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A Model for and Inventory of Cybersecurity Values: Metrics and Best Practices
Published in Natalie M. Scala, James P. Howard, Handbook of Military and Defense Operations Research, 2020
Natalie M. Scala, Paul L. Goethals
Beyond the attack tree and attack surface, most threat models incorporate some type of metric to quantify the level of risk in a system or process for a decision-maker. For example, trust metrics are used to detect malicious bot activity in networks, aid in authenticating user credentials, or support cryptographic methods. Metrics developed to infer the intent of an individual utilize pattern matching algorithms, hypothesis testing, or statistical process control to differentiate between anomalous and normal behavior. To properly design a metric to aid in strengthening defense, the security environment must be fully understood. This includes the nature of the threat and also previous attempts to quantify risk in this space. The remaining portion of this chapter is afforded to designing or selecting metrics grounded in defensive principles for the cyber domain.
Measuring the complexity of migration transition: an attempt using metrics
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2020
Harjot Kaur, Karanjeet Singh Kahlon
A trust metric system for the multi-agent environment has been established (Bista et al., 2006), in which authors have formulated various parameters on the basis of whom the trust metrics can be calculated. The value of trust metrics related to an agent has been calculated by measuring the overall reputation possessed by it. For quantifying the reputation of various agents, a construct of a referral network has been used. This construct uses a special type of agent called witness agent to provide the referral ratings of reputation or various agents in a multi-agent system. The presented trust metrics have also been evaluated by setting up an e-commerce environment comprising various trading agents. These agents use the presented trust metrics to assess the trustworthiness of other agents in an e-commerce environment.
Trust and Distrust based Cross-domain Recommender System
Published in Applied Artificial Intelligence, 2021
Trust in recommendation found its place among researchers as an important additional factor to provide personalized recommendation. (O’Donovan and Smyth 2005) provide the computational models of trust and also showed that incorporating trust into standard approach improves prediction accuracy. The trust modeling, propagation and aggregation has been discussed by (Victor, Cock, and Cornelis 2011) as trust enhancement in recommendation approach. Further, they discussed about the importance of trust enhancement focusing on the trust metric and the operators used in trust-based recommendation. A trust-based collaborative filtering for the recommendation purpose is proposed by (Massa and Avesani 2004). A trust-based approach in CF is introduced by (Papagelis, Plexousakis, and Kutsuras 2005) which uses trust inferences as transitive association between users in social network using context. They have proposed a trust computational model that applies confidence and uncertainty properties in the subjective notion of trust that helps to deal with sparsity and cold-start problems. CF is a widely used algorithm in recommendation which is further enhanced by introducing trust factor in collaborative approach by (Lathia, Hailes, and Capra 2008). (Hwang and Chen 2007) has made another attempt to introduce trust in CF by directly including trust factor in CF recommendation process. They compute the trust score directly from the user rating data and included trust propagation from the web-of-trust. A CF approach using clustering of trust and distrust is proposed by (Ma et al. 2017). They have used SVD sign based clustering algorithm to process trust and distrust-based matrix to discover the community of trusted user. A classification scheme for trust metric is proposed by (Ziegler and Lausen 2004) for semantic web scenarios for the computation of local group trust computation.(Abdul-Rahman and Hailes 2000) has proposed a community-based trust model in the real world social trust characteristics using reputation mechanism. An effort has been made by (Golbeck, Parsia, and Hendler 2003) which exploits trust metrics in social networks. They discussed the semantic web in the multi-dimensional networks which evolves from ontological trust specification. (Jamali and Ester 2009) combines the trust and collaborative filtering approach to measure the confidence of the recommendation approach.