Intelligent Misbehavior Detection System for Detecting False Position Attacks in Vehicular Networks

in Hawlader, Faisal; Boualouache, Abdelwahab; Faye, Sébastien (Eds.) et al The 2021 IEEE International Conference on Communications (the 4th Workshop on 5G and Beyond Wireless Security) (2021, June)

Position falsification attacks are one of the most dangerous internal attacks in vehicular networks. Several Machine Learning-based Misbehavior Detection Systems (ML-based MDSs) have recently been proposed ... [more ▼]

Position falsification attacks are one of the most dangerous internal attacks in vehicular networks. Several Machine Learning-based Misbehavior Detection Systems (ML-based MDSs) have recently been proposed to detect these attacks and mitigate their impact. However, existing ML-based MDSs require numerous features, which increases the computational time needed to detect attacks. In this context, this paper introduces a novel ML-based MDS for the early detection of position falsification attacks. Based only on received positions, our system provides real-time and accurate predictions. Our system is intensively trained and tested using a publicly available data set, while its validation is done by simulation. Six conventional classification algorithms are applied to estimate and construct the best model based on supervised learning. The results show that the proposed system can detect position falsification attacks with almost 100% accuracy. [less ▲]

Poster: Characterizing Driving Behaviors Through a Car Simulation Platform

Poster (2017, November 27)

Human mobility has opened up to many themes in recent years. Human behavior and how a driver might react to certain situations, whether dangerous (e.g. an accident) or simply part of the evolution of new ... [more ▼]

Human mobility has opened up to many themes in recent years. Human behavior and how a driver might react to certain situations, whether dangerous (e.g. an accident) or simply part of the evolution of new technologies (e.g. autonomous driving), leaves many avenues to be explored. Although experiments have been deployed in real situations, it remains difficult to encounter the conditions that certain studies may require. For this reason, we have set up a driving simulator (comprising several modules) that is able to reproduce a realistic driving environment. Although, as the literature has already demonstrated, the conditions are often far from reality, simulation platforms are nonetheless capable of reproducing an incredibly large number of scenarios on the fly. In this poster, we explain how we conceived the simulator, as well as the system we developed for collecting metrics on both the driver and the simulation environment. In addition, we take advantage of this conference to publicly share a dataset consisting of 25 drivers performing the same road circuit on the "Project Cars" game. [less ▲]

Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals

in ISPRS International Journal of Geo-Information (2017), 6(3), 62

The potential of geospatial big data has been drawing attention for a few years. Despite the larger and larger market penetration of portable technologies (nomadic and wearable devices like smartphones ... [more ▼]

The potential of geospatial big data has been drawing attention for a few years. Despite the larger and larger market penetration of portable technologies (nomadic and wearable devices like smartphones and smartwatches), their opportunities for travel behavior analysis are still relatively unexplored. The main objective of our study is to extract the human mobility patterns from GPS traces in order to derive an indicator for enhancing Collaborative Mobility (CM) between individuals. The first step, extracting activity duration and location, is done using state-of-the-art automated recognition tools. Sensors data are used to reconstruct individual’s activity location and duration across time. For constructing the indicator, in a second step, we defined different variables and methods for specific case studies. Smartphone sensor data are being collected from a limited number of individuals and for one week. These data are used to evaluate the proposed indicator. Based on the value of the indicator, we analyzed the potential for identifying CM among groups of users, such as sharing traveling resources (e.g., carpooling, ridesharing, parking sharing) and time (rescheduling and reordering activities). [less ▲]

An Open Dataset for Human Activity Analysis using Smart Devices

Report (2017)

The study of human mobility and activities has opened up to an incredible number of studies in the past, most of which included the use of sensors distributed on the body of the subject. More recently ... [more ▼]

The study of human mobility and activities has opened up to an incredible number of studies in the past, most of which included the use of sensors distributed on the body of the subject. More recently, the use of smart devices has been particularly relevant because they are already everywhere and they come with accurate miniaturized sensors. Whether it is smartphones, smartwatches or smartglasses, each device can be used to describe complementary information such as emotions, precise movements, or environmental conditions. In this short paper, we release the applications we have developed and an example of a collected dataset. We propose that opening multi-sensors data from daily activities may enable new approaches to studying human behavior. [less ▲]

