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Proceedings ArticleDOI

High-Performance Monitoring Sensors for Home Computer Users Security Profiling

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TLDR
This research tries to identify the monitoring factors and suggests a novel observation solution to create high-performance sensors to generate the user security profile for a home user concerning the user’s privacy.
Abstract
Recognising user’s risky behaviours in real-time is an important element of providing appropriate solutions and recommending suitable actions for responding to cybersecurity threats. Employing user modelling and machine learning can make this process automated by requires high-performance intelligent agent to create the user security profile. User profiling is the process of producing a profile of the user from historical information and past details. This research tries to identify the monitoring factors and suggests a novel observation solution to create high-performance sensors to generate the user security profile for a home user concerning the user’s privacy. This observer agent helps to create a decision-making model that influences the user’s decision following real-time threats or risky behaviours.

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References
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Book

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TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
Journal ArticleDOI

Automatic personalization based on Web usage mining

TL;DR: The ability to track users’ browsing behavior down to individual mouse clicks has brought the vendor and end customer closer than ever before, and it is now possible for a vendor to personalize his product message for individual customers at a massive scale, a phenomenon that is being referred to as mass customization.
Journal ArticleDOI

Ontological user profiling in recommender systems

TL;DR: Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy are shown.
Proceedings ArticleDOI

You've been warned: an empirical study of the effectiveness of web browser phishing warnings

TL;DR: Using a model from the warning sciences, how users perceive warning messages is analyzed and suggestions for creating more effective warning messages within the phishing context are offered.
Book ChapterDOI

Designing and Evaluating Explanations for Recommender Systems

TL;DR: This chapter gives an overview of the area of explanations in recommender systems, and approaches the literature from the angle of evaluation: that is, what makes an explanation “good”, and suggest guidelines as how to best evaluate this.
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