Analysis of Bulk Power System Resilience Using Vulnerability Graph

Critical infrastructure such as a Bulk Power System (BPS) should have some quantifiable measure of resiliency and definite rule-sets to achieve a certain resilience value. Industrial Control System (ICS) and Supervisory Control and Data Acquisition (SCADA) networks are integral parts of BPS. BPS or ICS are themselves not vulnerable because of their proprietary technology, but when the control network and the corporate network need to have communications for performance measurements and reporting, the ICS or BPS become vulnerable to cyber-attacks. Thus, a systematic way of quantifying resiliency and identifying crucial nodes in the network is critical for addressing the cyber resiliency measurement process. This can help security analysts and power system operators in the decision-making process. This thesis focuses on the resilience analysis of BPS and proposes a ranking algorithm to identify critical nodes in the network. Although there are some ranking algorithms already in place, but they lack comprehensive inclusion of the factors that are critical in the cyber domain. This thesis has analyzed a range of factors which are critical from the point of view of cyber-attacks and come up with a MADM (Multi-Attribute Decision Making) based ranking method. The node ranking process will not only help improve the resilience but also facilitate hardening the network from vulnerabilities and threats. The proposed method is called MVNRank which stands for Multiple Vulnerability Node Rank. MVNRank algorithm takes into account the asset value of the hosts, the exploitability and impact scores of vulnerabilities as quantified by CVSS (Common Vulnerability Scoring System). It also considers the total number of vulnerabilities and severity level of each vulnerability, degree centrality of the nodes in vulnerability graph and the attacker’s distance from the target node. We are using a multi-layered directed acyclic graph (DAG) model and ranking the critical nodes in the corporate and control network which falls in the paths to the target ICS. We don\u27t rank the ICS nodes but use them to calculate the potential power loss capability of the control center nodes using the assumed ICS connectivity to BPS. Unlike most of the works, we have considered multiple vulnerabilities for each node in the network while generating the rank by using a weighted average method. The resilience computation is highly time consuming as it considers all the possible attack paths from the source to the target node which increases in a multiplicative manner based on the number of nodes and vulnerabilities. Thus, one of the goals of this thesis is to reduce the simulation time to compute resilience which is achieved as illustrated in the simulation results

Application of Nonlinear Site Response Analysis in Coastal Plain South Carolina

The 1933 Long Beach, 1957 San Francisco, 1967 Caracas, 1985 Mexico City, 1989 Loma Prieta, and 1994 Northridge earthquake events left evidences of how the local site condition can affect the characteristics of propagating earthquake wave from the bedrock through the top soil. The ground motion amplitude, frequency content or the duration can be affected by the local site condition and thus can cause significant amplification or de-amplification to the original bedrock motion which can seriously affect the structures. Quantification of such site effect on ground motions is a challenging task. This dissertation is dedicated to improve the existing ground response quantification techniques and the related knowledge base. The first major attempt towards ground response quantification was the development of the 1994 NEHRP (BSSC, 1995) seismic site factor provision. Since the development of the NEHRP provisions, several studies have found these factors to produce inadequate predictions for the state of South Carolina. In an attempt to generate seismic site factors based on conditions specific to South Carolina Coastal Plain (SCCP), a series of nonlinear one-dimensional ground response analyses are performed by this author as part of a research team considering appropriate soil profiles and location specific ground excitations. After the generation of this new site factor model, a systematic repercussions study is performed by applying earthquake loads, considering both NEHRP and the new site factors, on typical highway bridge structures

Leveraging Maching Learning in Financial Fraud Forensics in the Age of Cybersecurity

Financial sectors are lucrative cyber-attack targets because of their immediate financial gain. As a result, financial institutions face challenges in developing systems that can automatically identify security breaches and separate fraudulent transactions from legitimate transactions. Today, organizations widely use machine learning techniques to identify any fraudulent behavior in customers\u27 transactions. However, machine learning techniques are often challenging because of financial institutions\u27 confidentiality policy, leading to not sharing the customer transaction data. This chapter discusses some crucial challenges of handling cybersecurity and fraud in the financial industry and building machine learning-based models to address those challenges. The authors utilize an open-source e-commerce transaction dataset to illustrate the forensic processes by creating a machine learning model to classify fraudulent transactions. Overall, the chapter focuses on how the machine learning models can help detect and prevent fraudulent activities in the financial sector in the age of cybersecurity

Seasonal Price Variation and Market Intregration of Tilapia (Oreochromis Niloticus) Fish in Some Selected Areas of Bangladesh

A study was undertaken to examine the marketing system and price behavior of tilapia fish in selected areas of Mymensingh district of Bangladesh during the month of March-May 2012. The objectives of the study were to estimate costs and margins, seasonal price variation and to test market integration of Tilapia fish. Primary and secondary data were used for this study. The higher marketing cost was incurred by aratdars and the lowest by retailer. On the other hand, retailers earned the highest net marketing margins. Analysis of market integration shows that Tilapia fish market in Bangladesh was well integrated. The study identified some problems related to economic, technical, marketing, social and natural calamities aspects and suggested some measures for solving these problems. The findings of the study revealed that the marketing of tilapia was a profitable business and some recommendations were provided for the improvement of tilapia marketing in the country. Keywords: Engle Granger co-integration, Market integration, marketing system, price behavior, Tilapia