Energy Forensics Analysis

The energy consumed by a building can reveal information about the occupants and their activities inside the building. This could be utilized by industries and law enforcement agencies for commercial or legal purposes. Utility data from Smart Meter (SM) readings can reveal detailed information that could be mapped to foretell resident occupancy and type of appliance usage over desired time intervals. However, obtaining SM data in the United States is laborious and subjected to legal and procedural constraints. This research develops a user-driven simulation tool with realistic data options and assumptions of potential human behavior to determine energy usage patterns over time without any utility data. In this work, factors such as occupant number, the possibility of place being occupied, thermostat settings, building envelope, appliances used in households, appliance capacities, and the possibility of using each appliance, weather, and heating-cooling systems specifications are considered. For five specific benchmarked scenarios, the range of the random numbers is specified based on assumed potential human behavior for occupancy and energy-consuming appliances usage possibility, with respect to the time of the day, weekday, and weekends. The simulation is developed using the Visual Basic Application (VBA)® in Microsoft Excel®, based on the discrete-event Monte Carlo Simulation (MCS). This simulation generates energy usage patterns and electricity and natural gas costs over 30-minutes intervals for one year. The simulated energy usage and the cost are reflected in the sensitivity analysis by comparing factors such as occupancy, appliance type, and time of the week. This work is intended to facilitate the analysis of building occupants\u27 activities by various stakeholders, subject to all legal provisions that apply. It is not intended for the general public to pursue these activities because legal ramifications might be involved

Post-Westgate SWAT : C4ISTAR Architectural Framework for Autonomous Network Integrated Multifaceted Warfighting Solutions Version 1.0 : A Peer-Reviewed Monograph

Police SWAT teams and Military Special Forces face mounting pressure and challenges from adversaries that can only be resolved by way of ever more sophisticated inputs into tactical operations. Lethal Autonomy provides constrained military/security forces with a viable option, but only if implementation has got proper empirically supported foundations. Autonomous weapon systems can be designed and developed to conduct ground, air and naval operations. This monograph offers some insights into the challenges of developing legal, reliable and ethical forms of autonomous weapons, that address the gap between Police or Law Enforcement and Military operations that is growing exponentially small. National adversaries are today in many instances hybrid threats, that manifest criminal and military traits, these often require deployment of hybrid-capability autonomous weapons imbued with the capability to taken on both Military and/or Security objectives. The Westgate Terrorist Attack of 21st September 2013 in the Westlands suburb of Nairobi, Kenya is a very clear manifestation of the hybrid combat scenario that required military response and police investigations against a fighting cell of the Somalia based globally networked Al Shabaab terrorist group.Comment: 52 pages, 6 Figures, over 40 references, reviewed by a reade

A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response

In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative research and refinement within the digital forensics profession. This study examines the contributions, limitations, and gaps in the existing research, shedding light on the potential and limitations of AI and ML techniques. By exploring these different research areas, we highlight the critical need for strategic planning, continual research, and development to unlock AI's full potential in digital forensics and incident response. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats

Improving Information Alignment and Distributed Coordination for Secure Information Supply Chains

Industries are constantly striving to incorporate the latest technology systems into their operations so that they can maintain a competitive edge in their respective markets. However, even when they are able to stay up to speed with technological advancement, there continues to be a gap between the workforce skill set and available technologies. Organizations may acquire advanced systems, yet end up spending extended periods of time in the implementation and deployment phases, resulting in lost resources and productivity. The primary focus of this research is on streamlining the implementation and integration of new information technology systems to avoid the dire consequences of the process being prolonged or inefficient. Specifically, the goal of this research is to mitigate business challenges in information sharing and availability for employees and managers interacting with business tools and each other. This was accomplished by first interviewing work professionals in order to identify gap parameters. Based on the interview findings, recommendations were made in order to enhance the usability of existing tools. At this point, the research setting was shifted from network operations to supply chain operations due to the restrictive nature of network operations. The research team succeeded in developing a user-centered methodology to implement and deploy new business systems to mitigate risk during integration of new systems as the transition is made from the classic way of performing tasks. While this methodology was studied in supply chain operations, it enabled the identification of a common trend of challenges in operations work settings, regardless of the business application. Hence the findings of this research can be extrapolated to any business setting, besides the ones actually studied by the team. In addition, this research ensures that operational teams are able to maximize their benefit out of the technology available, thus enabling them to keep up with the rapidly evolving world of technology while minimizing sacrifices in resources or productivity in the process

Using the open source library Libnodave for monitoring tasks in the Smart Grid scenario

[Abstract] Monitoring in the future intelligent power grids, called Smart Grids, is a critical task for the proper operation and surveillance of the infrastructure. On the other hand, open source systems are being progressively applied in R&D activities. This paper presents the utilization of the open source library Libnodave to communicate a programmable logic controller with a monitoring system through a TCP/IP network. The obtained results prove the feasibility of the proposal to be applied in the Smart Grids scenario.Junta de Extremadura; IB1804