Virginia Tech National Security Institute

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10th Annual Hume Center & IC CAE Colloquium
(Virginia Tech, 2023-04-12)
The 10th annual Hume Center colloquium, sponsored through our Intelligence Community Center for Academic Excellence (IC CAE) grant, will be full of exciting student and faculty presentations related to ongoing research and experiential learning programs from across multiple departments and colleges at Virginia Tech. The theme of this year's colloquium will be "Critical and Emerging Technologies", and will showcase our students' continued endeavors to become the next generation of national security leaders.
2019 SAIC National Security Education Program Colloquium
(Virginia Tech. Hume Center., 2019-04-16)
The annual National Security Education Program Colloquium is the highlight of our educational programs. It allows students from across the university to interact with leaders from the intelligence and national security community and includes a student research poster session, panels featuring government and industry speakers, networking sessions, and a keynote address. The theme for 2019 is The Weaponization of Information and Artificial Intelligence.
2024 Hume Center & IC CAE Colloquium
(Virginia Tech, 2024-04)
The 11th annual Hume Center Colloquium, sponsored through our Intelligence Community Center for Academic Excellence (IC CAE) grant, was full of exciting student and faculty presentations related to ongoing research and experiential learning programs from across multiple departments and colleges at Virginia Tech. The theme of this year's colloquium was "The Role of the Intelligence Community in a turbulent 2025-2075" and showcased our students' continued endeavors to become the next generation of national security leaders.
Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
Kim, Brian; Sagduyu, Yalin; Davaslioglu, Kemal; Erpek, Tugba; Ulukus, Sennur (MDPI, 2022-07-29)
This paper studies the privacy of wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect transmissions of interest. There exists one transmitter that transmits to its receiver in the presence of an eavesdropper. In the meantime, a cooperative jammer (CJ) with multiple antennas transmits carefully crafted adversarial perturbations over the air to fool the eavesdropper into classifying the received superposition of signals as noise. While generating the adversarial perturbation at the CJ, multiple antennas are utilized to improve the attack performance in terms of fooling the eavesdropper. Two main points are considered while exploiting the multiple antennas at the adversary, namely the power allocation among antennas and the utilization of channel diversity. To limit the impact on the bit error rate (BER) at the receiver, the CJ puts an upper bound on the strength of the perturbation signal. Performance results show that this adversarial perturbation causes the eavesdropper to misclassify the received signals as noise with a high probability while increasing the BER at the legitimate receiver only slightly. Furthermore, the adversarial perturbation is shown to become more effective when multiple antennas are utilized.
An Analysis of Radio Frequency Transfer Learning Behavior
Wong, Lauren J.; Muller, Braeden; McPherson, Sean; Michaels, Alan J. (MDPI, 2024-06-03)
Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML). This work systematically evaluates how the training domain and task, characterized by the transmitter (Tx)/receiver (Rx) hardware and channel environment, impact radio frequency (RF) TL performance for example automatic modulation classification (AMC) and specific emitter identification (SEI) use-cases. Through exhaustive experimentation using carefully curated synthetic and captured datasets with varying signal types, channel types, signal to noise ratios (SNRs), carrier/center frequencys (CFs), frequency offsets (FOs), and Tx and Rx devices, actionable and generalized conclusions are drawn regarding how best to use RF TL techniques for domain adaptation and sequential learning. Consistent with trends identified in other modalities, our results show that RF TL performance is highly dependent on the similarity between the source and target domains/tasks, but also on the relative difficulty of the source and target domains/tasks. Results also discuss the impacts of channel environment and hardware variations on RF TL performance and compare RF TL performance using head re-training and model fine-tuning methods.
