No abstract

The IoT’s quick development has brought up several security problems and issues that cannot be solved using traditional intelligent systems. Deep learning (DL) in the field of artificial intelligence (AI) has proven to be efficient, with many advantages that can be used to address IoT cybersecurity concerns. This study trained two models of intelligent networks—namely, DenseNet and Inception Time—to detect cyber-attacks based on a multi-class classification method. We began our investigation by measuring the performance of these two networks using three datasets: the ToN-IoT dataset, which consists of heterogeneous data; the Edge-IIoT dataset; and the UNSW2015 dataset. Then, the results were compared by identifying several cyber-attacks. Extensive experiments were conducted on standard ToN-IoT datasets using the DenseNet multicategory classification model. The best result we obtained was an accuracy of 99.9% for Windows 10 with DenseNet, but by using the Inception Time approach we obtained the highest result for Windows 10 with the network, with 100% accuracy. As for using the Edge-IIoT dataset with the Inception Time approach, the best result was an accuracy of 94.94%. The attacks were also assessed in the UNSW-NB15 database using the Inception Time approach, which had an accuracy rate of 98.4%. Using window sequences for the sliding window approach and a six-window size to start training the Inception Time model yielded a slight improvement, with an accuracy rate of 98.6% in the multicategory classification.

No abstract

No abstract

Intelligent control of multi-agent autonomous humanoid robots is a very complex problem, especially in the RoboCup domain. This is due to the dynamics of the environment and the complexity of behaviors that should be executed in real time. Moreover, the overall complexity increases since another mechanism for coordinating the robot’s behaviors should be involved. Throughout the past years, acceptable results have been obtained using different approaches to solve the behavior control problem (decision trees, fuzzy techniques, support vector machines, reinforcement learning, etc.). As case-based reasoning (CBR) is tightly related to the way humans reason, researches are now concentrated on finding the most relevant solutions that could handle behavior control problems of humanoid soccer robots using CBR techniques. This paper proposes an extended algorithm of a more complex architecture to control more complex soccer behaviors such as dribbling and goal scoring applied to multi-robot scenarios between attacker and goalie. The decomposition of features into a hierarchy of levels has led to reduced complexity of the overall behavior execution in real time. However, the average retrieval accuracy and the average performance accuracy were relatively low (70.3% and 77%, respectively). We intend to combine neural networks with CBR to learn adaptation rules for reducing the complexity of hand-coded rules and improving the overall controller performance.