The Internet is becoming one of the most significant threats to personal and state information security. Therefore, the identification and counteraction of inappropriate information in global network content become the problem of national importance. The paper presents a new approach to building an intelligent system for identification and counteraction of malicious and inappropriate information on the Internet using artificial intelligence methods, in particular, machine learning and big data processing. The system architecture includes a set of intelligent components for data collection, information objects classification, ensuring the timeliness of analysis, eliminating incompleteness and inconsistency of analysis results, selecting the countermeasures for counteraction, and visualization. The paper presents an experimental evaluation of methods implemented for information object classification in single-threaded and multithreaded modes using various classifiers included in the Scikit-learn and Spark MLlib libraries.
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