No abstract

Hunting moving targets with random motions and behavior is a challenge for robotic systems, and it occupies a significant position in the research on coordination and cooperation in multi-robot systems. Cooperative hunting between robots is used in a wide range of fields such as industry, military, rescue and other fields. The aim of this paper is to present a new strategy for target hunting by means of a cooperative multi-robot system in two-dimensional space, especially moving targets with random and unexpected behavior. The strategy is inspired from the behavior of wolves in hunting and Wolf Swarm Algorithm, as it summarizes the roles of robots in the system in three roles: the leader wolf, the antagonist wolf, and the follower wolf. This diversity of roles contributed to improve the convergence performance of the algorithm and reduce significantly the pursuit time. The validity of this strategy is supported by computer simulations.

No abstract

IoT network is a promising technology, IoT implementation is growing rapidly but cybersecurity is still a loophole, detection of attacks in IOT infrastructures is a growing concern in the field of IoT. With the increased use of Internet of Things in different areas, cyber-attacks are also increasing proportionately and can cause failures in the system. IDS becomes the leading security solution. Anomaly based network intrusion detection (IDS) detection plays a major role in protecting networks against various malicious activities. Improving the security of loT networks has become one of the most critical issues. This is due to the large-scale development and deployment of loT devices and the insufficiency of Intrusion Detection Systems (IDS) to be deployed for the use of special purpose networks. In this article, the performance of several machine learning models has been compared to accurately predict attacks on IoT systems, the case of imbalanced classes was subsequently treated using the SMOTE technique. The Nystrom based kernel SVM is the first time used to detect attacks in the IoT network and the results are promising. The evaluation metrics used in the performance comparison are accuracy, precision, recall, f1 score, and auc-roc curve.

No abstract