Analysis of malicious input issues on intelligent systems

Intelligent systems can facilitate decision making and have been widely applied to various domains. The output of intelligent systems relies on the users\u27 input. However, with the development of Web-Based Interface, users can easily provide dishonest input. Therefore, the accuracy of the generated decision will be affected. This dissertation presents three essays to discuss the defense solutions for malicious input into three types of intelligent systems: expert systems, recommender systems, and rating systems. Different methods are proposed in each domain based on the nature of each problem. The first essay addresses the input distortion issue in expert systems. It develops four methods to distinguish liars from truth-tellers, and redesign the expert systems to control the impact of input distortion by liars. Experimental results show that the proposed methods could lead to the better accuracy or the lower misclassification cost. The second essay addresses the shilling attack issue in recommender systems. It proposes an integrated Value-based Neighbor Selection (VNS) approach, which aims to select proper neighbors for recommendation systems that maximize the e-retailer\u27s profit while protecting the system from shilling attacks. Simulations are conducted to demonstrate the effectiveness of the proposed method. The third essay addresses the rating fraud issue in rating systems. It designs a two-phase procedure for rating fraud detection based on the temporal analysis on the rating series. Experiments based on the real-world data are utilized to evaluate the effectiveness of the proposed method

Rating Fraud Detection---Towards Designing a Trustworthy Reputation Systems

Reputation systems could help consumers avoid transaction risk by providing historical consumers’ feedback. But, traditional reputation systems are vulnerable to the rating manipulation. It will undermine the trustworthiness of the reputation systems and users’ satisfaction will be lost. To address the issue, this study uses the real-world rating data from two travel website: Tripadvisor.com and Expedia.com and one e-commerce website Amazon.com to empirically exploit the features of fraudulent raters. Based on those features, it proposes the new method for fraudulent rater detection. First, it examines the received rating series of each entity and filter out the entity which is under attack (termed as target entity). Second, the clustering based method is applied to discriminate fraudulent raters. Experimental studies have shown that the proposed method is effective in detecting the fraudulent raters accurately while keeping the majority of the normal users in the systems in various attack environment settings

Fraud detections for online businesses: a perspective from blockchain technology

Background: The reputation system has been designed as an effective mechanism to reduce risks associated with online shopping for customers. However, it is vulnerable to rating fraud. Some raters may inject unfairly high or low ratings to the system so as to promote their own products or demote their competitors. Method: This study explores the rating fraud by differentiating the subjective fraud from objective fraud. Then it discusses the effectiveness of blockchain technology in objective fraud and its limitation in subjective fraud, especially the rating fraud. Lastly, it systematically analyzes the robustness of blockchain-based reputation systems in each type of rating fraud. Results: The detection of fraudulent raters is not easy since they can behave strategically to camouflage themselves. We explore the potential strengths and limitations of blockchain-based reputation systems under two attack goals: ballot-stuffing and bad-mouthing, and various attack models including constant attack, camouflage attack, whitewashing attack and sybil attack. Blockchain-based reputation systems are more robust against bad-mouthing than ballot-stuffing fraud. Conclusions: Blockchain technology provides new opportunities for redesigning the reputation system. Blockchain systems are very effective in preventing objective information fraud, such as loan application fraud, where fraudulent information is fact-based. However, their effectiveness is limited in subjective information fraud, such as rating fraud, where the ground-truth is not easily validated. Blockchain systems are effective in preventing bad mouthing and whitewashing attack, but they are limited in detecting ballot-stuffing under sybil attack, constant attacks and camouflage attack

Understanding Factors Influencing Users’ Retweeting Behavior---A Theoretical Perspective

Currently, a large percentage of tweets in micro-blogging platform are retweets. In this study, we propose to examine the factors that motivate users’ retweeting behavior, leading users to prefer to transform others’ tweets than posting their own. We suggest that Information Sharing Self-Efficacy, Attachment Motivation and Critical Mass are the three antecedents contributing to the users’ retweeting behavior. Both theoretical and practical implications of this study are also discussed

Designing Intelligent Expert Systems to Cope with Liars

To cope with the problem of input distortion by users of Web-based expert systems, we develop methods to distinguish liars from truth-tellers based on verifiable attributes, and redesign the expert systems to control the impact of input distortion. The four methods we propose are termed split tree, consolidated tree, value based split tree, and value based consolidated tree. They improve the performance of expert systems by improving accuracy or reduce misclassification cost. Numerical examples confirm that the most possible accurate recommendation is not always the most economical one. The recommendations based on minimizing misclassification costs are more moderate compared to that based on accuracy. In addition, the consolidated tree methods are more efficient than the split tree methods, since they do not always require the verification of attribute values

An Evidence-Based Analysis for Liver Transplants: Insights for Organ Allocation Policy

This study intends to utilize Evidence-Based Medicine (EBM) to support liver organ allocation policy making. The current liver organ allocation policy---the MELD scoring system suffers from several limitations in evaluating patients’ post-transplant survival. This study intends to develop a more cost effective policy for liver organ allocation which includes both recipients’ factors and donors’ characteristics. Using the liver transplants data collected from UNOS (United Network for Organ Sharing), the impact of donors’ cause of death (COD) on the survival risk of patients with Hepatics B surface antigen (HBsAg) test result positive is analyzed by the two-step analysis. First, the survival analysis is conducted in the entire population to detect the influence of HBsAg and Donor COD on graft survival. Second, the Kaplan-Meier method along with Cox proportional hazards models is adopted to explore whether there is an interaction effect between HBsAg and Donor COD

The black ocean strategy in Thailand logistic industry

The logistic is the core of Thai economy. While many business authors are focused on blue ocean strategy in pursuit of tapping into uncontested market space by using differentiation and low cost. There is another strategy that are most commonly use but not much revealed in the literature. It is named black ocean strategy and commonly use in logistic industry. It is the secret mantra from the past which still widely use in today business world. This paper has focus on used car sector as a part of logistic industry to study the viable of this strategy and found that black ocean is commonly used by used car companies. Since the automotive tax in Thailand is pretty high many logistic companies prefer to go for used car which is more economy. The study found that black ocean strategy is the viable tools to reduce the purchasing cost as well as increase the selling price for both logistic buyer and purchaser.The article is licensed under a Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0 International License, https://creativecommons.org/licenses/by-nc/4.0/fi=vertaisarvioitu|en=peerReviewed

Genetic and Functional Dissection of HTRA1 and LOC387715 in Age-Related Macular Degeneration

A common haplotype on 10q26 influences the risk of age-related macular degeneration (AMD) and encompasses two genes, LOC387715 and HTRA1. Recent data have suggested that loss of LOC387715, mediated by an insertion/deletion (in/del) that destabilizes its message, is causally related with the disorder. Here we show that loss of LOC387715 is insufficient to explain AMD susceptibility, since a nonsense mutation (R38X) in this gene that leads to loss of its message resides in a protective haplotype. At the same time, the common disease haplotype tagged by the in/del and rs11200638 has an effect on the transcriptional upregulation of the adjacent gene, HTRA1. These data implicate increased HTRA1 expression in the pathogenesis of AMD and highlight the importance of exploring multiple functional consequences of alleles in haplotypes that confer susceptibility to complex traits