wenchang Shi, Wei Huo
Research Collection School Of Computing and Information Systems
JavaScript engines have been shown prone to security vulnerabilities, which can lead to serious consequences due to their popularity. Fuzzing is an effective testing technique to discover vulnerabilities. The main challenge of fuzzing JavaScript engines is to generate syntactically and semantically valid inputs such that deep functionalities can be explored. However, due to the dynamic nature of JavaScript and the special features of different engines, it is quite challenging to generate semantically meaningful test inputs.We observed that state-of-the-art semantic-aware JavaScript fuzzers usually require manually written rules to analyze the semantics for a JavaScript engine, which is labor-intensive, incomplete and engine-specific. …
