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- Autoencoder; BotNets; Cyber Security; Ensemble Learning; Intrusion Detection; Machine Learning (1)
- Bound states in the continuum (1)
- Brain-inspired; catastrophic forgetting; class-incremental learning; Continual learning; feature distillation; feature re-consolidation; neuroscience-inspired; rehearsal-based learning strategies (1)
- Carbide-derived carbon (1)
- Cavity resonators (1)
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- Continual learning algorithms (1)
- Convolutional neural networks (1)
- Cyberattacks (1)
- Deep learning (1)
- Dielectric scatters (1)
- Electrochemical etching (1)
- Embedded eigenstates (1)
- Ferroelectric materials (1)
- Ferromagnetic materials (1)
- Image processing; image sensing; machine learning; neural networks (1)
- Malicious attacks (1)
- Malware analysis (1)
- Malware classification (1)
- Malware detection (1)
- Malware visualization (1)
- Ransomeware (1)
- Supercapacitor (1)
- Thanos (1)
- Threat (1)
- Virtual machine (1)
- Publication
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Articles 1 - 11 of 11
Full-Text Articles in Physical Sciences and Mathematics
Federated Learning Based Autoencoder Ensemble System For Malware Detection On Internet Of Things Devices, Steven Edward Arroyo
Federated Learning Based Autoencoder Ensemble System For Malware Detection On Internet Of Things Devices, Steven Edward Arroyo
Theses and Dissertations
New technologies are being introduced at a rate faster than ever before and smaller in size. Due to the size of these devices, security is often difficult to implement. The existing solution is a firewall-segmented “IoT Network” that only limits the effect of these infected devices on other parts of the network. We propose a lightweight unsupervised hybrid-cloud ensemble anomaly detection system for malware detection. We perform transfer learning using a generalized model trained on multiple IoT device sources to learn network traffic on new devices with minimal computational resources. We further extend our proposed system to utilize federated learning …
Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco
Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco
Theses and Dissertations
Artificial Intelligence (AI) has exploded into mainstream consciousness with commercial investments exceeding $90 billion in the last year alone. Inasmuch as consumer-facing applications such ChatGPT offer astounding access to algorithms that were hitherto restricted to academic research labs, public focus of attention on AI has created an avalanche of misinformation. The nexus of investor-driven hype, “surprising” inaccuracies in the answers provided by AI models – now anthropomorphically labeled as “hallucinations”, and impending legislation by well-meaning and concerned governments has resulted in a crisis of confidence in the science of AI. The primary driver for AI’s recent growth is the convergence …
Brain-Inspired Continual Learning: Robust Feature Distillation And Re-Consolidation For Class Incremental Learning, Hikmat Khan, Nidhal Carla Bouaynaya, Ghulam Rasool
Brain-Inspired Continual Learning: Robust Feature Distillation And Re-Consolidation For Class Incremental Learning, Hikmat Khan, Nidhal Carla Bouaynaya, Ghulam Rasool
Henry M. Rowan College of Engineering Faculty Scholarship
Artificial intelligence and neuroscience have a long and intertwined history. Advancements in neuroscience research have significantly influenced the development of artificial intelligence systems that have the potential to retain knowledge akin to humans. Building upon foundational insights from neuroscience and existing research in adversarial and continual learning fields, we introduce a novel framework that comprises two key concepts: feature distillation and re-consolidation. The framework distills continual learning (CL) robust features and rehearses them while learning the next task, aiming to replicate the mammalian brain's process of consolidating memories through rehearsing the distilled version of the waking experiences. Furthermore, the proposed …
Adversary Aware Continual Learning, Muhammad Umer
Adversary Aware Continual Learning, Muhammad Umer
Theses and Dissertations
Continual learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (classes). However, these approaches are adversary agnostic, i.e., they do not consider the possibility of malicious attacks. In this dissertation, we have demonstrated that continual learning approaches are extremely vulnerable to the adversarial backdoor attacks, where an intelligent adversary can introduce small amount of misinformation to the model in the form of imperceptible backdoor pattern during training to cause deliberate forgetting of a specific class at test time. We then propose a novel defensive framework to counter …
Using Dielectric Scatters To Selectively Excite Embedded Eigenstates In Cavity Resonators, Olugbenga Joshua Gbidi
Using Dielectric Scatters To Selectively Excite Embedded Eigenstates In Cavity Resonators, Olugbenga Joshua Gbidi
Theses and Dissertations
Bound states in the continuum (BICs) are waves that remain in the continuous spectrum of radiating waves that carry energy, however, still localized within the spectrum. BICs, also embedded eigenmodes, exhibit high quality factors that have been observed in optical and acoustic waveguides, photonic structures, and other material systems. Presently, there are limited means to select these BICs in terms of the quality factor and their excitation. In this work, we show that a different type of BIC, Quasi-BICs (Q-BICs), in open resonators can have their quality attuned by introducing embedded scatters. Using microwave cavities and dielectric scatters as an …
A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription, Fowad Shahid Sohail