Analyzing the Russian Way of War: Evidence from the 2008 Conflict with Georgia
Beehner, Lionel; Collins, Liam; Ferenzi, Steve; Person, Robert; Brantly, Aaron F. (Modern War Institute, 2018-03-20)
In the dog days of August 2008, a column of Russian tanks and troops rolled across the Republic of Georgia’s northern border and into South Ossetia, sparking a war that was over almost before it began. The war, while not insignificant, lasted all of five days. The number of casualties did not exceed one thousand, the threshold most political scientists use to classify a war, although thousands of Georgians were displaced. By historical comparison, when Soviet tanks entered Hungary in 1956 and Afghanistan in 1979–89, the fatalities totaled 2,500 and roughly 14,000 respectively.1 The Russia-Georgia conflict was a limited war with limited objectives, yet it was arguably a watershed in the annals of modern war. It marked the first invasion by Russian ground forces into a sovereign nation since the Cold War. It also marked a breakthrough in the integration of cyberwarfare and other nonkinetic tools into a conventional strategy— what some observers in the West have termed “hybrid warfare.” Finally, and perhaps most importantly, it provided a stark preview of what was to come in Ukraine in 2014. Russian “peacekeepers,” including unmarked Russian special forces—or Spetsnaz—stationed in the region carried out an armed incursion. That is, Russia used separatist violence as a convenient pretext to launch a full-scale multidomain invasion to annex territory, a type of aggression that many analysts in the West thought was a relic of the twentieth century. The 2008 Russia-Georgia War highlights not a new form of conflict but rather the incorporation of a new dimension to that conflict: cyberspace. Where states once tried to control the radio waves, broadcast television channels, newspapers, or other forms of communications, they now add to these sources of information control cyberspace and its component aspects, websites, and social media.2 This allows Russia to influence audiences around the world. Propaganda, disinformation, and the manipulation of the informational aspects of both conflict and nonconflict settings has been a persistent attribute of state behavior.3 The new dimension added to the conduct of hostilities created by cyberspace is both a challenge to conventional hybrid information manipulation tactics and a benefit. Even though the tactical gains achieved through cyberspace in Georgia by Russian non-state actors had limited impact, the strategic and psychological effects were robust. The plausibly deniable nature of the cyber side of conflict should not be understated and adds a new dimension to hybrid warfare that once required state resources to accomplish. Now, managed through forums and social media, decentralized noncombatants can join the fight. Arguably, the inclusion of cyber means into a kinetic battle, not as a standalone effect but rather as a force multiplier, constitutes a logical progression to the natural evolution of conflict and demonstrates the value of information operations (IO) during conflict.
Application of Cybernetics and Control Theory for a New Paradigm in Cybersecurity
Adams, Michael D.; Hitefield, Seth D.; Hoy, Bruce; Fowler, Michael C.; Clancy, Thomas Charles III (Virginia Tech, 2013-11-01)
A significant limitation of current cyber security research and techniques is its reactive and applied nature. This leads to a continuous ‘cyber cycle’ of attackers scanning networks, developing exploits and attacking systems, with defenders detecting attacks, analyzing exploits and patching systems. This reactive nature leaves sensitive systems highly vulnerable to attack due to un-patched systems and undetected exploits. Some current research attempts to address this major limitation by introducing systems that implement moving target defense. However, these ideas are typically based on the intuition that a moving target defense will make it much harder for attackers to find and scan vulnerable systems, and not on theoretical mathematical foundations. The continuing lack of fundamental science and principles for developing more secure systems has drawn increased interest into establishing a ‘science of cyber security’. This paper introduces the concept of using cybernetics, an interdisciplinary approach of control theory, systems theory, information theory and game theory applied to regulatory systems, as a foundational approach for developing cyber security principles. It explores potential applications of cybernetics to cyber security from a defensive perspective, while suggesting the potential use for offensive applications. Additionally, this paper introduces the fundamental principles for building non-stationary systems, which is a more general solution than moving target defenses. Lastly, the paper discusses related works concerning the limitations of moving target defense and one implementation based on non-stationary principles.
Assessing the Value of Transfer Learning Metrics for Radio Frequency Domain Adaptation
Wong, Lauren J.; Muller, Braeden P.; McPherson, Sean; Michaels, Alan J. (MDPI, 2024-07-25)
The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge gained from data with different distributions, TL offers higher performance and reduced training time, but has yet to be fully utilized in applications of machine learning (ML) and deep learning (DL) techniques and applications related to wireless communications, a field loosely termed radio frequency machine learning (RFML). This work examines whether existing transferability metrics, used in other modalities, might be useful in the context of RFML. Results show that the two existing metrics tested, Log Expected Empirical Prediction (LEEP) and Logarithm of Maximum Evidence (LogME), correlate well with post-transfer accuracy and can therefore be used to select source models for radio frequency (RF) domain adaptation and to predict post-transfer accuracy.
