Mohamed Mosbah

Badreddine Yacine Yacheur, Toufik Ahmed, Mohamed Mosbah

Cooperative intelligent transport systems rely on a set of Vehicle-to-Everything (V2X) applications to enhance road safety. Emerging new V2X applications like Advanced Driver Assistance Systems (ADASs) and Connected Autonomous Driving (CAD) applications depend on a significant amount of shared data and require high reliability, low end-to-end (E2E) latency, and high throughput. However, present V2X communication technologies such as ITS-G5 and C-V2X (Cellular V2X) cannot satisfy these requirements alone. In this paper, we propose an intelligent, scalable hybrid vehicular communication architecture that leverages the performance of multiple Radio Access Technologies (RATs) to meet the needs of these applications. Then, we propose a communication mode selection algorithm based on Deep Reinforcement Learning (DRL) to maximize the network's reliability while limiting resource consumption. Finally, we assess our work using the platooning scenario that requires high reliability. Numerical results reveal that the hybrid vehicular communication architecture has the potential to enhance the packet reception rate (PRR) by up to 30% compared to both the static RAT selection strategy and the multi-criteria decision-making (MCDM) selection algorithm. Additionally, it improves the efficiency of the redundant communication mode by 20% regarding resource consumption

Alexey Zhukov, Jenny Benois-Pineau, Amira Youssef et al.

Multimodal fusion is a multimedia technique that has become popular in the wide range of tasks where image information is accompanied by a signal/audio. The latter may not convey highly semantic information, such as speech or music, but some measures such as audio signal recorded by mics in the goal to detect rail structure elements or defects. While classical detection approaches such as You Only Look Once (YOLO) family detectors can be efficiently deployed for defect detection on the image modality, the single modality approaches remain limited. They yield an overdetection in case of the appearance similar to normal structural elements. The paper proposes a new multimodal fusion architecture built on the basis of domain rules with YOLO and Vision transformer backbones. It integrates YOLOv8n for rapid object detection with a Vision Transformer (ViT) to combine feature maps extracted from multiple layers (7, 16, and 19) and synthesised audio representations for two defect classes: rail Rupture and Surface defect. Fusion is performed between audio and image. Experimental evaluation on a real-world railway dataset demonstrates that our multimodal fusion improves precision and overall accuracy by 0.2 points compared to the vision-only approach. Student's unpaired t-test also confirms statistical significance of differences in the mean accuracy.

Vineeta Jain, Vijay Laxmi, Manoj Singh Gaur et al.

In this paper, we demonstrate a realistic variant of wireless Evil Twins (ETs) for launching device to device (D2D) attacks over the network, particularly for Android. We show an attack where an ET infects an Android device before the relay of network traffic through it, and disappears from the network immediately after inflicting the device. The attack leverages the captive portal facility of wireless networks to launch D2D attack. We configure an ET to launch a malicious component of an already installed app in the device on submission of the portal page. In this paper, we present an online, incremental, automated, fingerprinting based pre-association detection mechanism named as ETGuard which works as a client-server mechanism in real-time. The fingerprints are constructed from the beacon frames transmitted by the wireless APs periodically to inform client devices of their presence and capabilities in a network. Once detected, ETGuard continuously transmits deauthentication frames to prevent clients from connecting to an ET. ETGuard outperforms the existing state-of-the-art techniques from various perspectives. Our technique does not require any expensive hardware, does not modify any protocols, does not rely on any network specific parameters such as Round Trip Time (RTT), number of hops, etc., can be deployed in a real network, is incremental, and operates passively to detect ETs in real-time. To evaluate the efficiency, we deploy ETGuard in 802.11a/b/g wireless networks. The experiments are conducted using 12 different attack scenarios where each scenario differs in the source used for introducing an ET. ETGuard effectively detects ETs introduced either through a hardware, software, or mobile hotspot with high accuracy, only one false positive scenario, and no false negatives.

Shweta Bhandari, Wafa Ben Jaballah, Vineeta Jain et al.

With the digital breakthrough, smart phones have become very essential component. Mobile devices are very attractive attack surface for cyber thieves as they hold personal details (accounts, locations, contacts, photos) and have potential capabilities for eavesdropping (with cameras/microphone, wireless connections). Android, being the most popular, is the target of malicious hackers who are trying to use Android app as a tool to break into and control device. Android malware authors use many anti-analysis techniques to hide from analysis tools. Academic researchers and commercial anti-malware companies are putting great effort to detect such malicious apps. They are making use of the combinations of static, dynamic and behavior based analysis techniques. Despite of all the security mechanisms provided by Android, apps can carry out malicious actions through collusion. In collusion malicious functionality is divided across multiple apps. Each participating app accomplish its part and communicate information to another app through Inter Component Communication (ICC). ICC do not require any special permissions. Also, there is no compulsion to inform user about the communication. Each participating app needs to request a minimal set of privileges, which may make it appear benign to current state-of-the-art techniques that analyze one app at a time. There are many surveys on app analysis techniques in Android; however they focus on single-app analysis. This survey augments this through focusing only on collusion among multiple-apps. In this paper, we present Android vulnerabilities that may be exploited for a possible collusion attack. We cover the existing threat analysis, scenarios, and a detailed comparison of tools for intra and inter-app analysis. To the best of our knowledge this is the first survey on app collusion and state-of-the-art detection tools in Android.