Adaptive Alert Management for Balancing Optimal Performance among Distributed CSOCs using Reinforcement Learning

Large organizations typically have Cybersecurity Operations Centers (CSOCs) distributed at multiple locations that are independently managed, and they have their own cybersecurity analyst workforce. Under normal operating conditions, the CSOC locations are ideally staffed such that the alerts generated from the sensors in a work-shift are thoroughly investigated by the scheduled analysts in a timely manner. Unfortunately, when adverse events such as increase in alert arrival rates or alert investigation rates occur, alerts have to wait for a longer duration for analyst investigation, which poses a direct risk to organizations. Hence, our research objective is to mitigate the impact of the adverse events by dynamically and autonomously re-allocating alerts to other location(s) such that the performances of all the CSOC locations remain balanced. This is achieved through the development of a novel centralized adaptive decision support system whose task is to re-allocate alerts from the affected locations to other locations. This re-allocation decision is non-trivial because the following must be determined: (1) timing of a re-allocation decision, (2) number of alerts to be re-allocated, and (3) selection of the locations to which the alerts must be distributed. The centralized decision-maker (henceforth referred to as agent) continuously monitors and controls the level of operational effectiveness-LOE (a quantified performance metric) of all the locations. The agent's decision-making framework is based on the principles of stochastic dynamic programming and is solved using reinforcement learning (RL). In the experiments, the RL approach is compared with both rule-based and load balancing strategies. By simulating real-world scenarios, learning the best decisions for the agent, and applying the decisions on sample realizations of the CSOC's daily operation, the results show that the RL agent outperforms both approaches by generating (near-) optimal decisions that maintain a balanced LOE among the CSOC locations. Furthermore, the scalability experiments highlight the practicality of adapting the method to a large number of CSOC locations

The necessity of socio-ecological modification of two-tier economic model of secondary resources management in Ukraine

The problems of effective use of secondary resources, as well as the overall problem of managing them at the level of the national economy in the context of separate sectors and enterprises remain relevant for countries with transition economy for more than a quarter of a century. However, in the context of management situations modeling, including those with regard to more efficient use of secondary resources, the issue, mentioned above, requires further actualization and its solutions. The article discloses an ecological approach to the study of socio-economic relations, in particular to the manufacturing processes. It is shown that the scale of use of the environment and the structure of the industrial production established in Ukraine placed it among the countries with the highest absolute and specific indicators of formation and accumulation of toxic waste; a situation with them remains critical today. As a tool for issues optimization associated with the secondary resources and waste, the industry model is considered, in which the criterion of an optimality is the maximum excess of the economic result over the costs for the use of secondary raw materials and the elimination of social, ecological and economic damage due to waste generation. It is proved that the protection of the environment and public health from the negative impact of waste is an urgent task and a priority principle of the policy on the way of sustainable development of the state. The socioecological modification of economic-mathematical model of secondary resources management is proposed: environmental effect is represented as the value of prevented damage from environmental waste pollution; social effect is represented as the sum of the effects from improving the utilization of the labor force due to the reduced morbidity from social insurance savings and reduction of costs in healthcare

Real-time probabilistic collision avoidance for autonomous vehicles, using order reductive conflict metrics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2003.Includes bibliographical references (p. 131-137).Contemporary collision avoidance systems such as the Traffic Alert and Collision Avoidance System (TCAS) have proven their effectiveness in the Commercial Aviation (CA) industry within the last decade. Yet, TCAS and many systems like it represent attempts at collision avoidance that do not fully recognize the uncertain nature of a conflict event. Most systems circumvent probabilistic representation through simplifying approximations and pre-compiled notions of hazard space, since probabilistic representation of collision in three dimensions is considered to be an intractable problem. Recent developments by Kuchar and Yang[70] and Paielli and Erzberger[50] have shown that collision avoidance may be cast as a probabilistic state-space problem. Innovative solution approaches may then allow systems of this nature to probe collision risk in real-time, based on real-time state estimates. The research documented in this thesis further develops the probabilistic approach for the non-cooperative, two-vehicle problem as applied in real-time to autonomous aircraft. The research is kept in a general form, thereby warranting application to a wide variety of multi-dimensional collision avoidance applications and scenario geometries. The work primarily improves the state of the art through the creation of order reductive collision metrics in order to simplify the intractable problem of multi-dimensional collision risk calculation. As a result, a tractable, real-time, probabilistic algorithm is developed for the calculation of collision risk as a function of time.(cont.) The collision avoidance problem is contextualized not only within the realm of recent research within the CA industry, but is also likened to such concepts as the first passage time problem encountered in physics, and the field of reliability theory often encountered in civil and mechanical engineering problems. Yang's method of solution, a piece-wise straight-line Monte-Carlo approach to state propagation, is extended with a model-predictive, finite horizon risk accumulation algorithm. Through this extension we are capable of modelling collision risk for linear(-ized), time-variant, dynamic vehicle models and control strategies. A strategy is developed whereby the advantage of delayed collision avoidance action is calculated and it is framed as an extension of the notion of system operating characteristics (SOCs). The complexity of the probabilistic representation is reduced by application of quadratic conflict metrics. The numerical complexity can be reduced from [Omicron](N2n) to [Omicron](Nlog2(N)) at each time step within a finite horizon time interval. Risk calculation errors due to numerical and stochastic approximations are quantified. An applicability test is also devised whereby a vehicle's dynamic model and control characteristics may be used to calculate risk error estimates before implementing the bulk of the algorithmic solution. Various other applications of the work, outside the scope of collision avoidance, are also identified.by Thomas Jones.Ph.D

