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Full-Text Articles in Computer Engineering
Path-Safe :Enabling Dynamic Mandatory Access Controls Using Security Tokens, James P. Maclennan
Path-Safe :Enabling Dynamic Mandatory Access Controls Using Security Tokens, James P. Maclennan
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Deploying Mandatory Access Controls (MAC) is a popular way to provide host protection against malware. Unfortunately, current implementations lack the flexibility to adapt to emergent malware threats and are known for being difficult to configure. A core tenet of MAC security systems is that the policies they are deployed with are immutable from the host while they are active. This work looks at deploying a MAC system that leverages using encrypted security tokens to allow for redeploying policy configurations in real-time without the need to stop a running process. This is instrumental in developing an adaptive framework for security systems …
Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman
Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman
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Insider threats to information security have become a burden for organizations. Understanding insider activities leads to an effective improvement in identifying insider attacks and limits their threats. This dissertation presents three systems to detect insider threats effectively. The aim is to reduce the false negative rate (FNR), provide better dataset use, and reduce dimensionality and zero padding effects. The systems developed utilize deep learning techniques and are evaluated using the CERT 4.2 dataset. The dataset is analyzed and reformed so that each row represents a variable length sample of user activities. Two data representations are implemented to model extracted features …
Solidity Compiler Version Identification On Smart Contract Bytecode, Lakshmi Prasanna Katyayani Devasani
Solidity Compiler Version Identification On Smart Contract Bytecode, Lakshmi Prasanna Katyayani Devasani
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Identifying the version of the Solidity compiler used to create an Ethereum contract is a challenging task, especially when the contract bytecode is obfuscated and lacks explicit metadata. Ethereum bytecode is highly complex, as it is generated by the Solidity compiler, which translates high-level programming constructs into low-level, stack-based code. Additionally, the Solidity compiler undergoes frequent updates and modifications, resulting in continuous evolution of bytecode patterns. To address this challenge, we propose using deep learning models to analyze Ethereum bytecodes and infer the compiler version that produced them. A large number of Ethereum contracts and the corresponding compiler versions is …
Encryption And Compression Classification Of Internet Of Things Traffic, Mariam Najdat M Saleh
Encryption And Compression Classification Of Internet Of Things Traffic, Mariam Najdat M Saleh
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The Internet of Things (IoT) is used in many fields that generate sensitive data, such as healthcare and surveillance. Increased reliance on IoT raised serious information security concerns. This dissertation presents three systems for analyzing and classifying IoT traffic using Deep Learning (DL) models, and a large dataset is built for systems training and evaluation. The first system studies the effect of combining raw data and engineered features to optimize the classification of encrypted and compressed IoT traffic using Engineered Features Classification (EFC), Raw Data Classification (RDC), and combined Raw Data and Engineered Features Classification (RDEFC) approaches. Our results demonstrate …
Few-Shot Malware Detection Using A Novel Adversarial Reprogramming Model, Ekula Praveen Kumar
Few-Shot Malware Detection Using A Novel Adversarial Reprogramming Model, Ekula Praveen Kumar
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The increasing sophistication of malware has made detecting and defending against new strains a major challenge for cybersecurity. One promising approach to this problem is using machine learning techniques that extract representative features and train classification models to detect malware in an early stage. However, training such machine learning-based malware detection models represents a significant challenge that requires a large number of high-quality labeled data samples while it is very costly to obtain them in real-world scenarios. In other words, training machine learning models for malware detection requires the capability to learn from only a few labeled examples. To address …
Realistic Virtual Human Character Design Strategy And Experience For Supporting Serious Role-Playing Simulations On Mobile Devices, Sindhu Kumari
Realistic Virtual Human Character Design Strategy And Experience For Supporting Serious Role-Playing Simulations On Mobile Devices, Sindhu Kumari
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Promoting awareness of social determinants of health (SDoH) among healthcare providers is important to improve the patient care experience and outcome as it helps providers understand their patients in a better way which can facilitate more efficient and effective communication about health conditions. Healthcare professionals are typically educated about SDoH through lectures, questionaries, or role-play-based approaches; but in today’s world, it is becoming increasingly possible to leverage modern technology to create more impactful and accessible tools for SDoH education. Wright LIFE (Lifelike Immersion for Equity) is a simulation-based training tool especially created for this purpose. It is a mobile app …
