Mostafa Lotfi | Searcharxiv

arXiv2026

Topology-Age-Aware Cooperative Awareness in Vehicular Ad-Hoc Networks

In vehicular ad-hoc networks (VANETs), maintaining the freshness of status information among vehicles is critical for enabling timely and reliable safety-related decisions. In this paper, we consider the problem of scheduling cooperative status broadcasting in VANETs, where vehicles are allowed to broadcast not only their own updates but also the updates of other vehicles. We propose the age-aware cooperative broadcasting (A-CB) policy, in which a road-side unit (RSU) schedules target-sender pairs based on the total achievable age-gain over the sender's network links. Moreover, through exploiting the network topology, we introduce two topology-age-aware cooperative broadcasting (TA-CB) policies; the Local TA-CB that weights links according to the number of second-hop neighbors, and the Global TA-CB that leverages the betweenness centrality of vehicles to prioritize broadcasting decisions. The proposed policies are evaluated under both random and clustered network topologies. Numerical results demonstrate that cooperative policies significantly outperform non-cooperative broadcasting. Notably, in clustered network structures, the Local and the Global TA-CB policies achieve substantial improvements over A-CB, with each outperforming the other under different network conditions.

cs.NI↗

arXiv2025

A Comprehensive Study of Supervised Machine Learning Models for Zero-Day Attack Detection: Analyzing Performance on Imbalanced Data

Among the various types of cyberattacks, identifying zero-day attacks is problematic because they are unknown to security systems as their pattern and characteristics do not match known blacklisted attacks. There are many Machine Learning (ML) models designed to analyze and detect network attacks, especially using supervised models. However, these models are designed to classify samples (normal and attacks) based on the patterns they learn during the training phase, so they perform inefficiently on unseen attacks. This research addresses this issue by evaluating five different supervised models to assess their performance and execution time in predicting zero-day attacks and find out which model performs accurately and quickly. The goal is to improve the performance of these supervised models by not only proposing a framework that applies grid search, dimensionality reduction and oversampling methods to overcome the imbalance problem, but also comparing the effectiveness of oversampling on ml model metrics, in particular the accuracy. To emulate attack detection in real life, this research applies a highly imbalanced data set and only exposes the classifiers to zero-day attacks during the testing phase, so the models are not trained to flag the zero-day attacks. Our results show that Random Forest (RF) performs best under both oversampling and non-oversampling conditions, this increased effectiveness comes at the cost of longer processing times. Therefore, we selected XG Boost (XGB) as the top model due to its fast and highly accurate performance in detecting zero-day attacks.

cs.CR↗