IoT devices typically stream data such as sensor values to other devices including cloud-based services. Analyzing these streams for cyber-attacks is a challenging task. This is due to the infinite nature of stream-based datatypes. Analyzing streams can require additional real-time processing and computational performance capabilities. In this article, we focus on how concept drifts affect Botnet cyber-attack detection in IoT scenarios. To reveal the result, we incorporate the concept drift analysis to detect cyber-attacks on the Bot-IoT dataset, which consists of legitimate and simulated IoT network traffics, together with various types of attacks. We designed subdatasets of the Bot-IoT to ensure the concept drift occurs that eventually complete the experiments. The detection accuracies improved 15%–26% compared with the classification models without concept drift analysis. We also gain superior performance results by comparing confusion matrices when concept drift analysis is ongoing. We propose a technique featuring a dynamic sliding window based on the residual projection to perform concept drift analysis. During the process of finding concepts in data streams, the sample number is updated dynamically by comparing the anomalous quantity obtained by the residual projection method in the current window to the previous one. In addition to the Bot-IoT dataset, our method is also applied to two popular synthetic datasets SEA Concept and UG-2C-5D. The results demonstrate the effectiveness of our method with respect to the false alarm rate, misses, and average delay.

Online customers’ opinions represent a significant resource for both customers and enterprises to extract much information that helps them make the right decision. Finding relevant data while searching the internet is a big challenge for web users, known as the “Problem of Information Overload”. Recommender systems have been recognized as a promising way of solving such problems. In this paper, a product recommendation system called “SmartTips” is introduced. The proposed model is built based on aspect-based sentiment analysis, which exploits customers’ feedback and applies the aspect term extraction model to rate various products and extract user preferences as well. Several factors were considered, including readers’ votes, aspect term frequency, opinion words’ frequencies, etc. We tested our model on benchmark datasets that are widely used, and the results show that it outperforms the baseline methods regarding the mean squared errors of generated predictions.

Clustering has been a subject of wide research since it arises in many application domains. One of the clustering process issues is the evaluation of clustering results. Estimation of the obtained cluster structure quality is the main subject of cluster validity. In several years many cluster validity indexes were presented in the research community, but the general approach for clustering evaluation was not developed. In our work we are going to produce some methodology for cluster validity estimation and construct a special framework for its measure, which will combine a couple of current methods in one suitable tool. We suggest that these investigations will help a wide range of analyst in theirs work with clustering.