Kbot: Knowledge-Enabled Personalized Chatbot For Self-Management Of Asthma In Pediatric Population, Dipesh Kadariya
Kbot: Knowledge-Enabled Personalized Chatbot For Self-Management Of Asthma In Pediatric Population, Dipesh Kadariya
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Asthma, chronic pulmonary disease, is one of the major health issues in the United States. Given its chronic nature, the demand for continuous monitoring of patient’s adherence to the medication care plan, assessment of their environment triggers, and management of asthma control level can be challenging in traditional clinical settings and taxing on clinical professionals. A shift from a reactive to a proactive asthma care can improve health outcomes and reduce expenses. On the technology spectrum, smart conversational systems and Internet-of-Things (IoTs) are rapidly gaining popularity in the healthcare industry. By leveraging such technological prevalence, it is feasible to design …
Software Implementations And Applications Of Elliptic Curve Cryptography, Kirill Kultinov
Software Implementations And Applications Of Elliptic Curve Cryptography, Kirill Kultinov
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Elliptic Curve Cryptography (ECC) is a public-key cryptography system. Elliptic Curve Cryptography (ECC) can achieve the same level of security as the public-key cryptography system, RSA, with a much smaller key size. It is a promising public key cryptography system with regard to time efficiency and resource utilization. This thesis focuses on the software implementations of ECC over finite field GF(p) with two distinct implementations of the Big Integer classes using character arrays, and bit sets in C++ programming language. Our implementation works on the ECC curves of the form y^2 = x^3 + ax + b (mod p). The …
Tangible Interaction As An Aid For Object Navigation In 3d Modeling, Sanmathi Dangeti
Tangible Interaction As An Aid For Object Navigation In 3d Modeling, Sanmathi Dangeti
Open Access Theses
This study introduced an interaction technique that used tangible interaction for 3D modeling. A hybrid interaction technique using a Kinect camera and a smartphone with a gyroscope was developed for the navigating objects in a 3D modeling software. It was then tested on 20 participants categorized as amateurs who had basic 3D/ CAD modeling experience and 20 participants categorized as the experts who had extensive experience working with the modeling software. This research study presents the need for existence of such interaction technique, gaps from the related previous studies, statistical findings from the current study and possible reasons for the …
Using Ubiquitous Data To Improve Smartwatches' Context Awareness, Yuankun Song
Using Ubiquitous Data To Improve Smartwatches' Context Awareness, Yuankun Song
Open Access Theses
Nowadays, more and more data is being generated by various software applications, services and smart devices every second. The data contains abundant information about people’s daily lives. This research explored the possibility of improving smartwatches’ context awareness by using common ubiquitous data. The researcher developed a prototype system consisting of an Android application and a web application, and conducted an experiment where 10 participants performed several tasks with the help of a smartwatch. The result showed a significant improvement of the smartwatch’s context awareness running the prototype application, which used ubiquitous data to automatically execute proper actions according to contexts. …
Monitoring Dbms Activity To Detect Insider Threat Using Query Selectivity, Prajwal B. Hegde
Monitoring Dbms Activity To Detect Insider Threat Using Query Selectivity, Prajwal B. Hegde
Open Access Theses
The objective of the research presented in this thesis is to evaluate the importance of query selectivity for monitoring DBMS activity and detect insider threat. We propose query selectivity as an additional component to an existing anomaly detection system (ADS). We first look at the advantages of working with this particular ADS. This is followed by a discussion about some existing limitations in the anomaly detection system (ADS) and how it affects its overall performance. We look at what query selectivity is and how it can help improve upon the existing limitations of the ADS. The system is then implemented …
A Stochastic Petri Net Based Nlu Scheme For Technical Documents Understanding, Adamantia Psarologou
A Stochastic Petri Net Based Nlu Scheme For Technical Documents Understanding, Adamantia Psarologou
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Natural Language Understanding (NLU) is a very old research field, which deals with machine reading comprehension. Despite the many years of work and the numerous accomplishments by several researchers in the field, there is still place for significant improvements. Here, our goal is to develop a novel NLU methodology for detecting and extracting event/action associations in technical documents. In order to achieve this goal we present a synergy of methods (Kernel extraction, Formal Language Modeling, Stochastic Petri-nets (SPN) mapping and Event Representation via SPN graph synthesis). In particular, the basic meaning of a natural language sentence is given by its …