A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription, Fowad Shahid Sohail
Theses and Dissertations
Advances in Artificial Intelligence and Machine learning have enabled a variety of new technologies. One such technology is Automatic Speech Recognition (ASR), where a machine is given audio and transcribes the words that were spoken. ASR can be applied in a variety of domains to improve general usability and safety. One such domain is Air Traffic Control (ATC). ASR in ATC promises to improve safety in a mission critical environment. ASR models have historically required a large amount of clean training data. ATC environments are noisy and acquiring labeled data is a difficult, expertise dependent task. This thesis attempts to …
Malware Binary Image Classification Using Convolutional Neural Networks, John Kiger, Shen-Shyang Ho, Vahid Heydari
Malware Binary Image Classification Using Convolutional Neural Networks, John Kiger, Shen-Shyang Ho, Vahid Heydari
Faculty Scholarship for the College of Science & Mathematics
The persistent shortage of cybersecurity professionals combined with enterprise networks tasked with processing more data than ever before has led many cybersecurity experts to consider automating some of the most common and time-consuming security tasks using machine learning. One of these cybersecurity tasks where machine learning may prove advantageous is malware analysis and classification. To evade traditional detection techniques, malware developers are creating more complex malware. This is achieved through more advanced methods of code obfuscation and conducting more sophisticated attacks. This can make the manual process of analyzing malware an infinitely more complex task. Furthermore, the proliferation of malicious …
Technical Analysis Of Thanos Ransomware, Ikuromor Ogiriki, Christopher Beck, Vahid Heydari
Technical Analysis Of Thanos Ransomware, Ikuromor Ogiriki, Christopher Beck, Vahid Heydari
Faculty Scholarship for the College of Science & Mathematics
Ransomware is a developing menace that encrypts users’ files and holds the decryption key hostage until the victim pays a ransom. This particular class of malware has been in charge of extortion hundreds of millions of dollars every year. Adding to the problem, generating new variations is cheap. Therefore, new malware can detect antivirus and intrusion detection systems and evade them or manifest in ways to make themselves undetectable. We must first understand the characteristics and behavior of various varieties of ransomware to create and construct effective security mechanisms to combat them. This research presents a novel dynamic and behavioral …
Simultaneous Stress And Field Control Of Sustainable Switching Of Ferroelectric Phases, P. Finkel, M. Staruch, A. Amin, M. Ahart, Samuel E. Lofland
Simultaneous Stress And Field Control Of Sustainable Switching Of Ferroelectric Phases, P. Finkel, M. Staruch, A. Amin, M. Ahart, Samuel E. Lofland
Faculty Scholarship for the College of Science & Mathematics
In ferroelectrics, manifestation of a strong electromechanical coupling is attributed to both engineered domain morphology and phase transformations. However, realization of large sustainable and reversible strains and polarization rotation has been limited by fatigue, nonlinearity and hysteresis losses. Here, we demonstrate that large strain and polarization rotation can be generated for over 40 × 106 cycles with little fatigue by realization of a reversible ferroelectric-ferroelectric phase transition in [011] cut Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3 (PIN-PMN-PT) relaxor ferroelectric single crystal. Direct tuning of this effect through combination of stress and applied electric field, confirmed both macroscopically and microscopically with x-ray and Raman scattering, reveals …
Carbide-Derived Carbon By Electrochemical Etching Of Vanadium Carbides, Luis G.B. Camargo, Benjamin G. Palazzo, Greg Taylor, Zach A. Norris, Yash K. Patel, Jeffrey D. Hettinger, Lei Yu
Carbide-Derived Carbon By Electrochemical Etching Of Vanadium Carbides, Luis G.B. Camargo, Benjamin G. Palazzo, Greg Taylor, Zach A. Norris, Yash K. Patel, Jeffrey D. Hettinger, Lei Yu
Faculty Scholarship for the College of Science & Mathematics
Carbide-derived Carbon (CDC) has been demonstrated to be an excellent electrode material for electrochemical devices including supercapacitors due to its chemical and electrochemical stability, large specific surface area and controllable pore size and morphology. Currently, CDC is prepared from metal carbides by chlorination in a chlorine gas atmosphere at temperatures of 350°C or higher. In this paper, conversion using electrochemical methods is reported, which can be achieved by oxidizing vanadium carbides (VC or V2C) in aqueous solutions at room temperature and a mild electrode potential to prepare CDC thin film as electrode materials for “on-chip” supercapacitiors. It was …
Quad General Tree Drawing Algorithm And General Trees Characterization: Towards An Environment For The Experimental Study On General Tree Drawing Algorithms, Chu Yao
Theses and Dissertations
Information visualization produces (interactive) visual representations of abstract data to reinforce human cognition and perception; thus enabling the viewer to gain knowledge about the internal structure of the data and causal relationships in it. The visualization of information hierarchies is concerned with the presentation of abstract hierarchical information about relationships between various entities. It has many applications in diverse domains such as software engineering, information systems, biology, and chemistry. Information hierarchies are typically modeled by an abstract tree, where vertices are entities and edges represent relationships between entities. The aim of visualizing tree drawings is to automatically produce drawings of …