A Hybrid Classification Approach for Intrusion Detection in IoT Network

809-816With the increase in number of IoT devices, the capabilities to provide reliable security and detect the malicious activities within the IoT network have become quite challenging. We propose a hybrid classification approach to detect multi-class attacks in the IoT network. In the proposed model, Principle Component Analysis (PCA) is used to extract the useful features and Linear Discriminant Analysis (LDA) is used to reduce the high dimension data set into lower dimension space by keeping less number of important features. This was assisted by use of a combination of neural network and Support Vector Machine (SVM) classifiers to improve the detection rate and decrease the false alarm rate. The neural network, a multi-class classifier, is used to classify the intruders in the network with more accuracy. The SVM is an efficient and fast learner classifier which is used to classify the unmatched behavior. The proposed method needs less computation complexity for intrusion detection. The performance of the proposed model was evaluated on two benchmark datasets for intrusion detection, i.e., NSL-KDD and UNSW-NB15. Results show that our model outperforms existing models

Designing Vision Based Autonomous Docile-X Mobile Robot for Real-time Application to Soccer Behaviors

This paper aims at developing a real-time, robust, and reliable behaviors for an omnidirectional soccer robot, can be used in crowded dynamically-changing environments. The soccer robot system consists of highly coordinated operations and movements so as to fulfill specific objectives, even under unfavorable situations. The associated issues are position control, velocity control and sensing information in addition to the need for imitating the human-like decision. The proposed method considers not only the kinematics of the robot but also its dynamics. Moreover, a control structure is designed and several behaviors for a soccer robot are proposed. Image processing, recognition and target following algorithm are illustrated through experiments