Air Force Institute of Technology Research Report 2006

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

The use of convolutional code for narrowband interference suppression in OFDM-DVBT system

The problem of mitigating narrowband interference (NBI) due to coexistence between Digital Video Broadcasting-Terrestrial (DVB-T) and International Mobile Telecommunication-Advanced (IMT-A) system is considered. It is assumed that a spectrum of IMT-A system between 790-862 MHz interfere the spectrum of the OFDM signal in DVB-T band. Two types of convolutional code (CC) which are non-systematic convolutional code (NSCC) and recursive systematic convolutional code (RSCC) are proposed to mitigate NBI. The performance of the two techniques is compared under additive white Gaussian noise (AWGN) channel. It is observed that NSCC has a better bit error rate (BER) performance than RSCC. The result showed good performance for low SNR (≤ 5dB)

Impacts and Risk of Generative AI Technology on Cyber Defense

Generative Artificial Intelligence (GenAI) has emerged as a powerful technology capable of autonomously producing highly realistic content in various domains, such as text, images, audio, and videos. With its potential for positive applications in creative arts, content generation, virtual assistants, and data synthesis, GenAI has garnered significant attention and adoption. However, the increasing adoption of GenAI raises concerns about its potential misuse for crafting convincing phishing emails, generating disinformation through deepfake videos, and spreading misinformation via authentic-looking social media posts, posing a new set of challenges and risks in the realm of cybersecurity. To combat the threats posed by GenAI, we propose leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of cyberattacks, as a foundational model for cyber defense. This paper aims to provide a comprehensive analysis of the risk areas introduced by the offensive use of GenAI techniques in each phase of the CKC framework. We also analyze the strategies employed by threat actors and examine their utilization throughout different phases of the CKC, highlighting the implications for cyber defense. Additionally, we propose GenAI-enabled defense strategies that are both attack-aware and adaptive. These strategies encompass various techniques such as detection, deception, and adversarial training, among others, aiming to effectively mitigate the risks posed by GenAI-induced cyber threats

Mobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists

Introductory Computer Forensics

INTERPOL (International Police) built cybercrime programs to keep up with emerging cyber threats, and aims to coordinate and assist international operations for ?ghting crimes involving computers. Although signi?cant international efforts are being made in dealing with cybercrime and cyber-terrorism, ?nding effective, cooperative, and collaborative ways to deal with complicated cases that span multiple jurisdictions has proven dif?cult in practic

Air Force Institute of Technology Research Report 2007

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Image quality assessment using artificial neural networks

not availabl

Human-Centric Deep Generative Models: The Blessing and The Curse

Over the past years, deep neural networks have achieved significant progress in a wide range of real-world applications. In particular, my research puts a focused lens in deep generative models, a neural network solution that proves effective in visual (re)creation. But is generative modeling a niche topic that should be researched on its own? My answer is critically no. In the thesis, I present the two sides of deep generative models, their blessing and their curse to human beings. Regarding what can deep generative models do for us, I demonstrate the improvement in performance and steerability of visual (re)creation. Regarding what can we do for deep generative models, my answer is to mitigate the security concerns of DeepFakes and improve minority inclusion of deep generative models. For the performance of deep generative models, I probe on applying attention modules and dual contrastive loss to generative adversarial networks (GANs), which pushes photorealistic image generation to a new state of the art. For the steerability, I introduce Texture Mixer, a simple yet effective approach to achieve steerable texture synthesis and blending. For the security, my research spans over a series of GAN fingerprinting solutions that enable the detection and attribution of GAN-generated image misuse. For the inclusion, I investigate the biased misbehavior of generative models and present my solution in enhancing the minority inclusion of GAN models over underrepresented image attributes. All in all, I propose to project actionable insights to the applications of deep generative models, and finally contribute to human-generator interaction