Competing Hazard Model of Household Vehicle Transaction Behavior with Discrete Time Intervals and Unobserved Heterogeneity

Co-authored with Martina Z. Frignani, Joshua Auld, Abolfazl Mohammadian, and Peter C. Nelson.  Tentatively accepted for publication in Transportation Research Record, 2010.

Attribute Constrained Rules for Partially Labeled Sequence Completion

Co-authored with Peter C. Nelson and Abolfazl Mohammadian, published in Lecture Notes in Computer Science: Advances in Data Mining - Applications and Theoretical Aspects, Springer Berlin/Heidelberg, 5633, July 2009.

Sequential pattern and rule mining have been the focus of much research, however predicting missing sets of elements within a sequence remains a challenge. Recent work in survey design suggests that if these missing elements can be inferred with a higher degree of certainty, it could greatly reduce the time burden on survey participants. To address this problem and the more general problem of missing sensor data, we introduce a new form of constrained sequential rules that use attribute presence to better capture rule confidence in sequences with missing data than previous constraint based techniques. Specifically we examine the problem of given a partially labeled sequence of sets, how well can the missing attributes be inferred. Our study shows this technique significantly improves prediction robustness when even large amounts of data are missing compared to traditional techniques.

Competing Hazard Model of Household Vehicle Transaction Behavior with Discrete Time Intervals and Unobserved Heterogeneity

Co-authored with Martina Z. Frignani, Joshua Auld, Abolfazl Mohammadian, and Peter C. Nelson.  To appear in Proceedings of the 89th Annual Meeting of the Transportation Research Board, Washington, D.C., January 2010

This paper presents the results of an internet-based prompted recall activity-travel survey using GPS data collection combined with a short activity preplanning and scheduling survey. Besides collecting traditional activity-travel diary data, this survey also collects basic information about activity planning and the scheduling process. Forty two households comprising 51 individuals have been surveyed . Since aging is a growing concern among transportation planners, this survey has a special focus on the elderly population with half of the survey sample consisting of elderly households.  Respondents carried a portable GPS device for 14 consecutive days and at the end of each day uploaded the collected data to a website where the activity-travel survey questionnaires were answered. Results indicate that the quality of the data collected is good and that the response rates were satisfactory considering the commitment involved in participation. The results reinforce previous findings that GPS surveys have an improved ability to capture trips which are frequently under-reported and provide valuable data about the activity planning and scheduling process itself. Respondents' feedback on their participation experience and fatigue and conditioning analysis reveal that this type of survey has a great potential for survey periods longer than two weeks.

An Automated GPS-Based Prompted Recall Survey With Learning Algorithms

Co-authored with Joshua Auld, Abolfazl (Kouros) Mohammadian, and Peter C. Nelson, Transportation Letters: The International Journal of Transportation Research, J. Ross Publishing, Vol. 1, No. 1, pp. 59-79, January 2009

Using GPS technology in the collection of household travel data has been gaining importance as the technology matures. This paper documents recent developments in the field of GPS travel surveying and ways in which GPS has been incorporated into or even replaced traditional household travel survey methods. A new household activity survey is presented which uses automated data reduction methods to determine activity and travel locations based on a series of heuristics developed from land-use data and travel characteristics. The algorithms are used in an internet-based prompted recall survey which utilizes advanced learning algorithms to reduce the burden placed on survey respondents. Initial results of a small pilot study are discussed and potential areas of future work are presented.

Mining Sequential Association Rules for Traveler Context Prediction

Co-authored with Abolfazl (Kouros) Mohammadian, Peter C. Nelson, and Sean T. Doherty.  Published in The First International Workshop on Computational Transportation Science (IWCTS’08). Held at The International Conference on Mobile and Ubiquitous Systems: Networks and Services (MOBIQUITOUS 2008), Dublin, Ireland, July 2008

Recent work has focused on creating models for generating traveler behavior for micro simulations. With the increase in hand held computers and GPS devices, there is likely to be an increasing demand for extending this idea to predicting an individual’s future travel plans for devices such as a smart traveler’s assistant. In this work, we introduce a technique based on sequential data mining for predicting multiple aspects of an individual’s next activity using a combination of user history and their similarity to other travelers. The proposed technique is empirically shown to perform better than more traditional approaches to this problem.

