Technology and Policy: System Acquisition in a Complex Operational Environment

The United States’ (US) ability to maintain a technological edge in the current operational environment is challenged by the increased ability of near-peer nations to produce military technology. In response to this problem, the US Army Engineer Research and Development Center (ERDC) seeks to model the three key elements of military system acquisition—context, product, and process—to develop a more comprehensive understanding regarding how and why nations acquire technical solutions. Through the application of the System Dynamics Modeling Process (SDMP), this research examines the interactions between the strategic context of Germany, the military products it acquires to address its operational needs, and the processes it employs to acquire military technology. The results of this research indicate that numerous dynamic variables of context impact the acquisitions process for Germany, particularly political support and subsequent monetary allocations to research and development.

On the Detection Capabilities of Signature-Based Intrusion Detection Systems in the Context of Web Attacks

Signature-based Intrusion Detection Systems (SIDS) play a crucial role within the arsenal of security components of most organizations. They can find traces of known attacks in the network traffic or host events for which patterns or signatures have been pre-established. SIDS include standard packages of detection rulesets, but only those rules suited to the operational environment should be activated for optimal performance. However, some organizations might skip this tuning process and instead activate default off-the-shelf rulesets without understanding its implications and trade-offs. In this work, we help gain insight into the consequences of using predefined rulesets in the performance of SIDS. We experimentally explore the performance of three SIDS in the context of web attacks. In particular, we gauge the detection rate obtained with predefined subsets of rules for Snort, ModSecurity and Nemesida using seven attack datasets. We also determine the precision and rate of alert generated by each detector in a real-life case using a large trace from a public webserver. Results show that the maximum detection rate achieved by the SIDS under test is insufficient to protect systems effectively and is lower than expected for known attacks. Our results also indicate that the choice of predefined settings activated on each detector strongly influences its detection capability and false alarm rate. Snort and ModSecurity scored either a very poor detection rate (activating the less-sensitive predefined ruleset) or a very poor precision (activating the full ruleset). We also found that using various SIDS for a cooperative decision can improve the precision or the detection rate, but not both. Consequently, it is necessary to reflect upon the role of these open-source SIDS with default configurations as core elements for protection in the context of web attacks. Finally, we provide an efficient method for systematically determining which rules deactivate from a ruleset to significantly reduce the false alarm rate for a target operational environment. We tested our approach using Snort’s ruleset in our real-life trace, increasing the precision from 0.015 to 1 in less than 16 h of work.

Combating the Current Pandemic and Preparing for the Next: Lessons Learned From the COVID-19 Pandemic From the Perspective of Deployed Special Operations Forces

ABSTRACT The coronavirus 2019 (COVID-19) pandemic continues to be a threat to global health, including the health of deployed armed forces. Servicemembers had to adjust to the “new normal” while maintaining the interests of the nation’s security as well as that of our host nation partners. This commentary examines how Special Operations Forces operating within four different regions worldwide leveraged the challenges presented by the onset of this pandemic in maintaining stability, sustaining a ready force, and operating forward deployed. Deployed forces face constant difficulties with logistical support, varied medical resources access and a medical system predominantly focused on trauma care. At the onset of the COVID-19 pandemic there was little guidance specific to these circumstances which required an improvised adaptation of the recommendations set by national and Department of Defense medical authorities. Plans were constantly revised to match the ever changing medical and operational environment. Strategies such as the “Bubble Philosophy” and tiered force protection measures helped our units to maintain a rigorous training cycle. New methods of communication and training with our host nation partners such as the use of Unmanned Aerial Systems (UAS) platforms to survey host nation training became standard. Through these measures all of our forces were able to maintain operational capacity, protect the force, and maintain rapport with the host nations. We hope these experiences will provide a rough framework for future forces faced with a similar struggle. We also want to stress that challenges vary depending on the area of operations and the pathogen responsible for the pandemic. Any feedback and collaboration that may come from this work is appreciated and encouraged.

Classification and Prediction of Software Incidents Using Machine Learning Techniques

An incident, in the perception of information technology, is an event that is not part of a normal process and disrupts operational procedure. This research work particularly focuses on software failure incidents. In any operational environment, software failure can put the quality and performance of services at risk. Many efforts are made to overcome this incident of software failure and to restore normal service as soon as possible. The main contribution of this study is software failure incidents classification and prediction using machine learning. In this study, an active learning approach is used to selectively label those data which is considered to be more informative to build models. Firstly, the sample with the highest randomness (entropy) is selected for labeling. Secondly, to classify the labeled observation into either failure or no failure classes, a binary classifier is used that predicts the target class label as failure or not. For classification, Support Vector Machine is used as a main classifier to classify the data. We derived our prediction models from the failure log files collected from the ECLIPSE software repository.

