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An Analysis of Complexity of Insider Attacks to Databases
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2021 | ACM Transactions on Management Information Systems | Citations: 0
Authors: Kul, Gökhan; Upadhyaya, Shambhu; Hughes, Andrew

Abstract: Insider attacks are one of the most dangerous threats to an organization. Unfort ... Expand

Abstract: Insider attacks are one of the most dangerous threats to an organization. Unfortunately, they are very difficult to foresee, detect, and defend against due to the trust and responsibilities placed on the employees. In this article, we first define the notion of user intent and construct a model for a common scenario that poses a very high risk for sensitive data stored in the organization’s database. We show that the complexity of identifying pseudo-intents of a user in this scenario is coNP-Complete, and launching a harvester insider attack within the boundaries of the defined threat model takes linear time while a targeted threat model is an NP-Complete problem. We also discuss the general defense mechanisms against the modeled threats and show that countering the harvester insider attack takes quadratic time while countering the targeted insider attack can take linear to quadratic time, depending on the strategy chosen. We analyze the adversarial behavior and show that launching an attack with minimum risk is also an NP-Complete problem. Finally, we perform timing experiments with the defense mechanisms on SQL query workloads collected from a national bank to test the feasibility of using these systems in real time. Collapse

Semantic filters: random-access memory

Topics: database system structured query language insider threat IT security defense random-access memory

Methods: cluster analysis experiment computational algorithm digital trace data k-means clustering
A Graph-Based Ant Algorithm for the Winner Determination Problem in Combinatorial Auctions
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2021 | Information Systems Research | Citations: 0
Authors: Ray, Abhishek; Ventresca, Mario; Kannan, Karthik

Abstract: Combinatorial auctions (CAs) are used to allocate bundles of items among interes ... Expand

Abstract: Combinatorial auctions (CAs) are used to allocate bundles of items among interested bidders. However, to resolve bidder preference elicitation problems, CAs are conducted iteratively. Winner determination is a key bottleneck that restricted the widespread adoption of such iterative CAs. Time bounded winner determination is further complicated by the increased variety and velocity of bids each round, and so regular solvers such as IBM CPLEX and A Mathematical Programming Language have been demonstrably ineffective in determining winners within a short duration. In response, heuristic-based methods have enjoyed an increase in applicability. We add to the growing body of work in heuristic-based winner determination by proposing an ant colony–based anytime algorithm (traveling ants for combinatorial auctions, or TrACA) that produces optimal or near-optimal winner determination results within specified time. Experiments are performed on 94 open test instances that display a variety in bid-bundle composition. We show and compare the performance of TrACA to 20 most recent state-of-the-art heuristics and two of the best exact algorithms (CPLEX and Max W Clique). Compared with 12 of 20 heuristics that employ classic search (e.g., stochastic local search, tabu search) or genetic or memetic algorithms, TrACA does significantly better on all instances. On the other eight heuristics that employ advanced hybrid search methodologies (e.g., hyperheuristics, biased random keys), TrACA performs significantly better on the harder test instances. Additionally, we show that TrACA resolves the trade-off between time and accuracy by achieving optimal solutions on 76 out of 94 instances, in at most one-sixth the time taken by exact algorithms. Finally, we analyze the TrACA search process and highlight factors that make TrACA suitable for solving hard auction instances within a prespecified (short) duration. Collapse

Semantic filters: random-access memory

Topics: source code random-access memory Microsoft Windows virtualization technology cloud computing

Methods: computational algorithm experiment nonparametric test Mann Whitney U test optimization model
A data-driven methodology for the automated configuration of online algorithms
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2020 | Decision Support Systems | Citations: 0
Authors: Dunke, Fabian; Nickel, Stefan

Abstract: With the goal of devising algorithms for decision support in operational tasks, ... Expand

Abstract: With the goal of devising algorithms for decision support in operational tasks, we introduce a new methodology for the automated configuration of algorithms for combinatorial online optimization problems. The procedure draws upon available instance data and is capable of recognizing data patterns which prove beneficial to the overall out come. Since online optimization requires repetitive decision making without complete future information, no online algorithm can be optimal for every instance and it is reasonable to restrict attention to rule-based algorithms. We consider such algorithms in the form of procedures which derive their decisions using a threshold value. Threshold values are computed by evaluating a mathematical term (threshold value expression) composed of the available instance data elements. The goal then consists of determining the structure of the threshold value expression leading to the best algorithm performance. To this end, we employ a simulated annealing scheme returning the most fa vorable term composition given the available instance data. The resulting methodology can be implemented as part of data-driven decision support systems in order to facilitate knowledge-based decision making. Decision rules are generated in an automated fashion once historical input data is provided. The methodology is successfully in stantiated in a series of computational experiments for three classes of combinatorial online optimization problems (scheduling, packing, lot sizing). Results show that automatically configured online algorithms are even capable of substantially outperforming well-known online algorithms in respective problem settings. We attribute this effect to the methodology's capability of integrating instance data into the process of algorithm configuration. Collapse