Attacks and Defenses for Single-Stage Residue Number System PRNGs
Vennos, Amy; George, Kiernan; Michaels, Alan J. (MDPI, 2021-06-25)
This paper explores the security of a single-stage residue number system (RNS) pseudorandom number generator (PRNG), which has previously been shown to provide extremely high-quality outputs when evaluated through available RNG statistical test suites or in using Shannon and single-stage Kolmogorov entropy metrics. In contrast, rather than blindly performing statistical analyses on the outputs of the single-stage RNS PRNG, this paper provides both white box and black box analyses that facilitate reverse engineering of the underlying RNS number generation algorithm to obtain the residues, or equivalently key, of the RNS algorithm. We develop and demonstrate a conditional entropy analysis that permits extraction of the key given a priori knowledge of state transitions as well as reverse engineering of the RNS PRNG algorithm and parameters (but not the key) in problems where the multiplicative RNS characteristic is too large to obtain a priori state transitions. We then discuss multiple defenses and perturbations for the RNS system that fool the original attack algorithm, including deliberate noise injection and code hopping. We present a modification to the algorithm that accounts for deliberate noise, but rapidly increases the search space and complexity. Lastly, we discuss memory requirements and time required for the attacker and defender to maintain these defenses.
Collaborative Multi-Robot Multi-Human Teams in Search and Rescue
Williams, Ryan K.; Abaid, Nicole; McClure, James; Lau, Nathan; Heintzman, Larkin; Hashimoto, Amanda; Wang, Tianzi; Patnayak, Chinmaya; Kumar, Akshay (2022-04-30)
Robots such as unmanned aerial vehicles (UAVs) deployed for search and rescue (SAR) can explore areas where human searchers cannot easily go and gather information on scales that can transform SAR strategy. Multi-UAV teams therefore have the potential to transform SAR by augmenting the capabilities of human teams and providing information that would otherwise be inaccessible. Our research aims to develop new theory and technologies for field deploying autonomous UAVs and managing multi-UAV teams working in concert with multi-human teams for SAR. Specifically, in this paper we summarize our work in progress towards these goals, including: (1) a multi-UAV search path planner that adapts to human behavior; (2) an in-field distributed computing prototype that supports multi-UAV computation and communication; (3) behavioral modeling that yields spatially localized predictions of lost person location; and (4) an interface between human searchers and UAVs that facilitates human-UAV interaction over a wide range of autonomy.
A Combinatorial Approach to Hyperparameter Optimization
Khadka, Krishna; Chandrasekaran, Jaganmohan; Lei, Yu; Kacker, Raghu N.; Kuhn, D. Richard (ACM, 2024-04-14)
In machine learning, hyperparameter optimization (HPO) is essential for effective model training and significantly impacts model performance. Hyperparameters are predefined model settings which fine-tune the model’s behavior and are critical to modeling complex data patterns. Traditional HPO approaches such as Grid Search, Random Search, and Bayesian Optimization have been widely used in this field. However, as datasets grow and models increase in complexity, these approaches often require a significant amount of time and resources for HPO. This research introduces a novel approach using 𝑡-way testing—a combinatorial approach to software testing used for identifying faults with a test set that covers all 𝑡-way interactions—for HPO. 𝑇 -way testing substantially narrows the search space and effectively covers parameter interactions. Our experimental results show that our approach reduces the number of necessary model evaluations and significantly cuts computational expenses while still outperforming traditional HPO approaches for the models studied in our experiments.
A Coupled OpenFOAM-WRF Study on Atmosphere-Wake-Ocean Interaction
Gilbert, John; Pitt, Jonathan (MDPI, 2020-12-30)
This work aims to better understand how small scale disturbances that are generated at the air-sea interface propagate into the surrounding atmosphere under realistic environmental conditions. To that end, a one-way coupled atmosphere-ocean model is presented, in which predictions of sea surface currents and sea surface temperatures from a microscale ocean model are used as constant boundary conditions in a larger atmospheric model. The coupled model consists of an ocean component implemented while using the open source CFD software OpenFOAM, an atmospheric component solved using the Weather Research and Forecast (WRF) model, and a Python-based utility foamToWRF, which is responsible for mapping field data between the ocean and atmospheric domains. The results are presented for two demonstration cases, which indicate that the proposed coupled model is able to capture the propagation of small scale sea surface disturbances in the atmosphere, although a more thorough study is required in order to properly validate the model.
