The AFIT ENgineer, Volume 5, Issue 2

In this issue: Quantum information science (QIS) research at AFIT Engineers Week Returns to AFIT AFIT Joins U.S. Space Command’s Academic Engagement Enterprise Digital Innovation and Integration Center of Excellence (DIICE) FY22 External Sponsor Funding summar

Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

Security Challenges when Space Merges with Cyberspace

Spaceborne systems, such as communication satellites, sensory, surveillance, GPS and a multitude of other functionalities, form an integral part of global ICT cyberinfrastructures. However, a focussed discourse highlighting the distinctive threats landscape of these spaceborne assets is conspicuous by its absence. This position paper specifically considers the interplay of Space and Cyberspace to highlight security challenges that warrant dedicated attention in securing these complex infrastructures. The opinion piece additionally adds summary opinions on (a) emerging technology trends and (b) advocacy on technological and policy issues needed to support security responsiveness and mitigation

The AI Family: The Information Security Managers Best Frenemy?

In this exploratory study, we deliberately pull apart the Artificial from the Intelligence, the material from the human. We first assessed the existing technological controls available to Information Security Managers (ISMs) to ensure their in-depth defense strategies. Based on the AI watch taxonomy, we then discuss each of the 15 technologies and their potential impact on the transformation of jobs in the field of security (i.e., AI trainers, AI explainers and AI sustainers). Additionally, in a pilot study we collect the evaluation and the narratives of the employees (n=6) of a small financial institution in a focus group session. We particularly focus on their perception of the role of AI systems in the future of cyber security

Generative Neural Network-Based Defense Methods Against Cyberattacks for Connected and Autonomous Vehicles

The rapid advancement of communication and artificial intelligence technologies is propelling the development of connected and autonomous vehicles (CAVs), revolutionizing the transportation landscape. However, increased connectivity and automation also present heightened potential for cyber threats. Recently, the emergence of generative neural networks (NNs) has unveiled a myriad of opportunities for complementing CAV applications, including generative NN-based cybersecurity measures to protect the CAVs in a transportation cyber-physical system (TCPS) from known and unknown cyberattacks. The goal of this dissertation is to explore the utility of the generative NNs for devising cyberattack detection and mitigation strategies for CAVs. To this end, the author developed (i) a hybrid quantum-classical restricted Boltzmann machine (RBM)-based framework for in-vehicle network intrusion detection for connected vehicles and (ii) a generative adversarial network (GAN)-based defense method for the traffic sign classification system within the perception module of autonomous vehicles. The author evaluated the hybrid quantum-classical RBM-based intrusion detection framework on three separate real-world Fuzzy attack datasets and compared its performance with a similar but classical-only approach (i.e., a classical computer-based data preprocessing and RBM training). The results showed that the hybrid quantum-classical RBM-based intrusion detection framework achieved an average intrusion detection accuracy of 98%, whereas the classical-only approach achieved an average accuracy of 90%. For the second study, the author evaluated the GAN-based adversarial defense method for traffic sign classification against different white-box adversarial attacks, such as the fast gradient sign method, the DeepFool, the Carlini and Wagner, and the projected gradient descent attacks. The author compared the performance of the GAN-based defense method with several traditional benchmark defense methods, such as Gaussian augmentation, JPEG compression, feature squeezing, and spatial smoothing. The findings indicated that the GAN-based adversarial defense method for traffic sign classification outperformed all the benchmark defense methods under all the white-box adversarial attacks the author considered for evaluation. Thus, the contribution of this dissertation lies in utilizing the generative ability of existing generative NNs to develop novel high-performing cyberattack detection and mitigation strategies that are feasible to deploy in CAVs in a TCPS environment

Negative Multiplicity: Forecasting the Future Impact of Emerging Technologies on International Stability and Human Security

We asked 30 experts to forecast the developmental trajectories of twelve emerging technologies in the United States, Russia, and China until 2040 and to score their possible future impact on arms race stability, crisis stability, and humanitarian principles. The results reveal that, on average, their impact is expected to be negative, with some technologies negatively affecting all three dependent variables. We used a machine learning algorithm to cluster the technologies according to their anticipated impact. This process identified technology clusters comprised of diverse high-impact technologies that share key impact characteristics but do not necessarily share technical characteristics. We refer to these combined effects as ‘negative multiplicity’, reflecting the predominantly negative, concurrent, and in some cases similar, first- and second-order effects that emerging technologies are expected to have on international stability and human security. The expected alignment of the technology development trajectories of the United States, Russia, and China by 2040, in combination with the negative environment created by geopolitical competition, points to a nascent technological arms race that threatens to seriously impede international arms control efforts to regulate emerging technologies