Semantic filters: random-access memory

Topics: decision making knowledge base desktop computer random-access memory Microsoft Windows

Methods: computational algorithm experiment optimization model simulation sensitivity analysis
Uplift Forest for Multiple Treatments and Continuous Outcomes

2020 | International Conference on Information Systems | Citations: 0
Authors: Gubela, Robin M; Lessmann, Stefan

Abstract: Artificial intelligence (AI) models support digital commerce through enabling th ... Expand

Abstract: Artificial intelligence (AI) models support digital commerce through enabling the personalization of communication and services. The paper focuses on predictive models for targeting marketing actions. Extracting information on customers’ preferences from big data, such models forecast the reaction of customers to marketing stimuli. Prior research uses models for treatment effect estimation and focuses on a single treatment. In practice, digital marketers have many options to approach customers with different messages and over diverse channels. The support of these decisions requires multiple treatment models. The paper proposes an algorithm for multiple treatments and continuous outcomes. The tree-based algorithm relies on a new splitting criterion, which identifies the optimal treatment by optimizing outcome heterogeneities between treatments. It supports different evaluation measures and experimental designs by incorporating propensity scores. Empirical results on two real-world marketing data sets confirm the effectiveness of our approach compared to state-of-the-art benchmarks. Collapse

Semantic filters: random-access memory

Topics: electronic mail marketing management decision making missing data decision support

Methods: experimental group computational algorithm random forest classification experiment propensity score method
Generating Artificial Data for Empirical Analysis of Control-flow Discovery Algorithms
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2019 | Business & Information Systems Engineering | Citations: 0
Authors: Jouck, Toon; Depaire, Benoît

Abstract: Within the process mining domain, research on comparing control-flow (CF) discov ... Expand

Abstract: Within the process mining domain, research on comparing control-flow (CF) discovery techniques has gained importance. A crucial building block of empirical analysis of CF discovery techniques is obtaining the appropriate evaluation data. Currently, there is no answer to the question of how to collect such evaluation data. The paper introduces a methodology for generating artificial event data (GED) and an implementation called the Process Tree and Log Generator. The GED methodology and its implementation provide users with full control over the characteristics of the generated event data and an integration within the ProM framework. Unlike existing approaches, there is no tradeoff between including long-term dependencies and soundness of the process. The contributions of the paper provide a solution for a necessary step in the empirical analysis of CF discovery algorithms. Collapse

Semantic filters: random-access memory

Topics: Python laptop computer random-access memory systems implementation

Methods: computational algorithm event log business process modeling descriptive statistic simulation
An IoT Software Architecture for an Evacuable Building Architecture

2019 | HICSS | Citations: 0
Authors: Muccini, Henry; Arbib, Claudio; Davidsson, Paul; Moghaddam, Mahyar T

Abstract: This paper presents a computational component designed to improve and evaluate e ... Expand

Abstract: This paper presents a computational component designed to improve and evaluate emergency handling plans. In real-time, the component operates as the core of an Internet of Things (IoT) infrastructure aimed at crowd monitoring and optimum evacuation paths planning. In this case, a software architecture facilitates achieving the minimum time necessary to evacuate people from a building. In design-time, the component helps discovering the optimal building dimensions for a safe emergency evacuation, even before (re-) construction of a building. The space and time dimension are discretized according to metrics and models in literature. The component formulates and solves a linearized, time-indexed ﬂow problem on a network that represents feasible movements of people at a suitable frequency. The CPU time to solve the model is compliant with real-time use. The application of the model to a real location with real data testiﬁes the model capability to optimize the safety standards by small changes in the building dimensions, and guarantees an optimal emergency evacuation performance. Collapse

Semantic filters: random-access memory

Topics: mobile system internet of things Microsoft Windows earthquake radio frequency identification

Methods: simulation computational algorithm experiment case study
A Stochastic Program for Black Start Allocation

2019 | HICSS | Citations: 0
Authors: Patsakis, Georgios; Aravena, Ignacio; Livermore, Lawrence

Abstract: The fast and secure restoration of the power system after an extended blackout h ... Expand

Abstract: The fast and secure restoration of the power system after an extended blackout highly depends on the location of Black Start (BS) resources. In contrast to most generators, BS units have the ability to start without being connected to an already energized power grid. Selecting a unit to provide BS services is associated with costly technical upgrades, continuous testing, and compensation for the services, and once a unit is selected as BS it is expected to provide that service for several years. For these reasons, the selection process to allocate new BS units is very important and currently handled by experts in the ﬁeld. Building on the existing literature for power system restoration and black start allocation, we formulate an optimization problem aimed at allocating BS units optimally in the power grid. While restoration plans are usually examined under the assumption of a total blackout, in reality most blackouts are partial, leaving parts of the grid energized and certain elements damaged. In order to account for these cases during the selection process, we formulate a two-stage stochastic program that optimizes the allocation of BS resources over a number of outage scenarios. We use a scenario decomposition algorithm to solve the resulting optimization problem to near-optimality in a high performance computing environment. We conduct numerical experiments using the proposed model and decomposition method on the IEEE-39 test system. Collapse

