Towards Reliable Multi-Path Routing : An Integrated Cooperation Model for Drones

Ad-hoc networks have evolved into a vital wireless communication component by offering an adaptable infrastructure suitable for various scenarios in our increasingly interconnected and mobile world. However, this adaptability also exposes these networks to security challenges, given their dynamic nature, where nodes frequently join and leave. This dynamism is advantageous but presents resource constraints and vulnerability to malicious nodes, impacting data transmission reliability and security. In this context, this article explores the development of a secure routing protocol for Ad-hoc networks based on a cooperation reinforcement model to reduce the degradation of routing performance. We leverage the reputation of nodes as an additional security layer to monitor their behavior and evaluate their level of reliability. To exemplify our solution, we focus on drone fleets (UAVs) as a pertinent case study. Drones frequently operate in dynamic, challenging environments, relying on Ad-hoc networks for communication. They serve as an apt illustration, highlighting the complexities of the issue and the efficacy of our proposed remedy. The simulation results show the effectiveness of our proposed solution compared to stae-of-the-artsolutions

LongCGDroid: Android malware detection through longitudinal study for machine learning and deep learning

This study aims to compare the longitudinal performance between machine learning and deep learning classifiers for Android malware detection, employing different levels of feature abstraction. Using a dataset of 200k Android apps labeled by date within a 10-year range (2013-2022), we propose the LongCGDroid, an image-based effective approach for Android malware detection. We use the semantic Call Graph API representation that is derived from the Control Flow Graph and Data Flow Graph to extract abstracted API calls. Thus, we evaluate the longitudinal performance of LongCGDroid against API changes. Different models are used, machine learning models (LR, RF, KNN, SVM) and deep learning models (CNN, RNN). Empirical experiments demonstrate a progressive decline in performance for all classifiers when evaluated on samples from later periods. Whereas, the deep learning CNN model under the class abstraction maintains a certain stability over time. In comparison with eight state-of-the-art approaches, LongCGDroid achieves higher accuracy. [JJCIT 2023; 9(4.000): 328-346