RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks

This paper proposes a novel intrusion detection system (IDS), named RDTIDS, for Internet-of-Things (IoT) networks. The RDTIDS combines different classifier approaches which are based on decision tree and rules-based concepts, namely, REP Tree, JRip algorithm and Forest PA. Specifically, the first and second method take as inputs features of the data set, and classify the network traffic as Attack/Benign. The third classifier uses features of the initial data set in addition to the outputs of the first and the second classifier as inputs. The experimental results obtained by analyzing the proposed IDS using the CICIDS2017 dataset and BoT-IoT dataset, attest their superiority in terms of accuracy, detection rate, false alarm rate and time overhead as compared to state of the art existing schemes

Privacy-Preserving Schemes for Ad Hoc Social Networks: A Survey

We review the state of the art of privacypreserving schemes for ad hoc social networks including mobile social networks (MSNs) and vehicular social networks (VSNs). Specifically, we select and examine in-detail 33 privacy-preserving schemes developed for or applied in the context of ad hoc social networks. Based on novel schemes published between 2008 and 2016, we survey privacy preservation models including location privacy, identity privacy, anonymity, traceability, interest privacy, backward privacy, and content oriented privacy. Recent significant attacks of leaking privacy, countermeasures, and game theoretic approaches in VSNs and MSNs are summarized in the form of tables. In addition, an overview of recommendations for further research is provided. With this survey, readers can acquire a thorough understanding of research trends in privacy-preserving schemes for ad hoc social networks

Vehicular-cloud simulation framework for predicting traffic flow data

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