Intrusion detection for Internet of Things applying metagenome fast analysis

Today, intrusion detection and prevention systems (IDS / IPS) are a necessary element of protection against network attacks. The main goal of such systems is to identify an unauthorized access to the network and take appropriate countermeasures: alarming security officers about intrusion, reconfiguration of firewall to block further acts of the attacker, protection against cyberattacks and malware. For traditional computer networks there are a large number of sufficiently effective approaches for protection against malicious activity, however, for the rapidly developing dynamic adhoc networks (Internet of Things - IoT, MANET, WSN, etc.) the task of creating a universal protection means is quite acute. In this paper, we review various methods for detecting polymorphic intrusion activity (polymorphic viral code and sequences of operations), present a comparative analysis, and implement the suggested technology for detecting polymorphic chains of operations using bioinformatics for IoT. The proposed approach has been tested with different lengths of operation sequences and different k-measures, as a result of which the optimal parameters of the proposed method have been determined.