<p dir="ltr">Automated detection of machine-generated text has become increasingly crucial in various fields such as cybersecurity, journalism, and content moderation due to the proliferation of generated content, including fake news, spam, and bot-generated comments. Traditional methods for detecting such content often rely on rule-based systems or supervised learning approaches, which may struggle to adapt to evolving generation techniques and sophisticated manipulations. In this thesis, we explore the use of ensemble models and Generative Adversarial Networks (GANs) for the automated detection of machine-generated text. </p><p dir="ltr">Ensemble models combine the strengths of different approaches, such as utilizing both rule-based systems and machine learning algorithms, to enhance detection accuracy and robustness. We investigate the integration of linguistic features, syntactic patterns, and semantic cues into machine learning pipelines, leveraging the power of Natural Language Processing (NLP) techniques. By combining multiple modalities of information, Ensemble models can effectively capture the subtle characteristics and nuances inherent in machine-generated text, improving detection performance. </p><p dir="ltr">In my latest experiments, I examined the performance of a Random Forest classifier trained on TF-IDF representations in combination with RoBERTa embeddings to calculate probabilities for machine-generated text detection. Test1 results showed promising accuracy rates, indicating the effectiveness of combining TF-IDF with RoBERTa probabilities. Test2 further validated these findings, demonstrating improved detection performance compared to standalone approaches.<br></p><p dir="ltr">These results suggest that leveraging Random Forest TF-IDF representation with RoBERTa embeddings to calculate probabilities can enhance the detection accuracy of machine-generated text.<br></p><p dir="ltr">Furthermore, we delve into the application of GAN-RoBERTa, a class of deep learning models comprising a generator and a discriminator trained adversarially, for generating and detecting machine-generated text. GANs have demonstrated remarkable capabilities in generating realistic text, making them a potential tool for adversaries to produce deceptive content. However, this same adversarial nature can be harnessed for detection purposes,<br>where the discriminator is trained to distinguish between genuine and machine-generated text.<br></p><p dir="ltr">Overall, our findings suggest that the use of Ensemble models and GAN-RoBERTa architectures holds significant promise for the automated detection of machine-generated text. Through a combination of diverse approaches and adversarial training techniques, we have demonstrated improved detection accuracy and robustness, thereby addressing the challenges posed by the proliferation of generated content across various domains. Further research and refinement of these approaches will be essential to stay ahead of evolving generation techniques and ensure the integrity and trustworthiness of textual content in the digital landscape.</p>

Recent innovations in machine learning have paved the way for new capabilities in the field of radio frequency (RF) communications. Machine learning techniques such as reinforcement learning and deep neural networks (DNN) can be leveraged to improve upon traditional wireless communications methods so that they no longer require expertly-defined features. Simultaneously, cybersecurity and electronic warfare are growing areas of focus and concern in an increasingly technology-driven world. Privacy and confidentiality of communication links are both more important and more difficult than ever in the current high threat environment. RF machine learning (RFML) systems contribute to this threat as they have been shown to be successful in gleaning information from intercepted signals, through the use of learning-enabled eavesdroppers. This thesis focuses on a method of defense against such communications threats termed an adversarial evasion attack in which intelligently crafted perturbations of the RF signal are used to fool a DNN-enabled classifier, therefore securing the communications channel. One often overlooked aspect of evasion attacks is the concept of maintaining intended use. In other words, while an adversarial signal, or more generally an adversarial example, should fool the DNN it is attacking, this should not come at the detriment to it's primary application. In RF communications, this manifests in the idea that the communications link must be successfully maintained with friendly receivers, even when executing an evasion attack against malicious receivers. This is a difficult scenario, made even more so by the nature of channel effects present in over-the-air (OTA) communications, as is assumed in this work. Previous work in this field has introduced a form of evasion attack for RFML systems called a communications aware attack that explicitly addresses the reliable communications aspect of the attack by training a separate DNN to craft adversarial signals; however, this work did not utilize the full RF processing chain and left residual indicators of the attack that could be leveraged for defensive capabilities. First, this thesis focuses on implementing forward error correction (FEC), an aspect present in most communications systems, in the training process of the attack. It is shown that introducing this into the training stage allows the communications aware attack to implicitly use the structure of the coding to create smarter and more efficient adversarial signals. Secondly, this thesis then addresses the fact that in previous work, the resulting adversarial signal exhibiting significant out-of-band frequency content, a limitation that can be used to render the attack ineffective if preprocessing at the attacked DNN is assumed. This thesis presents two novel approaches to solve this problem and eliminate the majority of side content in the attack. By doing so, the communications aware attack is more readily applicable to real-world scenarios. / Master of Science / Deep learning has started infiltrating many aspects of society from the military, to academia, to commercial vendors. Additionally, with the recent deployment of 5G technology, connectivity is more readily accessible than ever and an increasingly large number of systems will communicate with one another across the globe. However, cybersecurity and electronic warfare call into question the very notion of privacy and confidentiality of data and communication streams. Deep learning has further improved these intercepting capabilities. However, these deep learning systems have also been shown to be vulnerable to attack. This thesis exists at the nexus of these two problems, both machine learning and communication security. This work expands upon adversarial evasion attacks meant to help elude signal classification at a deep learning-enabled eavesdropper while still providing reliable communications to a friendly receiver. By doing so, this work both provides a new methodology that can be used to conceal communication information from unwanted parties while also highlighting the glaring vulnerabilities present in machine learning systems.