Defending Recommender Systems: Detection of Profile Injection Attacks

Co-authored with Bamshad Mobasher and Robin Burke. published in Journal of Service Oriented Computing and Applications, Vol. 1, No. 3, pp. 157-170, November 2007 

Collaborative recommender systems are known to be highly vulnerable to profile injection attacks, attacks that involve the insertion of biased profiles into the ratings database for the purpose of altering the system’s recommendation behavior. Prior work has shown when profiles are reverse engineered to maximize influence; even a small number of malicious profiles can significantly bias the system. This paper describes a classification approach to the problem of detecting and responding to profile injection attacks. A number of attributes are identified that distinguish characteristics present in attack profiles in general, as well as an attribute generation approach for detecting profiles based on reverse engineered attack models. Three well known classification algorithms are then used to demonstrate the combined benefit of these attributes and the impact the selection of classifier has with respect to improving the robustness of the recommender system. Our study demonstrates this technique significantly reduces the impact of the most powerful attack models previously studied, particularly when combined with a support vector machine classifier.

Towards Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm Robustness

Co-authored with Bamshad Mobasher, Robin Burke, and Runa Bhaumik.  Published in ACM Transactions on Internet Technology, Vol. 7, No. 4, pp. 23-60, October 2007

Publicly accessible adaptive systems such as collaborative recommender systems present a security problem. Attackers, who cannot be readily distinguished from ordinary users, may inject biased profiles in an attempt to force a system to "adapt" in a manner advantageous to them. Such attacks may lead to a degradation of user trust in the objectivity and accuracy of the system. Recent research has begun to examine the vulnerabilities and robustness of different collaborative recommendation techniques in the face of "profile injection" attacks. In this article, we outline some of the major issues in building secure recommender systems, concentrating in particular on the modeling of attacks and their impact on various recommendation algorithms. We introduce several new attack models and perform extensive simulation-based evaluations to show which attacks are most successful and practical against common recommendation techniques. Our study shows that both user-based and item-based algorithms are highly vulnerable to specific attack models, but that hybrid algorithms may provide a higher degree of robustness. Using our formal characterization of attack models, we also introduce a novel classification-based approach for detecting attack profiles and evaluate its effectiveness in neutralizing attacks.

Classification Features for Attack Detection in Collaborative Recommender Systems

Co-authored with Robin Burke, Bamshad Mobasher, and Runa Bhaumik.  Published in Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'06) Philadelphia, Pennsylvania, August 2006.

Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in recommendations that favor or disfavor given items. Since collaborative recommender systems must be open to user input, it is difficult to design a system that cannot be so attacked. Researchers studying robust recommendation have therefore begun to identify types of attacks and study mechanisms for recognizing and defeating them. In this paper, we propose and study different attributes derived from user profiles for their utility in attack detection. We show that a machine learning classification approach that includes attributes derived from attack models is more successful than more generalized detection algorithms previously studied.

The Impact of Attack Profile Classification on the Robustness of Collaborative Recommendation

Co-authored with Runa Bhaumik, Robin Burke, and Bamshad Mobasher.  Published in Proceedings of the 2006 WebKDD Workshop Held at KDD'2006, Philadelphia, Pennsylvania, August 2006.

Collaborative recommender systems have been shown to be vulnerable to profile injection attacks. By injecting a large number of biased profiles into a system, attackers can manipulate the predictions of targeted items. To decrease this risk, researchers have begun to study mechanisms for detecting and preventing profile injection attacks. In prior work, we proposed several attributes for attack detection and have shown that a classifier built with them can be highly successful at identifying attack profiles. In this paper, we extend our work through a more detailed analysis of the information gain associated with these attributes across the dimensions of attack type and profile size. We then evaluate their combined effectiveness at improving the robustness of user based recommender systems.

Detection of Obfuscated Attacks in Collaborative Recommender Systems

Co-authored Bamshad Mobasher, Robin Burke, Jeff Sandvig, and Runa Bhaumik.  Published in Proceedings of the ECAI’06 Workshop on Recommender Systems Held at the 17th European Conference on Artificial Intelligence (ECAI'06), Riva del Garda, Italy, August, 2006.

