Alexandru G. Bardas

Amin Shojaeighadikolaei, Arman Ghasemi, Kailani Jones et al.

This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users. DR has a widely recognized potential for improving power grid stability and reliability, while at the same time reducing end-users energy bills. However, the conventional DR techniques come with several shortcomings, such as the inability to handle operational uncertainties while incurring end-user disutility, which prevents widespread adoption in real-world applications. The proposed framework addresses these shortcomings by implementing DR and DEM based on real-time pricing strategy that is achieved using deep reinforcement learning. Furthermore, this framework enables the power grid service provider to leverage distributed energy resources (i.e., PV rooftop panels and battery storage) as dispatchable assets to support the smart grid during peak hours, thus achieving management of distributed energy resources. Simulation results based on the Deep Q-Network (DQN) demonstrate significant improvements of the 24-hour accumulative profit for both prosumers and the power grid service provider, as well as major reductions in the utilization of the power grid reserve generators.

Francis Hahn, Mohd Mamoon, Alexandru G. Bardas et al.

Technology for security operations centers (SOCs) has a storied history of slow adoption due to concerns about trust and reliability. These concerns are amplified with artificial intelligence, particularly large language models (LLMs), which exhibit issues such as hallucinations and inconsistent outputs. To assess whether LLM-based tools can improve SOC efficiency, we embedded two PhD researchers within a multinational company SOC for six months of ethnographic fieldwork. We identified recurring challenges, such as repetitive tasks, fragmented/unclear data, and tooling bottlenecks, and collaborated directly with practitioners to develop LLM companion tools aligned with their operational needs. Iterative refinement reduced workflow disruption and improved interpretability, leading from skepticism to sustained adoption. Ethnographic analysis indicates that this shift was enabled by our sociotechnical co-creation process consistent with Nonaka's SECI model. This framework explains the common challenges in traditional SOC technology adoption, including workflow misalignment, rigidity against evolving threats and internal requirements, and stagnation over time. Our findings show that the co-creation approach can overcome these old barriers and create a new paradigm for creating usable technology for cybersecurity operations.

Dalton A. Hahn, Drew Davidson, Alexandru G. Bardas

Service meshes have emerged as an attractive DevOps solution for collecting, managing, and coordinating microservice deployments. However, current service meshes leave fundamental security mechanisms missing or incomplete. The security burden means service meshes may actually cause additional workload and overhead for administrators over traditional monolithic systems. By assessing the effectiveness and practicality of service mesh tools, this work provides necessary insights into the available security of service meshes. We evaluate service meshes from two perspectives: skilled system administrators (who deploy optimal configurations of available security mechanisms) and default configurations. Under these two models, we consider a comprehensive set of adversarial scenarios and uncover important design flaws with contradicting goals, as well as the limitations and challenges encountered in employing service mesh tools for operational environments.

Arman Ghasemi, Amin Shojaeighadikolaei, Kailani Jones et al.

This paper presents a Reinforcement Learning (RL) based energy market for a prosumer dominated microgrid. The proposed market model facilitates a real-time and demanddependent dynamic pricing environment, which reduces grid costs and improves the economic benefits for prosumers. Furthermore, this market model enables the grid operator to leverage prosumers storage capacity as a dispatchable asset for grid support applications. Simulation results based on the Deep QNetwork (DQN) framework demonstrate significant improvements of the 24-hour accumulative profit for both prosumers and the grid operator, as well as major reductions in grid reserve power utilization.

Amin Shojaeighadikolaei, Arman Ghasemi, Kailani R. Jones et al.

Demand Response (DR) has a widely recognized potential for improving grid stability and reliability while reducing customers energy bills. However, the conventional DR techniques come with several shortcomings, such as inability to handle operational uncertainties and incurring customer disutility, impeding their wide spread adoption in real-world applications. This paper proposes a new multiagent Reinforcement Learning (RL) based decision-making environment for implementing a Real-Time Pricing (RTP) DR technique in a prosumer dominated microgrid. The proposed technique addresses several shortcomings common to traditional DR methods and provides significant economic benefits to the grid operator and prosumers. To show its better efficacy, the proposed DR method is compared to a baseline traditional operation scenario in a small-scale microgrid system. Finally, investigations on the use of prosumers energy storage capacity in this microgrid highlight the advantages of the proposed method in establishing a balanced market setup.