Building an Intrusion Detection System in Critically Important Information Networks with Application of Data Mining Methods

Nowadays there is a wide variety of methods and means of protecting critical information networks against cyberattacks. Massive cyberattacks lead to creation of special technical solutions, means and systems to counter them. To detect network intrusions, modern methods, models, tools, software and complex technical solutions for intrusion detection and prevention systems are used, that can remain effective even when new or modified types of cyber threats appear. However, in practice, in case of new threats and anomalies that are generated by attacks with unidentified or vaguely defined properties, these tools do not always remain effective and require great time investments for their appropriate adaptation. Therefore, intrusion detection systems (IDS) must be continuously researched and improved to ensure continuity in their effective operation. Unfortunately, at present, there is no universal method of countering cyberattacks, and therefore there is a need for an integrated approach to solving this problem. The use of DATA MINING methods and artificial intelligence makes it possible to increase the effectiveness of countering intrusions and protect objects from potential intruders.