Ambuj Mahanti

Bio: Ambuj Mahanti is an academic researcher from Indian Institute of Management Calcutta. The author has contributed to research in topics: Heuristic & Best-first search. The author has an hindex of 14, co-authored 53 publications receiving 594 citations. Previous affiliations of Ambuj Mahanti include University of Maryland, College Park.

Cyber-risk decision models: To insure IT or not?

Abstract: Security breaches adversely impact profit margins, market capitalization and brand image of an organization. Global organizations resort to the use of technological devices to reduce the frequency of a security breach. To minimize the impact of financial losses from security breaches, we advocate the use of cyber-insurance products. This paper proposes models to help firms decide on the utility of cyber-insurance products and to what extent they can use them. In this paper, we propose a Copula-aided Bayesian Belief Network (CBBN) for cyber-vulnerability assessment (C-VA), and expected loss computation. Taking these as an input and using the concepts of collective risk modeling theory, we also compute the premium that a cyber risk insurer can charge to indemnify cyber losses. Further, to assist cyber risk insurers and to effectively design products, we propose a utility based preferential pricing (UBPP) model. UBPP takes into account risk profiles and wealth of the prospective insured firm before proposing the premium. Display Omitted Proposed Cyber risk insurance products to minimize the impact of financial loss of security breach.Cyber risk insurance products complement security technology.Our proposed Copula aided Bayesian Belief networks model helps to asses cyber risk.Collective risk & Utility Theory used to computes premium for Cyber risk insurance products.Cyber risks mode for to decide to opt for cyber insurance or not for organizations.

Improving Prediction Accuracy in Trust-Aware Recommender Systems

Abstract: Trust-aware recommender systems are intelligent technology applications that make use of trust information and user personal data in social networks to provide personalized recommendations. Earlier research in trust-aware systems have shown that the ability of trust-based systems to make accurate predictions coupled with their robustness from shilling attacks make them a better alternative than traditional recommender systems. In this paper we propose an approach for improving accuracy of predictions in trust-aware recommender systems. In our approach, we first reconstruct the trust network. Trust network is reconstructed by removing trust links between users having correlation coefficient below a specified threshold value. For prediction calculation we compare three different approaches based on trust and correlation. We show through experiments on real life Epinions data set that our proposed approach of reconstructing the trust network gives substantially better prediction accuracy than the original approach of using all trust statements in the network.

PRA*: a memory-limited heuristic search procedure for the Connection Machine

Abstract: A variant of A* search designed to run on the massively parallel SIMD (single-instruction-stream, multiple-data-steam) Connection Machine is described. The algorithm is designed to run in a limited memory; a retraction technique allows nodes with poor heuristic values to be removed from the open list until such time as they may need reexpansion if more promising paths fail. The algorithm, called PRA* (for parallel retraction A*), takes maximum advantage of the SIMD design of the Connection Machine and is guaranteed to return an optimal path when an admissible heuristic is used. Results comparing PRA* to R. Korf's IDA* (see Artif. Intell. J., vol.27, 1985) for the 15 puzzle show significantly fewer node expansions for PRA*. >

Recommender systems survey

Abstract: Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

Shilling attacks against recommender systems: a comprehensive survey

Abstract: Online vendors employ collaborative filtering algorithms to provide recommendations to their customers so that they can increase their sales and profits. Although recommendation schemes are successful in e-commerce sites, they are vulnerable to shilling or profile injection attacks. On one hand, online shopping sites utilize collaborative filtering schemes to enhance their competitive edge over other companies. On the other hand, malicious users and/or competing vendors might decide to insert fake profiles into the user-item matrices in such a way so that they can affect the predicted ratings on behalf of their advantages. In the past decade, various studies have been conducted to scrutinize different shilling attacks strategies, profile injection attack types, shilling attack detection schemes, robust algorithms proposed to overcome such attacks, and evaluate them with respect to accuracy, cost/benefit, and overall performance. Due to their popularity and importance, we survey about shilling attacks in collaborative filtering algorithms. Giving an overall picture about various shilling attack types by introducing new classification attributes is imperative for further research. Explaining shilling attack detection schemes in detail and robust algorithms proposed so far might open a lead to develop new detection schemes and enhance such robust algorithms further, even propose new ones. Thus, we describe various attack types and introduce new dimensions for attack classification. Detailed description of the proposed detection and robust recommendation algorithms are given. Moreover, we briefly explain evaluation of the proposed schemes. We conclude the paper by discussing various open questions.