Biblio

In the last few years, cluster ensembles have emerged as powerful techniques that integrate multiple clustering methods into recommender systems. Such integration leads to improving the performance, quality and the accuracy of the generated recommendations. This paper proposes a novel recommender system based on a cluster ensemble technique for big data. The proposed system incorporates the collaborative filtering recommendation technique and the cluster ensemble to improve the system performance. Besides, it integrates the Expectation-Maximization method and the HyperGraph Partitioning Algorithm to generate new recommendations and enhance the overall accuracy. We use two real-world datasets to evaluate our system: TED Talks and MovieLens. The experimental results show that the proposed system outperforms the traditional methods that utilize single clustering techniques in terms of recommendation quality and predictive accuracy. Most importantly, the results indicate that the proposed system provides the highest precision, recall, accuracy, F1, and the lowest Root Mean Square Error regardless of the used similarity strategy.

Traditional network routing protocol exhibits high statics and singleness, which provide significant advantages for the attacker. There are two kinds of attacks on the network: active attacks and passive attacks. Existing solutions for those attacks are based on replication or detection, which can deal with active attacks; but are helpless to passive attacks. In this paper, we adopt the theory of network coding to fragment the data in the Software-Defined Networks and propose a network coding-based resilient multipath routing scheme. First, we present a new metric named expected eavesdropping ratio to measure the resilience in the presence of passive attacks. Then, we formulate the network coding-based resilient multipath routing problem as an integer-programming optimization problem by using expected eavesdropping ratio. Since the problem is NP-hard, we design a Simulated Annealing-based algorithm to efficiently solve the problem. The simulation results demonstrate that the proposed algorithms improve the defense performance against passive attacks by about 20% when compared with baseline algorithms.