Cyberbiosecurity: A New Perspective on Protecting US Food and Agricultural System
Duncan, Susan E.; Reinhard, Robert; Williams, Robert C.; Ramsey, A. Ford; Thomason, Wade E.; Lee, Kiho; Dudek, Nancy; Mostaghimi, Saied; Colbert, Edward; Murch, Randall Steven (Frontiers, 2019-03-29)
Our national data and infrastructure security issues affecting the "bioeconomy" are evolving rapidly. Simultaneously, the conversation about cyber security of the U.S. food and agricultural system (cyber biosecurity) is incomplete and disjointed. The food and agricultural production sectors influence over 20% of the nation's economy ($ 6.7T) and 15% of U.S. employment (43.3M jobs). The food and agricultural sectors are immensely diverse and they require advanced technologies and efficiencies that rely on computer technologies, big data, cloud-based data storage, and internet accessibility. There is a critical need to safeguard the cyber biosecurity of our bio economy, but currently protections are minimal and do not broadly exist across the food and agricultural system. Using the food safetymanagement Hazard Analysis Critical Control Point systemconcept as an introductory point of reference, we identify important features in broad food and agricultural production and food systems: dairy, food animals, row crops, fruits and vegetables, and environmental resources (water). This analysis explores the relevant concepts of cyber biosecurity from food production to the end product user (such as the consumer) and considers the integration of diverse transportation, supplier, and retailer networks. We describe common challenges and unique barriers across these systems and recommend solutions to advance the role of cyber biosecurity in the food and agricultural sectors.
Cyberphysical Security Through Resiliency: A Systems-Centric Approach
Fleming, Cody H.; Elks, Carl R.; Bakirtzis, Georgios; Adams, Stephen C.; Carter, Bryan; Beling, Peter A.; Horowitz, Barry M. (2021-06)
Cyberphysical systems require resiliency techniques for defense, and multicriteria resiliency problems need an approach that evaluates systems for current threats and potential design solutions. A systems-oriented view of cyberphysical security, termed Mission Aware, is proposed based on a holistic understanding of mission goals, system dynamics, and risk.
Decoupling RNN Training and Testing Observation Intervals for Spectrum Sensing Applications
Moore, Megan O.; Buehrer, R. Michael; Headley, William Chris (MDPI, 2022-06-22)
Recurrent neural networks have been shown to outperform other architectures when processing temporally correlated data, such as from wireless communication signals. However, compared to other architectures, such as convolutional neural networks, recurrent neural networks can suffer from drastically longer training and evaluation times due to their inherent sample-by-sample data processing, while traditional usage of both of these architectures assumes a fixed observation interval during both training and testing, the sample-by-sample processing capabilities of recurrent neural networks opens the door for alternative approaches. Rather than assuming that the testing and observation intervals are equivalent, the observation intervals can be “decoupled” or set independently. This can potentially reduce training times and will allow for trained networks to be adapted to different applications without retraining. This work illustrates the benefits and considerations needed when “decoupling” these observation intervals for spectrum sensing applications, using modulation classification as the example use case. The sample-by-sample processing of RNNs also allows for the relaxation of the typical requirement of a fixed time duration of the signals of interest. Allowing for variable observation intervals is important in real-time applications like cognitive radio where decisions need to be made as quickly and accurately as possible as well as in applications like electronic warfare in which the sequence length of the signal of interest may be unknown. This work examines a real-time post-processing method called “just enough” decision making that allows for variable observation intervals. In particular, this work shows that, intuitively, this method can be leveraged to process less data (i.e., shorter observation intervals) for simpler inputs (less complicated signal types or channel conditions). Less intuitively, this works shows that the “decoupling” is dependent on appropriate training to avoid bias and ensure generalization.
Deep-Learning-Based Digitization of Protein-Self-Assembly to Print Biodegradable Physically Unclonable Labels for Device Security
Pradhan, Sayantan; Rajagopala, Abhi D.; Meno, Emma; Adams, Stephen; Elks, Carl R.; Beling, Peter A.; Yadavalli, Vamsi K. (MDPI, 2023-08-28)
The increasingly pervasive problem of counterfeiting affects both individuals and industry. In particular, public health and medical fields face threats to device authenticity and patient privacy, especially in the post-pandemic era. Physical unclonable functions (PUFs) present a modern solution using counterfeit-proof security labels to securely authenticate and identify physical objects. PUFs harness innately entropic information generators to create a unique fingerprint for an authentication protocol. This paper proposes a facile protein self-assembly process as an entropy generator for a unique biological PUF. The posited image digitization process applies a deep learning model to extract a feature vector from the self-assembly image. This is then binarized and debiased to produce a cryptographic key. The NIST SP 800-22 Statistical Test Suite was used to evaluate the randomness of the generated keys, which proved sufficiently stochastic. To facilitate deployment on physical objects, the PUF images were printed on flexible silk-fibroin-based biodegradable labels using functional protein bioinks. Images from the labels were captured using a cellphone camera and referenced against the source image for error rate comparison. The deep-learning-based biological PUF has potential as a low-cost, scalable, highly randomized strategy for anti-counterfeiting technology.