<p>Autonomous systems are often deployed in dynamic environments and are challenged with unexpected changes (novelties) in the environments where they receive novel data that was not seen during training. Given the uncertainty, they should be able to operate without (or with limited) human intervention and they are expected to (1) Adapt to such changes while still being effective and efficient in performing their multiple tasks. The system should be able to provide continuous availability of its critical functionalities. (2) Make informed decisions independently from any central authority. (3) Be Cognitive: learns the new context, its possible actions, and be rich in knowledge discovery through mining and pattern recognition. (4) Be Reflexive: reacts to novel unknown data as well as to security threats without terminating on-going critical missions. These characteristics combine to create the workflow of autonomous decision-making process in multi-agent environments (i.e.,) any action taken by the system must go through these characteristic models to autonomously make an ideal decision based on the situation. </p> <p><br></p> <p>In this dissertation, we propose novel learning-based models to enhance the decision-making process in autonomous multi-agent systems where agents are able to detect novelties (i.e., unexpected changes in the environment), and adapt to it in a timely manner. For this purpose, we explore two complex and highly dynamic domains </p> <p>(1) Transportation Networks (e.g., Ridesharing application): where we develop AdaPool: a novel distributed diurnal-adaptive decision-making framework for multi-agent autonomous vehicles using model-free deep reinforcement learning and change point detection. (2) Multi-agent games (e.g., Monopoly): for which we propose a hybrid approach that combines deep reinforcement learning (for frequent but complex decisions) with a fixed-policy approach (for infrequent but straightforward decisions) to facilitate decision-making and it is also adaptive to novelties. (3) Further, we present a domain agnostic approach for decision making without prior knowledge in dynamic environments using Bootstrapped DQN. Finally, to enhance security of autonomous multi-agent systems, (4) we develop a machine learning based resilience testing of address randomization moving target defense. Additionally, to further  improve the decision-making process, we present (5) a novel framework for multi-agent deep covering option discovery that is designed to accelerate exploration (which is the first step of decision-making for autonomous agents), by identifying potential collaborative agents and encouraging visiting the under-represented states in their joint observation space. </p>

A significant development in wireless communication and artificial intelligence has been made possible by the combination of 5G networks with deep learning methods. This paper explores the complex interactions between these areas, concentrating on the dangers that adversarial attacks represent in the context of 5G network slicing. Multiclass classification models are created first, utilizing CNN, LSTM, and MLP architectures using a thorough three-phase process. Real adversarial attacks like FGSM, CW, BIM, and PGD are subsequently created to highlight the models' vulnerability to manipulation. The result highlights the need for strong protection measures by highlighting the upsetting potential of these attacks. The recommended defensive methods are addressed in the last stage, providing potential countermeasures to adversary threats. This study emphasizes the significance of taking into account ecological and societal implications while accepting such breakthroughs by bridging the technology and sustainability components. Integrating sustainability into the conversation becomes increasingly important as we advance the boundaries of technological innovation. By doing this, it is provided the foundation for a future that balances technical advancement with ethical progress, promoting a more robust and inclusive digital environment.