Scale-up of Mycorrhizal-Assisted Phytoremediation system from Technology Readiness Level 6 (Relevant Environment) to 7 (Operational Environment): Cost-benefits within a Circular Economy Context

This chapter analyzes the costs-benefits of a particular phytomining methodology named mycorrhizal-assisted phytoremediation (MAP). This MAP system is responsible for phytostabilization and/or phytoextraction of secondary and critical raw materials from contaminated soil or mining wastes. To this aim, we evaluated the application of MAP in a modified constructed wetland, the vegetable depuration module (VDM), which permits the calibration of physical-chemical-biological variables in a contaminated substrate, as well as the partition of chemical elements within the liquid phase due to leaching and solid phases (biomass and soil). This successful methodology allows to scale-up from a Technology Readiness Level (TRL) 6 (demonstration in a relevant environment) toward TRL 7 (demonstration in an operational environment), which implies the transfer to the territory.

Degradation of Perovskite Thin Films and Solar Cells with Candle Soot C/Ag Electrode Exposed in a Control Ambient

Perovskite solar cells (PSCs) have already achieved efficiencies of over 25%; however, their instability and degradation in the operational environment have prevented them from becoming commercially viable. Understanding the degradation mechanism, as well as improving the fabrication technique for achieving high-quality perovskite films, is crucial to overcoming these shortcomings. In this study, we investigated details in the changes of physical properties associated with the degradation and/or decomposition of perovskite films and solar cells using XRD, FESEM, EDX, UV-Vis, Hall-effect, and current-voltage (I-V) measurement techniques. The dissociation, as well as the intensity of perovskite peaks, have been observed as an impact of film degradation by humidity. The decomposition rate of perovskite film has been estimated from the structural and optical changes. The performance degradation of novel planner structure PSCs has been investigated in detail. The PSCs were fabricated in-room ambient using candle soot carbon and screen-printed Ag electrode. It was found that until the perovskite film decomposed by 30%, the film properties and cell efficiency remained stable.

Pathways to Unsegregated Sharing of Airspace: Views of the Uncrewed Aerial Vehicle (UAV) Industry

The uncrewed aerial vehicle (UAV or drone) industry is expanding, offering services such as video/photography, inspection, monitoring, surveying, and logistics. This is leading to competing demands for airspace with existing crewed aircraft activities, especially in uncontrolled airspace. As a result, there is an increasingly urgent need for a shared airspace solution that enables drones to be integrated with the wider aviation community in unsegregated operations. The purpose of this research was to engage with the drone industry to understand their issues regarding shared airspace as an important first step in the co-development of operating procedures that can provide equitable airspace access for all. An online, interactive workshop format was employed, with participants (n~80) drawn from the UK drone industry and other attendant organisations. Verbal and written data were recorded, and then analysed using thematic analysis. The findings summarise the issues on a range of topics, grouped into three over-arching themes: (1) operational environment; (2) technical and regulatory environment; and (3) equity and wider society. Results suggested that important issues included the necessity for a dependable detect-and-avoid (DAA) system for in-flight de-confliction, based on onboard electronic conspicuity (EC) devices, and the need for support for shared airspace from the wider aviation community. This study contributes to the stakeholder engagement that will be essential if the co-development of a shared airspace solution is to be widely acceptable to all.

Remaining useful life prediction of cylinder liner based on nonlinear degradation model

In order to effectively monitor the wear and predict the life of cylinder liner, a nonlinear degradation model with multi-source uncertainty based on Wiener process is established to evaluate the remaining useful life (RUL) of cylinder liner wear. Due to complex service performance of cylinder liner, the uncertainty of operational environment and working conditions of cylinder liner wear are considered into the model by a random function. The probability density function (PDF) formula of RUL is derived, and the maximum likelihood estimation method is adopted to estimate the unknown parameters of PDF. Considering the evaluated parameters as the initial values, the model parameters are updated adaptively, and an adaptive PDF is obtained. Furthermore, the proposed model is compared with two classical degradation models. The results show that the proposed model has a good performance for predicting the life, and the error is within 5%. The method can provide a reference for condition monitoring of cylinder liner wear.

Improving winter traction for vehicles in northern operations

As part of the campaign to increase readiness in northern regions, a near commercial off-the-shelf (COTS) solution was identified for the High Mobility Multipurpose Wheeled Vehicle (HMMWV); and used to assess the suitability of commercially available winter tires for operational deployment. Initial performance evaluations conducted during the winters of 2020 and 2021 demonstrated and quantified significant improvements to traction and handling on a variety of winter surfaces. User feedback from United States Army Alaska (USARAK) Soldiers confirmed these results in an operational environment. Results of this study provide new winter tire specifications for the Army and justify the procurement of a HMMWV winter tire for improved safety and capability for US Soldier and vehicle fleet needs. The data and Soldier evaluations support attaining a National Stock Number (NSN) and provide data to develop models of winter vehicle performance that include the impact of winter tires and chains. This work also paves the way for future development and procurement of winter tires for vehicles where COTS solutions are unavailable. The motivation is to provide Soldiers with state-of-the-art winter tires to increase safety, capability, and operational compatibility with North Atlantic Treaty Organization (NATO) partners in the European Theater of Operations, and mobility superiority in all environments.