This work falls in the area of collaborative malware detection systems which rely on expertise and knowledge from multiple different antivirus software for malware detection. A critical component of such systems is the collaborative malware detection decision process. In this paper, we propose a novel decision model, RevMatch, where collaborative malware decisions are made based on labeled malware detection history from participating antiviruses. We evaluate our proposal using real-world malware data sets and demonstrate that collaborative malware detection techniques can improve the malware detection accuracy compared to using a single albeit the best antivirus. Moreover, we demonstrate how RevMatch outperforms all other existing collaborative decision models in terms of detection accuracy while being computationally efficient and robust against various malicious insider attacks.

The issue of cyberbullying is a social concern that has arisen due to the prevalent use of computer technology today. In this paper, we present a multi-faceted solution to mitigate the effects of cyberbullying, one that uses computer technology in order to combat the problem. We propose to provide assistance for various groups affected by cyberbullying (the bullied and the bully, both). Our solution was developed through a series of group projects and includes i) technology to detect the occurrence of cyberbullying ii) technology to enable reporting of cyberbullying iii) proposals to integrate third-party assistance when cyberbullying is detected iv) facilities for those with authority to manage online social networks or to take actions against detected bullies. In all, we demonstrate how this important social problem which arises due to computer technology can also leverage computer technology in order to take steps to better cope with the undesirable effects that have arisen.

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In this article, we introduce a framework for selecting web objects (texts, videos, simulations) from a large online repository to present to patients and caregivers, in order to assist in their healthcare. Motivated by the paradigm of peer-based intelligent tutoring, we model the learning gains achieved by users when exposed to specific web objects in order to recommend those objects most likely to deliver benefit to new users. We are able to show that this streamlined presentation leads to effective knowledge gains, both through a process of simulated learning and through a user study, for the specific application of caring for children with autism. The value of our framework for peer-driven content selection of health information is emphasized through two additional roles for peers: attaching commentary to web objects and proposing subdivided objects for presentation, both of which are demonstrated to deliver effective learning gains, in simulations. In all, we are offering an opportunity for patients to navigate the deep waters of excessive online information towards effective management of healthcare, through content selection influenced by previous peer experiences.Additional Key Words and Phrases: Shareable health knowledge, cyber-based empowering of patients, e-communities for patients and caregivers, computational support for patient-centred care, discovery of new knowledge for decision support, effective information retrieval for healthcare applications ACM Reference Format: John Champaign, Robin Cohen, and Disney Yan Lam. 2015. Empowering patients and caregivers to manage healthcare via streamlined presentation of web objects selected by modeling learning benefits obtained by similar peers.

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