Sungjune Park

Understanding the Value of Countermeasure Portfolios in Information Systems Security

Organizations are faced with a variety of information security threats and implement several information system security countermeasures (ISSCs) to mitigate possible damage due to security attacks. These security countermeasures vary in their ability to deal with different types of security attacks and, hence, are implemented as a portfolio of ISSCs. A key challenge for organizations is to understand the economic consequences of security attacks relative to the ISSC portfolio implemented. This paper combines the risk analysis and disaster recovery perspectives to build an integrated simulation model of ISSC portfolio value. The model incorporates the characteristics of an ISSC portfolio relative to the threat and business environments and includes the type of attack, frequency of attacks, possible damage, and the extent and time of recovery from damage. The simulation experiments provide interesting insights into the interactions between ISSC portfolio components and characteristics of business and threat environments in determining portfolio value.

EMS call volume predictions: A comparative study

The demand for ambulances fluctuates throughout the week, depending on the day of week and, even more so, the time of day. Many emergency medical services (EMS) managers adjust the number of ambulances deployed using various demand pattern analyses, including moving averages. Simply forecasting the number of expected calls for an entire region does not allow managers to deploy their often-limited resources effectively so that emergency response time is minimized. In order for deployment plans, or even sophisticated optimization models, to be more effective, emergency call forecasts must be accurate for both time and location. For purposes of this study, we consider forecasts accurate for a 4x4sq.mile region if they are within +/-0.25 of actual calls for hourly forecasts and within +/-0.5 of actual calls for 3-h forecasts. An artificial neural network (ANN) designed to forecast demand volume of specific areas during different times of the day is compared to current industry practice for accuracy of prediction. Our study shows that both methods produce accurate forecasts for certain levels of time and space granularity. Results also suggest that the high level of space and time details in forecasts desired by EMS managers may be difficult to obtain regardless of which method is used.

Sequence-based clustering for Web usage mining: A new experimental framework and ANN-enhanced K-means algorithm

We develop a general sequence-based clustering method by proposing new sequence representation schemes in association with Markov models. The resulting sequence representations allow for calculation of vector-based distances (dissimilarities) between Web user sessions and thus can be used as inputs of various clustering algorithms. We develop an evaluation framework in which the performances of the algorithms are compared in terms of whether the clusters (groups of Web users who follow the same Markov process) are correctly identified using a replicated clustering approach. A series of experiments is conducted to investigate whether clustering performance is affected by different sequence representations and different distance measures as well as by other factors such as number of actual Web user clusters, number of Web pages, similarity between clusters, minimum session length, number of user sessions, and number of clusters to form. A new, fuzzy ART-enhanced K-means algorithm is also developed and its superior performance is demonstrated.

Performance of Fuzzy ART neural network and hierarchical clustering for part-machine grouping based on operation sequences

The problem context for this study is one of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time manufacturing systems and for streamlining material flows in general. Given this problem context, this study develops an experimental procedure to compare the performance of a fuzzy ART neural network, a relatively recent neural network method, with the performance of traditional hierarchical clustering methods. For large, industry-type data sets, the fuzzy ART network, with the modifications proposed here, is capable of performance levels equal or superior to those of the widely used hierarchical clustering methods. However, like other ART networks, Fuzzy ART also results in category proliferation problems, an aspect that continues to require attention for ART networks. However, low execution times and superior solution quality make fuzzy ART a useful addition to the set of tools and techniques now available for group technology and design of cellular manufacturing systems.

Optimal Pricing of Digital Experience Goods Under Piracy

Piracy of digital experience goods such as music recordings has received increased attention in the literature. Much of this research has focused on pricing policies, protection against piracy, and governmental policies in the software industry. In this research, we focus on pricing policies of producers of digital experience goods. We consider a heterogeneous consumer market with different segments, each having a different affinity to piracy. We analyze the effect of different producer pricing policies on the revenue of the creator of the product, who may be different than the producer. Our results indicate that the explicit incorporation of these different consumer segments will cause the producer to charge lower prices and, therefore, lead to higher legal product diffusion. We show that the royalty system does not solve the double marginalization problem and is suboptimal from a supply-chain perspective. Also, the creator of the goods prefers a lower price than the producers optimal price, and this tendency increases with the creators per unit royalty.

Joint replenishment problem under continuous unit cost change

The joint replenishment problem determines order quantities and the grouping of products replenished from the same supplier. The objective is to minimize the ordering and holding cost of the purchasing firm. The problem is frequently solved assuming products with constant unit cost. An efficient algorithm for solving the joint replenishment problem for products that may be experiencing unit cost increase or decrease is developed. The proposed algorithm is tested on a sample of randomly generated problems containing up to 25 items and it is shown that it identifies the global optimal solutions for most of these problems. For the worst case where the algorithm fails to identify the global optimal solution, the solution it provides had a total cost 0.007% above the global minimum.

Neural networks and customer grouping in e-commerce: a framework using fuzzy ART

This paper introduces a proposed neural network-based data mining method that utilizes a companys internal data about customers for the purpose of marketing strategies such as target marketing and direct marketing. Unlike past data mining approaches that put market survey or customer feedback data into input values, the fuzzy ART neural network proposed in this paper takes a customers purchasing history as input values and clusters similar customers into groups. Step by step procedures of how to implement the fuzzy ART algorithm are provided and practical considerations for it to be used in marketing management in the context of electronic commerce are discussed.

An Investigation of the Roles of Electronic Marketplace in the Supply Chain

Electronic marketplace (EM) has been considered lately as an alternative coordination mechanism between suppliers and buyers in a supply chain. In this paper an appropriate model for EM-based supply chain is developed in order to investigate the impact of EM on two important supply chain performance measures - total supply chain cost per sale and customer fill rate. We adopt a simulation modeling approach and conduct experiments with key factors which include auction-based coordination mechanism. In addition, supply chain effects such as demand pooling, matching and aggregation effects in procurement of parts, etc. are also considered. The results indicate that EM could bring operational benefits even though strategic and situational factors that change the magnitude of benefits may occasionally prevent EM from being successful in a supply chain.

Information technology and interorganizational learning

We model an interorganizational learning to examine the implications of IT.A framework is developed to select appropriate knowledge management strategies.The size of a firm is important in determining its learning mechanism and strategy.Fast learning from partners is beneficial when a firms internal learning is slow. In this paper, we study the impact of IT-enabled learning mechanisms and learning strategies on the long-term knowledge outcomes of a firm in an interorganizational setting. Consistent with prior research in this area, we use a computational simulation model to study four IT-enabled learning mechanisms: internal electronic communication networks, external communication networks, company knowledge repositories and portals, and interorganizational knowledge repositories and portals. We also explore the interactions between a firms internal and external learning strategies and these learning mechanisms under three different scenarios of partner size symmetry.

Software Diversity for Improved Network Security: Optimal Distribution of Software-Based Shared Vulnerabilities

Firms, and other agencies, tend to adopt widely used software to gain economic benefits of scale, which can lead to a software monoculture. This can, in turn, involve the risk of correlated computer systems failure as all systems on the network are exposed to the same software-based vulnerabilities. Software diversity has been introduced as a strategy for disrupting such a monoculture and ultimately decreasing the risk of correlated failure. Nevertheless, common vulnerabilities can be shared by different software products. We thus expand software diversity research here and consider shared vulnerabilities between different software alternatives. We develop a combinatorial optimization model of software diversity on a network in an effort to identify the optimal software distribution that best improves network security. We also develop a simulation model of virus propagation based on the susceptible-infected-susceptible model. This model allows calculation of the epidemic threshold, a measure of network re...