Social networks have captured the attention of many people worldwide. However, these services have also attracted a considerable number of malicious users whose aim is to compromise the digital assets of other users by using messages as an attack vector to execute different types of cyberattacks against them. This work presents an approach based on natural language processing tools and a convolutional neural network architecture to detect and classify four types of cyberattacks in social network messages, including malware, phishing, spam, and even one whose aim is to deceive a user into spreading malicious messages to other users, which, in this work, is identified as a bot attack. One notable feature of this work is that it analyzes textual content without depending on any characteristics from a specific social network, making its analysis independent of particular data sources. Finally, this work was tested on real data, demonstrating its results in two stages. The first stage detected the existence of any of the four types of cyberattacks within the message, achieving an accuracy value of 0.91. After detecting a message as a cyberattack, the next stage was to classify it as one of the four types of cyberattack, achieving an accuracy value of 0.82.

Vehicular networks is a key technology for efficiently communicating both user’s devices and cars for timely information regarding safe driving conditions and entertaining applications like social media, video streaming, and gaming services, among others. In view of this, mobile communications making use of cellular resources may not be an efficient and cost-effective alternative. In this context, the implementation of light-fidelity (LiFi) in vehicular communications could be a low-cost, high-data-rate, and efficient-bandwidth usage solution. In this work, we propose a mathematical analysis to study the average throughput in a road intersection equipped with a traffic light that operates as a server, which is assumed to have LiFi communication links with the front lights of the vehicles waiting for the green light. We further assume that the front vehicle (the car next to the traffic light) is able to communicate to the car immediately behind it by using its own tail lights and the front lights of such vehicle, and so on and so forth. The behavior of the road junction is modeled by a Markov chain, applying the Queueing theory with an M/M/1 system in order to obtain the average queue length. Then, Little’s theorem is applied to calculate the average waiting delay when the red light is present in the traffic light. Finally, the mathematical expression of the data throughput is derived.

Peer-to-Peer (P2P) networks have emerged as potential solutions to issues that cause inefficient download times in networks because they can use the resources in the entire network, allowing nodes to act both as servers and clients simultaneously. Commonly, P2P networks use radio frequency (RF) to communicate among nodes; however, light fidelity (LiFi) has been developed as an alternative to wireless fidelity (WiFi) systems due to some advantages such as great speed (up to 1 Tbps), a high level of security, and less saturated channels in unlicensed bandwidth, making it ideal for RF-sensitive environments and networks with static nodes, since LiFi systems require a high node alignment level to enable efficient communication. In this article, we develop a mathematical model that captures the dynamics of LiFi- and WiFi-based P2P networks in static environments to allow for adequate LiFi links in managed conditions where the content is distributed from a single source node to the rest of the peers through different architectures. For the services considered in our work, we compare the performance of P2P networks using WiFi 7 and LiFi 2.0 technologies to provide clear quantitative numerical results that allow for adequate selection between these systems. Also, we validate our mathematical results through extensive simulations.

Social networks have captured the attention of many people worldwide. However, these services have also attracted a considerable number of malicious users whose purpose is to compromise digital assets of other members by using messages as an attack vector to execute different variants of cyberattacks against them. Therefore, this work presents an approach based on Natural Language Processing tools and a Convolutional Neural Network architecture to detect and classify, on social network messages, four types of cyberattacks, such as malware, phishing, spam, and even one whose purpose is deceiving the user into spreading malicious messages to other users, which in this work is identified as bot attacks. One notable feature of this work is that it analyzes textual content without depending on any characteristics from a specific social network, making its analysis independent from particular data sources. Finally, this work was tested on real data, demonstrating its results in two stages. The first detects the existence of any of the four cyberattacks within the message, obtaining an accuracy value of 0.91. After detecting a message as a cyberattack, the next stage is to classify it into one of the four types of cyberattack, achieving an accuracy value of 0.82.

Performance Analysis in Peer-to-Peer Networks with Collaborative Initial States