Designing a Block Cipher in Galois Extension Fields for IoT Security
George, Kiernan; Michaels, Alan J. (MDPI, 2021-11-05)
This paper focuses on a block cipher adaptation of the Galois Extension Fields (GEF) combination technique for PRNGs and targets application in the Internet of Things (IoT) space, an area where the combination technique was concluded as a quality stream cipher. Electronic Codebook (ECB) and Cipher Feedback (CFB) variations of the cryptographic algorithm are discussed. Both modes offer computationally efficient, scalable cryptographic algorithms for use over a simple combination technique like XOR. The cryptographic algorithm relies on the use of quality PRNGs, but adds an additional layer of security while preserving maximal entropy and near-uniform distributions. The use of matrices with entries drawn from a Galois field extends this technique to block size chunks of plaintext, increasing diffusion, while only requiring linear operations that are quick to perform. The process of calculating the inverse differs only in using the modular inverse of the determinant, but this can be expedited by a look-up table. We validate this GEF block cipher with the NIST test suite. Additional statistical tests indicate the condensed plaintext results in a near-uniform distributed ciphertext across the entire field. The block cipher implemented on an MSP430 offers a faster, more power-efficient alternative to the Advanced Encryption Standard (AES) system. This cryptosystem is a secure, scalable option for IoT devices that must be mindful of time and power consumption.
Development and Analysis of a Spiral Theory-based Cybersecurity Curriculum
Back, Godmar V.; Basu, Debarati; Naciri, William; Lohani, Vinod K.; Plassmann, Paul E.; Barnette, Dwight; Ribbens, Calvin J.; Gantt, Kira; McPherson, David (2017-01-09)
Enhance cybersecurity learning experiences of students at Virginia Tech’s large engineering program
Disappearing cities on US coasts
Ohenhen, Leonard O.; Shirzaei, Manoochehr; Ojha, Chandrakanta; Sherpa, Sonam F.; Nicholls, Robert J. (Nature Research, 2024-03-06)
The sea level along the US coastlines is projected to rise by 0.25–0.3 m by 2050, increasing the probability of more destructive flooding and inundation in major cities. However, these impacts may be exacerbated by coastal subsidence— the sinking of coastal land areas—a factor that is often underrepresented in coastal-management policies and long-term urban planning. In this study, we combine high-resolution vertical land motion (that is, raising or lowering of land) and elevation datasets with projections of sea-level rise to quantify the potential inundated areas in 32 major US coastal cities. Here we show that, even when considering the current coastal-defence structures, further land area of between 1,006 and 1,389 km² is threatened by relative sea-level rise by 2050, posing a threat to a population of 55,000–273,000 people and 31,000–171,000 properties. Our analysis shows that not accounting for spatially variable land subsidence within the cities may lead to inaccurate projections of expected exposure. These potential consequences show the scale of the adaptation challenge, which is not appreciated in most US coastal cities.
Disruptive Role of Vertical Land Motion in Future Assessments of Climate Change-Driven Sea-Level Rise and Coastal Flooding Hazards in the Chesapeake Bay
Sherpa, Sonam Futi; Shirzaei, Manoochehr; Ojha, Chandrakanta (American Geophysical Union, 2023-04)
Future projections of sea-level rise (SLR) used to assess coastal flooding hazards and exposure throughout the 21st century and devise risk mitigation efforts often lack an accurate estimate of coastal vertical land motion (VLM) rate, driven by anthropogenic or non-climate factors in addition to climatic factors. The Chesapeake Bay (CB) region of the United States is experiencing one of the fastest rates of relative sea-level rise on the Atlantic coast of the United States. This study uses a combination of space-borne Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite System (GNSS), Light Detecting and Ranging (LiDAR) data sets, available National Oceanic and Atmospheric Administration (NOAA) long-term tide gauge data, and SLR projections from the Intergovernmental Panel on Climate Change (IPCC), AR6 WG1 to quantify the regional rate of relative SLR and future flooding hazards for the years 2030, 2050, and 2100. By the year 2100, the total inundated areas from SLR and subsidence are projected to be 454(316–549)–600(535𝐴𝐴–690) km² for Shared Socioeconomic Pathways (SSPs) 1–1.9 to 5–8.5, respectively, and 342(132–552)–627(526–735) 𝐴𝐴 km2 only from SLR. The effect of storm surges based on Hurricane Isabel can increase the inundated area to 849(832–867)–1,117(1,054–1,205) km² under different VLM and SLR scenarios. We suggest that accurate estimates of VLM rate, such as those obtained here, are essential to revise IPCC projections and obtain accurate maps of coastal flooding and inundation hazards. The results provided here inform policymakers when assessing hazards associated with global climate changes and local factors in CB, required for developing risk management and disaster resilience plans.

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