Designing Deep Neural Network with Chicken Swarm Optimization for Violence Video Classification Using VSD2014 Dataset

IoT (Internet of Things) usage in industrial and scientific domains is progressively increasing. Currently, IoTs are utilized in numerous applications in different domains, similar to communication technology, environmental monitoring, agriculture, medical services, and manufacturing purposes. But, the IoT systems are vulnerable against various intrusions and attacks in the perspective on the security view. It is essential to create an intrusion detection model to detect and secure the network from different attacks and anomalies that continually happen in the network. In this paper, the anomaly detection model for an IoT network using deep neural networks (DNN) with chicken swarm optimization (CSO) algorithm was proposed. Presently, the DNN has demonstrated its efficiency in different fields that are applicable to its usage. Deep learning is the type of algorithm based on machine learning which used many layers to gradually extricate more significant features of level from the raw inputs. The UNSW-NB15 dataset was utilized to evaluate the anomaly detection model. The proposed model obtained 94.85% accuracy and 96.53% detection rate which is better than other compared techniques like GA-NB, GSO, and PSO for validation. The DNN-CSO model has performed well in detecting most of the attacks, and it is appropriate for detecting anomalies in the IoT network.

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Designing Deep Neural Network with Chicken Swarm Optimization for Violence Video Classification Using VSD2014 Dataset

Cited by 2 publications

References 8 publications

A novel bitwise arithmetic optimization algorithm for the rule base optimization of deep neuro-fuzzy system

A novel bitwise arithmetic optimization algorithm for the rule base optimization of deep neuro-fuzzy system

Artificial Intelligence-Based Security Protocols to Resist Attacks in Internet of Things

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