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Summary Software‐defined networking (SDN) is a new networking architecture that decouples both the control and management planes from the data plane of forwarding devices. Control and management planes are implemented at a logically centralized entity called the controller. Despite numerous advantages, SDN is more prone to logical errors like loops, black holes, network reachability problems, and access control list (ACL) policies violation. In the existing approaches, the network requirements are specified by different network administrators using the ACL policies. SDN allows multiple network administrators to specify the ACL policies simultaneously, which may lead to conflicts and overlaps among the ACL policies. In this research work, a novel technique, called auto‐resolving overlapping and conflicts in ACL policies (ROCA), is proposed to efficiently detect and solve both the conflicts and the overlaps among the ACL policies by using the techniques of set theory, 3D structure, and separating axis theorem. It is shown by simulation and testing on the real network traces that ROCA outperforms the existing approaches in terms of computation time avoiding conflicts and overlapping among the ACL policies.

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Summary On the social Web, the amount of video content either originated from wireless devices or previously received from media servers has increased enormously in the recent years. The astounding growth of Web videos has stimulated researchers to propose new strategies to organize them into their respective categories. Because of complex ontology and large variation in content and quality of Web videos, it is difficult to get sufficient, precisely labeled training data, which causes hindrance in automatic video classification. In this paper, we propose a novel content‐ and context‐based Web video classification framework by rendering external support through category discriminative terms (CDTs) and semantic relatedness measure (SRM). Mainly, a three‐step framework is proposed. Firstly, content‐based video classification is proposed, where twofold use of high‐level concept detectors is leveraged to classify Web videos. Initially, category classifiers induced from VIREO‐374 detectors are trained to classify Web videos, and then concept detectors with high confidence for each video are mapped to CDT through SRM‐assisted semantic content fusion function to further boost the category classifiers, which intuitively provide a more robust measure for Web video classification. Secondly, a context‐based video classification is proposed, where twofold use of contextual information is also harnessed. Initially, cosine similarity and then semantic similarity are measured between text features of each video and CDT through vector space model (VSM)‐ and SRM‐assisted semantic context fusion function, respectively. Finally, classification results from content and context are fused to compensate for the shortcomings of each other, which enhance the video classification performance. Experiments on large‐scale video dataset validate the effectiveness of the proposed solution.