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Attack propagation models within honeypot systems aim at providing insights about attack strategies that target multiple honeypots, rather than analyzing attacks on each honeypot separately. Traditional attack propagation models focus on building a single probabilistic model. This modeling approach may be misleading, since it does not take into consideration contextual information such as the country...
In recent years, transfer learning has been used successfully to improve the predictive performance of collaborative filtering (CF) for sparse data by transferring patterns across domains. In this work, we advance transfer learning (TL) in recommendation systems (RSs), facilitating improvement within a domain rather than across domains. Specifically, we utilize TL for in-domain usage. This reduces...
The emergence and penetration of smart mobile devices has given rise to the development of context-aware systems that utilize sensors to collect available data about users in order to improve various user services. Recently, the use of context-aware recommender systems (CARS) aimed at recommending items to users has expanded, particularly those that consider user context. Adding context to recommendation...
Honey pots are computer resources that are used to detect and deflect network attacks on a protected system. The data collected from honey pots can be utilized to better understand cyber-attacks and provide insights for improving security measures, such as intrusion detection systems. In recent years, attackers' sophistication has increased significantly, thus additional and more advanced analytical...
We present an end-to-end supervised based system for detecting malware by analyzing network traffic. The proposed method extracts 972 behavioral features across different protocols and network layers, and refers to different observation resolutions (transaction, session, flow and conversation windows). A feature selection method is then used to identify the most meaningful features and to reduce the...
In this paper we present Nesto, a network selection and offloading system for android based mobile devices. Nesto chooses the best connectivity solution between available heterogeneous wireless networks using network switching. The suggested framework supports several configurable policies and addresses the following requirements: battery energy saving, bandwidth maximization, an offloading strategy...
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