In the field of wireless communication systems, the interest in machine learning has increased in recent years. Adversarial machine learning includes attack and defense methods on machine learning components. It is a topic that has been thoroughly studied in computer vision and natural language processing but not to the same extent in wireless communication. In this thesis, a Fast Gradient Sign Method (FGSM) attack on a neural receiver is studied. Furthermore, the thesis investigates whether it is possible to make a neural receiver robust against these attacks. The study is made using the python library Sionna, a library used for research on for example 5G, 6G and machine learning in wireless communication. The effect of a FGSM attack is evaluated and mitigated with different models within adversarial training. The training data of the models is either augmented with adversarial samples, or original samples are replaced with adversarial ones. Furthermore, the power distribution and range of the adversarial samples included in the training are varied. The thesis concludes that a FGSM attack decreases the performance of a neural receiver and needs less power than a barrage jamming attack to achieve the same performance loss. A neural receiver can be made more robust against a FGSM attack when the training data of the model is augmented with adversarial samples. The samples are concentrated on a specific attack power range and the power of the adversarial samples is normally distributed. A neural receiver is also proven to be more robust against a barrage jamming attack than conventional methods without defenses.

<p dir="ltr">The research work presented in this thesis focuses on two highly important topics in the modern age. The first topic of research is the development of various image-based Network Intrusion Detection Systems (NIDSs) and performing a comprehensive analysis of their performance. Wi-Fi networks have become ubiquitous in enterprise and home networks which creates opportunities for attackers to target the networks. These attackers exploit various vulnerabilities in Wi-Fi networks to gain unauthorized access to a network or extract data from end users' devices. The deployment of an NIDS helps detect these attacks before they can cause any significant damages to the network's functionalities or security. Within the scope of our research, we provide a comparative analysis of various deep learning (DL)-based NIDSs that utilize various imaging techniques to detect anomalous traffic in a Wi-Fi network. The second topic in this thesis is the development of learning modules for security and privacy in Machine Learning (ML). The increasing integration of ML in various domains raises concerns about its security and privacy. In order to effectively address such concerns, students learning about the basics of ML need to be made aware of the steps that are taken to develop robust and secure ML-based systems. As part of this, we introduce a set of hands-on learning modules designed to educate students on the importance of security and privacy in ML. The modules provide a theoretical learning experience through presentations and practical experience using Python Notebooks. The modules are developed in a manner that allows students to easily absorb the concepts regarding privacy and security of ML models and implement it in real-life scenarios. The efficacy of this process will be obtained from the results of the surveys conducted before and after providing the learning modules. Positive results from the survey will demonstrate the learning modules were effective in imparting knowledge to the students and the need to incorporate security and privacy concepts in introductory ML courses.</p>

No description available.

<p>Generative Adversarial Networks (GANs) have consistently defined the state-of-the-art in the generative modelling of high-quality images in several applications.  The images generated using GANs, however, do not lend themselves to being directly used in supervised learning tasks without first being curated through annotations.  This dissertation investigates how to carry out automatic on-the-fly segmentation of GAN-generated images and how this can be applied to the problem of producing high-quality simulated data for X-ray based security screening.  The research exploits the hidden layer properties of GAN models in a self-supervised learning framework for the automatic one-shot segmentation of images created by a style-based GAN.  The framework consists of a novel contrastive learner that is based on a Sinkhorn distance-based clustering algorithm and that learns a compact feature space for per-pixel classification of the GAN-generated images.  This facilitates faster learning of the feature vectors for one-shot segmentation and allows on-the-fly automatic annotation of the GAN images.  We have tested our framework on a number of standard benchmarks (CelebA, PASCAL, LSUN) to yield a segmentation performance that not only exceeds the semi-supervised baselines by an average wIoU margin of 1.02 % but also improves the inference speeds by a factor of 4.5.  This dissertation also presents BagGAN, an extension of our framework to the problem domain of X-ray based baggage screening.  BagGAN produces annotated synthetic baggage X-ray scans to train machine-learning algorithms for the detection of prohibited items during security screening.  We have compared the images generated by BagGAN with those created by deterministic ray-tracing models for X-ray simulation and have observed that our GAN-based baggage simulator yields a significantly improved performance in terms of image fidelity and diversity.  The BagGAN framework is also tested on the PIDRay and other baggage screening benchmarks to produce segmentation results comparable to their respective baseline segmenters based on manual annotations.</p>

