Collusion attacks in Internet of Things: Detection and mitigation using a fog based model

© 2017 IEEE. This paper discusses the problem of collusion attacks in Internet of Things (IoT) environments and how mobility of IoT devices increases the difficulty of detecting such types of attacks. It demonstrates how approaches used in detecting collusion attacks in WSNs are not applicable in IoT environments. To this end, the paper introduces a model based on the Fog Computing infrastructure to keep track of IoT devices and detect collusion attackers. The model uses fog computing layer for real-time monitoring and detection of collusion attacks in IoT environments. Moreover, the model uses a software defined system layer to add a degree of flexibility for configuring Fog nodes in order to enable them to detect various types of collusion attacks. Furthermore, the paper highlights the possible overhead on Fog nodes and network when applying the proposed model, and claims that the Fog layer infrastructure can provide the required resources for the scalability of the model

Automatic identification of rhetorical relations among intra-sentence discourse segments in Arabic

© 2019 Inderscience Enterprises Ltd. Identifying discourse relations, whether implicit or explicit, has seen renewed interest and remains an open challenge. We present the first model that automatically identifies both explicit and implicit rhetorical relations among intra-sentence discourse segments in Arabic text. We build a large discourse annotated corpora following the rhetorical structure theory framework. Our list of rhetorical relations is organised into three level hierarchies of 23 fine-grained relations, grouped into seven classes. To automatically learn these relations, we evaluate and reuse features from literature, and contribute three additional features: accusative of purpose, specific connectives and the number of antonym words. We perform experiments on identifying fine-grained and coarse-grained relations. The results show that compared with all the baselines, our model achieves the best performance in most cases, with an accuracy of 91.05%

Toward an automatic summarisation of Arabic text depending on rhetorical relations

Copyright © 2019 Inderscience Enterprises Ltd. Rhetorical relations between two text segments are crucial information and have been proven useful for many natural language processing applications. In this paper, we propose a supervised approach for automatic identifying of rhetorical relations in Arabic texts. Our model attempts to identify both implicit and explicit rhetorical relations between elementary discourse units which will be exploited in automatic summarisation of Arabic texts. To carry out this research, we developed a discourse annotated corpus following the rhetorical structure theory framework with high reliability. Relations annotation was done using a set of 23 fine-grained relations enriched with nuclearity annotation. To automatically learn these relations, we reuse some state of the arts features and contribute new lexical and semantics’ features. The experimental results on fine-grained and coarse-grained relations show that our model achieved best performance relative to all baselines