Machine learning (ML)-based network anomaly detection methods are proven to provide automated network protection from traffic misbehavior and authorized system access through data monitoring and analysis. However, conventional centralized methods present risks for data privacy and breaches. By facilitating distributed model training over a number of network nodes, Federated Learning (FL) emerges as a key enabler for effective anomaly detection yet while preserving the privacy of the data. This paper studies FL-based detection approaches under two different Deep Learning models, CNN and MLP. We use XGBoost for feature selection and the two UNSWNB15 and CICDDoS2019 datasets for assessing the effectiveness of each model through the evaluation of standard performance metric criteria, namely the recall, precision, accuracy, and F1score metrics. Our experimental findings indicate that integrating XGBoost-based feature selection with the CNN model yields superior performance on the UNSW-NB15 dataset, whereas the MLP model benefits more from the same integration when applied to the CICDDoS2019 dataset.

Rising phishing attacks pose serious cybersecurity threats due to their use of fraudulent links to collect confidential user information. In this paper, we evaluate the performance of various Machine Learning (ML) models, including Decision Trees, Random Forest, and Extreme Gradient Boosting, to address this growing threat. Additionally, we assess the effectiveness of different feature selection techniques, such as Analysis of Variance, Correlation-based Selection, Mutual Information, and Recursive Feature Elimination with Cross-Validation. Our findings demonstrate that combining Extreme Gradient Boosting with Recursive Feature Elimination and Cross-Validation outperforms previous methods. The proposed solution achieved an accuracy of 97.33%, a recall of 97.1656%, an F1 score of 97.3%, and a precision of 97.42%, highlighting its potential for effectively identifying phishing attacks

This proposal aims to suggest improvements for the article titled “Low-Cost Data Centers in Developing Countries” with the aim of enhancing its content, relevance, and practicality. Our expertise in data infrastructure, we believe these improve-ments will provide valuable insights and guidance to readers interested in establishing efficient data centers in developing countries.

Urban Traffic Networks are characterized by their high dynamics and increased traffic congestion cases, leading to a more complex road traffic management. The present research work suggests an innovative advanced vehicle guidance system based on Hierarchical Interval Type-2 Fuzzy Logic model optimized by the Particle Swarm Optimization (PSO) method. Indeed, this system allows an intelligent and prompt adjustment of the road traffic network in a dynamic way and improves the entire road network quality, particularly in case of congestions or jams, considering real-time traffic information. The best followed road is selected according to the quality of traffic and route length, together with contextual factors pertaining to the driver, the environment, and the path. The proposed system is executed and simulated using SUMO (Simulation of Urban Mobility), for which four large areas situated in the cities of Sfax, Luxembourg, Bologna and Cologne have been tested. The simulation results proved the effectiveness of learning the Hierarchical Interval Type-2 Fuzzy Logic model using PSO real time technique to accomplish multi-objective optimality regarding two criteria: number of cars that attain their destination and average travel time. The obtained results have confirmed the efficiency of the proposed system.

Traffic congestion is a problem in most large cities world wide. It occurs when the capacity of road is surpassed, resulting in augmented vehicular queuing and slower average speeds. The traffic congestion can be caused or increased by various conditions like weather, road work, road traffic incidents. To deal with these problems, we propose a novel cooperative Multi-Agent system (MAS) for Road Traffic Decision Making in Vehicular Ad-Hoc network (VANET) based on a Hierarchical Interval Type-2 Fuzzy Knowledge Representation System (CMRHFS) used for travel route guidance. Our proposal aims to increase the road safety and the quality of the entire road network, especially in case of congestions, accidents and jams, considering traffic information in real-time as well as drivers travel time to attain their destinations. The obtained simulation results have proved our suggested system efficiency compared to Dijkstra's algorithm and Hierarchical Interval Type-2 Fuzzy Logic System (HIT2FLS) regarding two criteria: average travel time and path flow.

Traffic congestion leads to many problems, namely road users' dissatisfaction, air pollution and waste of time and fuel. For this reason, congestion detection at an early stage is required to perform an efficient exploitation of resources. This paper proposed a Hierarchical Type-2 Beta Fuzzy Knowledge Representation system for the selection of optimal route. Consequently, this system aims to avoid longer travel times, and to decrease traffic accidents and the number of traffic congestion situations. The selection is performed through itineraries assessment by contextual factors such as Max speed and density of a given path. For the validation, the traffic simulation was done with the open source microscopic road traffic simulator SUMO. When compared with the Dijkstra's algorithm, the proposed system showed better performance in terms of average travel time and path flow. These promising results prove the potential of our method to relieve traffic congestion.

The road traffic becomes more complex to manage because of the high dynamics of traffic flow and the rise of travel time when the number of vehicles augments in the road networks. Hence, the shortest itinerary based on route length (as provided by GPS navigators) cannot be the best solution nowadays. The application of type-2 Fuzzy Logic is regarded as an effective way for transportation engineering to prevent the problem of ambiguity and uncertainty of road perceptions. In this paper, we propose a hierarchical type-2 Fuzzy Logic System to evaluate itinerary by integrating contextual factors influencing the route choice like speed and road work information.

This paper presents an intelligent hybrid system to support the planning for a mobile robot motion in unknown and dynamic environment. Called Fuzzy-MARCoPlan (Fuzzy-MultiAgent Remote Control motion Planning), this system optimizes the path by the introduction of sub-goals and through a multiagent cooperation based on fuzzy reasoning. In fact, we propose to agentify the surrounding zones of the robot; these zone agents compete for attracting the sub-goal. A planning agent, fortified with a fuzzy rule based system, decides on the best sub-goal to reach. Fuzzy-MARCoPlan is simulated and tested on several navigation environments which are generated randomly under the multiagent platform MadKit. These tests confirm the robustness of the proposed system in terms of path optimality in a dynamic environment. Moreover, the obtained results reinforce the advantage of a multiagent planning hybridized with fuzzy reasoning for mobile robot motion planning.

In view of the high dynamicity of traffic flow and the polynomial increase in the number of vehicles on road networks, the route choice problem becomes more complex. A classical shortest path algorithm based only on road length is no longer relevant. We propose in this paper an adaptive vehicle guidance system instigated from the ants behavior, well known for its good adaptativity; this system allows adjusting intelligently and promptly the route choice according to the real-time changes in the road network situations, such as new congestions and jams. This method is implemented as a deliberative module of a vehicle ant agent in a collaborative multiagent system representing the entire road network. Series of simulations, under a multiagent platform, allow us to discuss the improvement of the global road traffic quality in terms of time, fluidity, and adaptativity.