The vulnerability of collaborative recommender systems has been well established; particularly to reverse-engineered attacks designed to bias the system in an attacker’s favor. Recent research has begun to examine detection schemes to recognize and defeat the effects of known attack models. In this paper we propose several techniques an attacker might use to modify an attack to avoid detection, and show that these obfuscated versions can be nearly as effective as the reverse-engineered models yet harder to detect. We explore empirically the impact of these obfuscated attacks against systems with and without detection, and discuss alternate approaches to reducing the effectiveness of such attacks.

Securing Collaborative Filtering Against Malicious Attacks Through Anomaly Detection

Co-authored with Runa Bhaumik, Bamshad Mobasher, and Robin Burke. Published in Proceedings of the 4th Workshop on Intelligent Techniques for Web Personalization (ITWP'06) Held at AAAI 2006, Boston, Massachusetts, July 2006.

Collaborative filtering recommenders are highly vulnerable to malicious attacks designed to affect predicted ratings. Previous work related to detecting such attacks has focused on detecting profiles. Approaches based on profile classification to a large extent depend on profiles conforming to known attack models. In this paper we examine approaches for detecting suspicious rating trends based on statistical anomaly detection. We empirically show these techniques can be highly successful at detecting items under attack and time intervals when an attack occurred. In addition we explore the effects of rating distribution on detection performance and show that this varies based on distribution characteristics when these techniques are used.

Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation

Co-authored with Bamshad Mobasher, Robin Burke, and Runa Bhaumik.  Published in Advances in Web Mining and Web Usage Analysis Olfa Nasraoui, Osmar R. Zaïane, Myra Spiliopoulou, Bamshad Mobasher, Brij Masand, and Philip S. Yu (eds.). Lecture Notes in Computer Science (Vol. 4198), Springer, 2006.

Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems and their reliance on userspecified judgments for building profiles. Attackers can easily introduce biased data in an attempt to force the system to "adapt" in a manner advantageous to them. Our research in secure personalization is examining a range of attack models, from the simple to the complex, and a variety of recommendation techniques. In this chapter, we explore an attack model that focuses on a subset of users with similar tastes and show that such an attack can be highly successful against both user-based and item-based collaborative filtering. We also introduce a detection model that can significantly decrease the impact of this attack.

Detecting Profile Injection Attacks in Collaborative Recommender Systems

Co-authored with Robin Burke, Bamshad Mobasher, and Runa Bhaumik.  Published in Proceedings of the 8th IEEE Conference on E-Commerce Technology (CEC' 06) San Francisco, California, June 2006.  Winner of best paper award.

Evaluation of Profile Injection Attacks In Collaborative Recommender Systems

Co-authored with Runa Bhaumik, Jeff Sandvig, Bamshad Mobasher, and Robin Burke.  Published in DePaul CTI Research Symposium / Midwest Software Engineering Conference (CTIRS/MSEC 2006) Chicago, Illinois, April 2006.  Winner of best paper award.

Segment-Based Injection Attacks against Collaborative Recommender Systems

Co-authored with Robin Burke, Bamshad Mobasher, and Runa Bhaumik.  Published in Proceedings of the International Conference on Data Mining (ICDM 2005) Houston, Texas, November 2005.

Collaborative Recommendation Vulnerability To Focused Bias Injection Attacks

Co-authored with Robin Burke, Bamshad Mobasher, and Runa Bhaumik.  Published in Proceedings of the Workshop on Privacy and Security Aspects of Data Mining Held at ICDM'05, Houston, Texas, November 2005.

Profile Injection Attack Detection for Securing Collaborative Recommender Systems

Masters Thesis, advisor Bamshad Mobasher, DePaul University 2006

Researchers have shown that collaborative recommender systems, the most common type of web personalization system, are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in recommendations that favor or disfavor given items. Since collaborative recommender systems must be open to user input, it is difficult to design a system that cannot be so attacked. Researchers studying robust recommendation have therefore begun to study mechanisms for recognizing and defeating attacks. In prior work, we have introduced a variety of attributes designed to detect profile injection attacks and evaluated their combined classification performance against several well studied attack models using supervised classification techniques. In this paper, we propose and study the impact the dimensions of attack type, attack intent, filler size, and attack size have on the effectiveness of such a detection scheme. We conclude by experimentally exploring the weaknesses of a detection scheme based on supervised classification, and techniques that can be combined with this approach to address these vulnerabilities.

Genetically Evolving Optimal Neural Networks

Published in Neural Networks and Expert Systems, The Institute of Chartered Financial Analysts of India (ICFAI)