Semantic filters: random-access memory

Topics: Python random-access memory high-performance computing

Methods: computational algorithm optimization model simulation experiment
Offloading for Mobile Device Performance Improvement

2019 | HICSS | Citations: 0
Authors: Temesgene, Dagnachew; Porras, Jari; Parkkila, Janne

Abstract: Mobile devices are increasingly becoming part of everyday life. These include sm ... Expand

Abstract: Mobile devices are increasingly becoming part of everyday life. These include smart phones, tablets, wearable devices etc. Due to their mobility aspect, they are always constrained in their size and weight, which limits their resource capacity, e.g. processing power, and battery life. One possible solution for augmentation of such resource-constrained devices is through efficient usage of their surrounding resources, i.e. using some offloading technique. This paper studies how offloading of tasks to the surrounding resources affects on both the performance of task execution as well as the battery life of the mobile device. Two mobile phones and two tablets (from two different manufacturers) are studied in the experiments to find out the impact of the device characteristics. Two computationally demanding tasks, namely image processing and encryption/decryption, are used in these experiments. These results are compared to our earlier results on mobile devices of previous generations. We assumed that the increased computing power of new devices would make offloading obsolete. Our results show gains both in energy saving and in computational performance with these mobile devices. The comparison to our earlier results show that the performance increase of newer mobile device generations has not diminished the benefits of offloading. These results are in line with results presented in literature and they show that the offloading could offer a viable approach for resource augmentation of mobile devices towards edge/fog resources emphasized by the new 5G technology. Collapse

Semantic filters: random-access memory

Topics: mobile system data encryption tablet smartphone random-access memory

Methods: experiment image processing
Performance Characterization of State-Of-The-Art Deep Learning Workloads on an IBM “Minsky” Platform

2018 | HICSS | Citations: 0
Authors: Guignard, Mauricio; Schild, Marcelo; Bederian, Carlos S

Abstract: Deep learning algorithms are known to demand signiﬁcant computing horsepower, in ... Expand

Abstract: Deep learning algorithms are known to demand signiﬁcant computing horsepower, in particular when it comes to training these models. The capability of developing new algorithms and improving the existing ones is in part determined by the speed at which these models can be trained and tested. One alternative to attain signiﬁcant performance gains is through hardware acceleration. However, deep learning has evolved into a large variety of models, including but not limited to fully-connected, convolutional, recurrent and memory networks. Therefore, it appears difﬁcult that a single solution can provide effective acceleration for this entire deep learning ecosystem. Collapse

Semantic filters: random-access memory

Topics: application programming interface Python computer hardware Linux operating system

Methods: deep learning TensorFlow convolutional neural network experiment computational algorithm
Not all digital venture ideas are created equal: Implications for venture creation processes
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2018 | Journal of Strategic Information Systems | Citations: 0
Authors: von Briel, Frederik; Recker, Jan; Davidsson, Per

Abstract: Digital ventures are formed around ideas that have digital artifacts at their co ... Expand

Abstract: Digital ventures are formed around ideas that have digital artifacts at their core. We develop theory that explains how the composition of digital artifacts influences venture creation processes. First, we develop propositions that link differences in the embodiment and coupling of digital artifact components to tensions in venture creation process inputs, behaviors, and outputs. Second, we link compositional differences in digital artifacts to differences in venture creation process initiation, duration, and outcome. Our theorizing establishes a foundation for future research on digital artifacts within and beyond entrepreneurship contexts, and for future research on entrepreneurship within and beyond digital artifact contexts. Collapse

Semantic filters: random-access memory

Topics: entrepreneurship digital entrepreneurship user experience electronic market random-access memory

Methods: design artifact theory development experiment laboratory experiment business process modeling

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Found 24 articles

An Analysis of Complexity of Insider Attacks to Databases

A Graph-Based Ant Algorithm for the Winner Determination Problem in Combinatorial Auctions

A data-driven methodology for the automated configuration of online algorithms

Uplift Forest for Multiple Treatments and Continuous Outcomes

Generating Artificial Data for Empirical Analysis of Control-flow Discovery Algorithms

An IoT Software Architecture for an Evacuable Building Architecture

A Stochastic Program for Black Start Allocation

Offloading for Mobile Device Performance Improvement

Performance Characterization of State-Of-The-Art Deep Learning Workloads on an IBM “Minsky” Platform

Not all digital venture ideas are created equal: Implications for venture creation processes