OPTIMIZATION OF ENERGY STORAGE SCHEDULING IN ELECTRICITY MARKETS

In the existing literature, merchants\u27 trading actions are usually assumed not to affect market prices; however, a large-scale energy storage merchant’s actions can affect market prices. This work examined two electricity merchant scenarios: one with only energy storage and the other with both energy storage and renewable power plants. We approximated market impact via a linear function of the electricity traded by the merchant. This study began by applying dynamic programming to the optimal economic dispatch policy of electricity merchants and it considered the market impact, physical characteristics of storage systems, and the uncertainty of renewable energy sources. Then, this study evaluated the effect of the self-consumption demand on the co-optimization scheduling of prosumers with both energy storage and distributed renewable energy sources. Furthermore, this work investigated how the production tax credits (PTC) impacted merchants\u27 co-optimization scheduling policy under two common PTC subsidy policies. Finally, the time-coupling constraints require market participants to make decisions in advance based on forecasted electricity prices. However, independent system operators (ISOs) have the most comprehensive and detailed information regarding market operations, so they are more likely to generate more accurate pricing estimates than individual merchants. Therefore, this study analyzed whether allowing the ISO to schedule the generators and energy storage could bring economic benefits to the social-welfare maximizing ISO and the profit-maximizing electricity merchant or generators. This study found that if the ISO sends the cleared prices to the electricity merchant, a merchant will arrive at the same optimal scheduling decisions as those from the perspective of the ISO --Abstract, p. ii

Mesh-Mon: a Monitoring and Management System for Wireless Mesh Networks

A mesh network is a network of wireless routers that employ multi-hop routing and can be used to provide network access for mobile clients. Mobile mesh networks can be deployed rapidly to provide an alternate communication infrastructure for emergency response operations in areas with limited or damaged infrastructure. In this dissertation, we present Dart-Mesh: a Linux-based layer-3 dual-radio two-tiered mesh network that provides complete 802.11b coverage in the Sudikoff Lab for Computer Science at Dartmouth College. We faced several challenges in building, testing, monitoring and managing this network. These challenges motivated us to design and implement Mesh-Mon, a network monitoring system to aid system administrators in the management of a mobile mesh network. Mesh-Mon is a scalable, distributed and decentralized management system in which mesh nodes cooperate in a proactive manner to help detect, diagnose and resolve network problems automatically. Mesh-Mon is independent of the routing protocol used by the mesh routing layer and can function even if the routing protocol fails. We demonstrate this feature by running Mesh-Mon on two versions of Dart-Mesh, one running on AODV (a reactive mesh routing protocol) and the second running on OLSR (a proactive mesh routing protocol) in separate experiments. Mobility can cause links to break, leading to disconnected partitions. We identify critical nodes in the network, whose failure may cause a partition. We introduce two new metrics based on social-network analysis: the Localized Bridging Centrality (LBC) metric and the Localized Load-aware Bridging Centrality (LLBC) metric, that can identify critical nodes efficiently and in a fully distributed manner. We run a monitoring component on client nodes, called Mesh-Mon-Ami, which also assists Mesh-Mon nodes in the dissemination of management information between physically disconnected partitions, by acting as carriers for management data. We conclude, from our experimental evaluation on our 16-node Dart-Mesh testbed, that our system solves several management challenges in a scalable manner, and is a useful and effective tool for monitoring and managing real-world mesh networks

2011-2012 Undergraduate Bulletin

https://soundideas.pugetsound.edu/bulletins/1012/thumbnail.jp

2012-2013 Undergraduate Bulletin

https://soundideas.pugetsound.edu/bulletins/1013/thumbnail.jp

Bridgewater State University Undergraduate & Graduate Catalog 2015-2016

This 2015-2016 Bridgewater State University Catalog outlines programs of study. This catalog is an official publication of Bridgewater State University. The rules, regulations, policies, fees and other charges, courses of study, and academic requirements that appear in this catalog were in effect at the time of its publication. Whether noted elsewhere in this catalog or not, the university reserves the right to change, eliminate, and add to any existing (and to introduce additional) rules, regulations, policies, fees and other charges, courses of study and academic requirements that appear in this catalog, in the Student Handbook or on its website or otherwise. Whenever it does so, the university will give as much advance notice as it considers feasible or appropriate, but it reserves the right in all cases to do so without notice. Statement of Student Responsibility Students should read and understand the rules, requirements and policies described in the catalog. Additionally, all enrolled students are expected to read and be familiar with the content of the Student Handbook. In all cases, students bear ultimate responsibility for reading the catalog and the Student Handbook and following the policies, rules, requirements and regulations of the university.https://vc.bridgew.edu/bsu_catalogs/1007/thumbnail.jp