Numerical Analysis of a Combustion Model for Layered Media Via Mathematical Homogenization

We propose to investigate a mathematical modelfor combustion in a rod made of periodically alternating thinlayers of two combustible materials such as those occurring ingun propellants. We apply the homogenization theory to resolvethe fast oscillations of the model’s coefficients across adjacentlayers, and set up numerical simulations to better understandthe reactions occurring in such media

Modelling Nuclear Weapon Effects in Wargaming Using Monte Carlo Simulations

The United States Army’s interpretation of nuclear weapon effects needs change and modernization. Wargaming exercises are commonplace in today’s military, however, despite the growing threat of non-strategic nuclear weapons (NSNW), little has been done to inform battlefield commanders on their true effects. Our research seeks to develop a tool for commanders to easily interpret quantifiable effects of a NSNW. Utilizing Monte Carlo simulation, we are developing a new methodology to analyze NSNW effects. Our model allows a commander to calculate the expected unit strength following a NSNW strike which will aid in their operational decision making ability. The Monte Carlo simulation method for analyzing nuclear effects offers a novel approach to account for variation while giving the commander an analytically interpretable output as descriptive statistics that avoids probabilities

Data-Efficient, Federated Learning for Raw Network Traffic Detection

Traditional machine learning (ML) models used for enterprise network intrusion detection systems (NIDS) typically rely on vast amounts of centralized data with expertly engineered features. Previous work, however, has shown the feasibility of using deep learning (DL) to detect malicious activity on raw network traffic payloads rather than engineered features at the edge, which is necessary for tactical military environments. In the future Internet of Battlefield Things (IoBT), the military will find itself in multiple environments with disconnected networks spread across the battlefield. These resource-constrained, data-limited networks require distributed and collaborative ML/DL models for inference that are continually trained both locally, using data from each separate tactical edge network, and then globally in order to learn and detect malicious activity represented across the multiple networks in a collaborative fashion. Federated Learning (FL), a collaborative paradigm which updates and distributes a global model through local model weight aggregation, provides a solution to train ML/DL models in NIDS utilizing learning from multiple edge devices from the disparate networks without the sharing of raw data. We develop and experiment with a data-efficient, FL framework for IoBT settings for intrusion detection using only raw network traffic in restricted, resource-limited environments. Our results indicate that regardless of the DL model architecture used on edge devices, the Federated Averaging FL algorithm achieved over 93% accuracy in model performance in detecting malicious payloads after only five episodes of FL training

Translating digital anthropometry measurements obtained from different 3D body image scanners

Background Body image scanners are used in industry and research to reliably provide a wealth of anthropometric measurements within seconds. The demonstrated utility of the scanners drives the current proliferation of more commercially available devices that rely on their own reference body sites and proprietary algorithms to output anthropometric measurements. Since each scanner relies on its own algorithms, measurements obtained from different scanners cannot directly be combined or compared. Objectives To develop mathematical models that translate anthropometric measurements between the three popular commercially available scanners. Methods A unique database that contained 3D scanner measurements in the same individuals from three different scanners (Styku, Human Solutions, and Fit3D) was used to develop linear regression models that translate anthropometric measurements between each scanner. A limits of agreement analysis was performed between Fit3D and Styku against Human Solutions measurements and the coefficient of determination, bias, and 95% confidence interval were calculated. The models were then applied to normalized scanner data from four different studies to compare the results of a k-means cluster analysis between studies. A scree plot was used to determine the optimal number of clusters derived from each study. Results Correlations ranged between R2 = 0.63 (Styku and Human Solutions mid-thigh circumference) to R2 = 0.97 (Human Solutions and Fit3D neck circumference). In general, Fit3D had better agreement with Human Solutions compared to Styku. The widest disagreement was found in chest circumference (Fit3D (bias = 2.30, 95% CI = [−3.83, 8.43]) and Styku (bias = −5.60, 95% CI = [−10.98, −0.22]). The optimal number of body shape clusters in each of the four studies was consistently 5. Conclusions The newly developed models that translate measurements between the scanners Styku and Fit3D to predict Human Solutions measurements make it possible to standardize data between scanners allowing for data pooling and comparison

Activity-Attack Graphs for Intelligence-Informed Threat COA Development

A threat course of action (COA) describes the likely tactics, techniques, and procedures (TTPs) an adversary may deploy across the cyber kill-chain. Threat COA development and analysis informs hunt teams, incident responders, and threat emulation efforts on likely activities the adversary will conduct during an attack. In this paper, we propose a novel approach to generate and evaluate threat COAs through association rule mining. We identify frequent TTP itemsets to create a set of activity groups that describe associations between TTPs. We overlay activity groups to create a directed and edge-weighted activity-attack graph. The graphs hypothesize various adversary avenues of attack, and the weighted edges inform the analyst\u27s trust of a hypothesized TTP in the COA. Our research identifies meaningful associations between TTPs and provides an analytical approach to generating threat COAs. Further, our implementation uses the STIX framework for extensibility and usability in a variety of threat intelligence environments

Gaining Competitive Advantages in Cyberspace through the Integration of Breakthrough Technologies in Autonomy, Artificial Intelligence, and Machine Learning

Cyberspace has characteristics that differ from air, land, maritime, and space domains, which affect how the Joint Force operates and defends it. Fast-moving innovations are transforming the character of warfare in cyberspace, requiring novel technology integration. Effective integration of breakthrough technologies in autonomy, artificial intelligence, and machine learning into cyberspace can enable competitive advantages to be gained that enhance the combat power of joint forces conducting multi-domain operations. These technologies help shorten the sensor-to-shooter pathway to accelerate and optimize decision-making processes. These technologies also permit the enhancement of cyber situational understanding from the ingest, fusion, synthesis, analysis, and visualization of big data from varied cyber data sources to enable decisive, warfighting information advantage via the display of key cyber terrain with relevance in the commander’s area of operations at the tactical edge. These technologies engender actionable information and recommendations to optimize human-machine decision-making via autonomous active cyber defense to effectively execute command and control while informing resourcing decisions. Competitive advantages gained allow key actions to be taken to generate, preserve, and apply informational power against a relevant actor while also permitting maneuver through the information environment