Anomaly detection in software-defined networking utilizing multi-verse deer hunting optimization enabled deep q-network for traffic flow rate prediction

For the consolidated management and supervising of massive networks, software-defined networking (SDN) is seen to be the best option. Nonetheless, it should be highlighted that SDN design experiences the same security problems as conventional networks. To bridge this gap, an efficient model for anomaly detection (AD) in SDN named Multi-verse Deer Hunting Optimization (MVDHO) is introduced. Firstly, SDN nodes are simulated. After that, SDN switches are controlled by the control plane to identify the condition of switches like ON, IDLE, or OFF conditions based on the detection plane. Secondly, the detection plane module consists of two modules, such traffic flow detection and AD. In the detection plane, the SDN switch flow rate is recorded in the form of time-series data and the condition of the switch is predicted based on time-series data using Deep Long short-term memory (LSTM). Similarly, in AD, the behaviour of the communication is recorded as a log file by extracting the significant features. Moreover, appropriate features are selected by mutual information. Finally, the detection of anomaly is performed employing Deep Q-Network, which is trained using MVDHO. Here, MVDHO is obtained by the combination of a Multi-verse Optimizer (MVO) and Deer Hunting Optimization Algorithm (DHOA). The detected anomalies are Denial of Service (DoS), Buffer_overflow, Guess_password, SQL attack and Named attack. The metrics utilized in this research namely, Traffic flow detection accuracy (TFDA), accuracy, true positive rate (TPR), and true negative rate (TNR) attained maximum values with 91.6%, 94.7%, 90.8%, and 86.5%, and also, the minimum value of computational time is 52.99s.