<p>Over the past decade, distributed representation learning has emerged as a popular alternative to conventional centralized machine learning training. The increasing interest in distributed representation learning, specifically federated learning, can be attributed to its fundamental property that promotes data privacy and communication savings. While conventional ML encourages aggregating data at a central location (e.g., data centers), distributed representation learning advocates keeping data at the source and instead transmitting model parameters across the network. However, since the advent of deep learning, model sizes have become increasingly large often comprising million-billions of parameters, which leads to the problem of communication latency in the learning process. In this thesis, we propose to tackle the problem of communication latency in two different ways: (i) learning private representation of data to enable its sharing, and (ii) reducing the communication latency by minimizing the corresponding long-range communication requirements.</p> <p><br></p> <p>To tackle the former goal, we first start by studying the problem of learning representations that are private yet informative, i.e., providing information about intended ''ally'' targets while hiding sensitive ''adversary'' attributes. We propose Exclusion-Inclusion Generative Adversarial Network (EIGAN), a generalized private representation learning (PRL) architecture that accounts for multiple ally and adversary attributes, unlike existing PRL solutions. We then address the practical constraints of the distributed datasets by developing Distributed EIGAN (D-EIGAN), the first distributed PRL method that learns a private representation at each node without transmitting the source data. We theoretically analyze the behavior of adversaries under the optimal EIGAN and D-EIGAN encoders and the impact of dependencies among ally and adversary tasks on the optimization objective. Our experiments on various datasets demonstrate the advantages of EIGAN in terms of performance, robustness, and scalability. In particular, EIGAN outperforms the previous state-of-the-art by a significant accuracy margin (47% improvement), and D-EIGAN's performance is consistently on par with EIGAN under different network settings.</p> <p><br></p> <p>We next tackle the latter objective - reducing the communication latency - and propose two timescale hybrid federated learning (TT-HF), a semi-decentralized learning architecture that combines the conventional device-to-server communication paradigm for federated learning with device-to-device (D2D) communications for model training. In TT-HF, during each global aggregation interval, devices (i) perform multiple stochastic gradient descent iterations on their individual datasets, and (ii) aperiodically engage in consensus procedure of their model parameters through cooperative, distributed D2D communications within local clusters. With a new general definition of gradient diversity, we formally study the convergence behavior of TT-HF, resulting in new convergence bounds for distributed ML. We leverage our convergence bounds to develop an adaptive control algorithm that tunes the step size, D2D communication rounds, and global aggregation period of TT-HF over time to target a sublinear convergence rate of O(1/t) while minimizing network resource utilization. Our subsequent experiments demonstrate that TT-HF significantly outperforms the current art in federated learning in terms of model accuracy and/or network energy consumption in different scenarios where local device datasets exhibit statistical heterogeneity. Finally, our numerical evaluations demonstrate robustness against outages caused by fading channels, as well favorable performance with non-convex loss functions.</p>

<p dir="ltr">Generative Image-to-Image Translation (I2IT) involves transforming an input image from one domain to another. Typically, this transformation retains the content in the input image while adjusting the domain-dependent style elements. Generative I2IT finds utility in a wide range of applications, yet its effectiveness hinges on adaptations to the unique characteristics of the data at hand. This dissertation pushes the boundaries of I2IT by applying it to stain-related problems in computational pathology. Particularly, the main contributions span two major applications of stain translation: H&E-to-H&E and H&E-to-IHC, each with its unique requirements and challenges. More specifically, the first contribution addresses the generalization challenge posed by the high variability in H&E stain appearances to any task-specific machine learning models. To this end, the Generative Stain Augmentation Network (G-SAN) is introduced to augment the training images in any downstream task with random and diverse H&E stain appearances. Experimental results demonstrate G-SAN’s ability to enhance model generalization across stain variations in downstream tasks. The second key contribution in this dissertation focuses on H&E-to-IHC stain translation. The major challenge in learning accurate H&E-to-IHC stain translation is the frequent and sometimes severe inconsistencies in the groundtruth H&E-IHC image pairs. To make training more robust to these inconsistencies, a novel contrastive learning based loss, named the Adaptive Supervised PatchNCE (ASP) loss is presented. Experimental results suggest that the proposed ASP-based framework outperforms the state-of-the-art in H&E-to-IHC stain translation by significant margins. Additionally, a new dataset for H&E-to-IHC translation – the Multi-IHC Stain Translation (MIST) dataset, is released to the public, featuring paired images from H&E to four different IHC stains. For future directions of generative I2IT in stain translation problems, a proof-of-concept study of applying the latest diffusion model based I2IT methods to the problem of virtual H&E staining is presented.</p>