In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to attain an accurate and robust clustering of information, a hybrid algorithm was used to introduce a recommender engine with collaborative filtering using Apache Spark and machine learning (ML) libraries. In this study, we implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to predict the best-rated movies. Our proposed system uses the last search data of a user regarding movie category and references this to instruct the recommender engine, thereby making a list of predictions for top ratings. The proposed study used a model-based approach of matrix factorization, the ALS algorithm along with a collaborative filtering technique, which solved the cold start, sparse, and scalability problems. In particular, we performed experimental analysis and successfully obtained minimum root mean squared errors (oRMSEs) of 0.8959 to 0.97613, approximately. Moreover, our proposed movie recommendation system showed an accuracy of 97% and predicted the top 1000 ratings for movies.

Name Data Network (NDN) is a futuristic Internet architecture that delivers content based on strategically chosen names and caches content on intermediate routers in the content-delivery path, which is used to fulfill future requests for the same data. In-network caching significantly improves network throughput but also makes NDN vulnerable to revamp security attacks such as cache poisoning, cache pollution, and cache snooping. In this paper, we focused on content pollution attack, in which the attacker disrupts the cache locality by intelligently crafted fake requests. This paper introduce monitor metrics for quantitative anomaly detection of requests and proposed a scheme based on Gini Impurity to detect network under cache pollution attack. The paper also proposed an Interest throttling mechanism based on trust to reinforce network under cache pollution attacks. The proposed scheme is evaluated through various experiments on NdnSIM simulator.