Demo Abstract: Human Mobility Profiling Using Privacy-Friendly Wi-Fi and Activity Traces

in Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems (SenSys 2016) (2016, November 14)

Human mobility is one of the key topics to be considered in the networks of the future, both by industrial and research communities that are already focused on multidisciplinary applications and user ... [more ▼]

Human mobility is one of the key topics to be considered in the networks of the future, both by industrial and research communities that are already focused on multidisciplinary applications and user-centric systems. If the rapid proliferation of networks and high-tech miniature sensors makes this reality possible, the ever-growing complexity of the metrics and parameters governing such systems raises serious issues in terms of privacy, security and computing capability. In this demonstration, we show a new system, able to estimate a user's mobility profile based on anonymized and lightweight smartphone data. In particular, this system is composed of (1) a web analytics platform, able to analyze multimodal sensing traces and improve our understanding of complex mobility patterns, and (2) a smartphone application, able to show a user's profile generated locally in the form of a spider graph. In particular, this application uses anonymized and privacy-friendly data and methods, obtained thanks to the combination of Wi-Fi traces, activity detection and graph theory, made available independent of any personal information. [less ▲]

A Two-Level Approach to Characterizing Human Activities from Wearable Sensor Data

in Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (2016), 7(3),

The rapid emergence of new technologies in recent decades has opened up a world of opportunities for a better understanding of human mobility and behavior. It is now possible to recognize human movements ... [more ▼]

The rapid emergence of new technologies in recent decades has opened up a world of opportunities for a better understanding of human mobility and behavior. It is now possible to recognize human movements, physical activity and the environments in which they take place. And this can be done with high precision, thanks to miniature sensors integrated into our everyday devices. In this paper, we explore different methodologies for recognizing and characterizing physical activities performed by people wearing new smart devices. Whether it’s smartglasses, smartwatches or smartphones, we show that each of these specialized wearables has a role to play in interpreting and monitoring moments in a user's life. In particular, we propose an approach that splits the concept of physical activity into two sub-categories that we call micro- and macro-activities. Micro- and macro-activities are supposed to have functional relationship with each other and should therefore help to better understand activities on a larger scale. Then, for each of these levels, we show different methods of collecting, interpreting and evaluating data from different sensor sources. Based on a sensing system we have developed using smart devices, we build two data sets before analyzing how to recognize such activities. Finally, we show different interactions and combinations between these scales and demonstrate that they have the potential to lead to new classes of applications, involving authentication or user profiling. [less ▲]

Characterizing the Topology of an Urban Wireless Sensor Network for Road Traffic Management

in IEEE Transactions on Vehicular Technology (2016), 65(7), 5720-5725

In a near future, wireless networks will be one of the key technologies for road traffic management in smart cities. Vehicles and dedicated roadside units should be interconnected through wireless ... [more ▼]

In a near future, wireless networks will be one of the key technologies for road traffic management in smart cities. Vehicles and dedicated roadside units should be interconnected through wireless technologies such as IEEE 802.11p (WAVE). Traffic light and road signs may also take their place in this architecture, forming a large-scale network of small devices that report measurements, take orders from a control center, and are able to take decisions autonomously based on their local perception. Such a network shares many similarities with classical wireless sensor and actuator networks, starting with its distributed organization and with the role of the control center. However, its topology, and subsequently the appropriate selection of protocols and algorithms, will be strongly influenced by each city's characteristics. In this article, we characterize and discuss probable topologies of these networks. The aim of this work is to provide network models that can be used to evaluate protocols and algorithms using realistic scenarios in place of generic random graphs. We deploy such networks over 52 city maps extracted from OpenStreetMap and characterize the resulting graphs, with a particular focus on connectivity aspects (degree distribution and connected components). The tools, the complete datasets, OMNeT++ network models are available freely online. [less ▲]

Heat is in the Eye of the Beholder: Towards Better Authenticating on Smartglasses

in Proceedings of the 9th International Conference on Human System Interaction (HSI'16) (2016, July)

Smart and wearable devices are trendy electronic objects that have become increasingly popular in recent years. Those devices are, by definition, tightly connected with the user's personal activities ... [more ▼]

Smart and wearable devices are trendy electronic objects that have become increasingly popular in recent years. Those devices are, by definition, tightly connected with the user's personal activities. Authentication is therefore a critical feature for both identifying users and personalizing the services on the device. In particular, the emergence of smartglasses changed the way we thought a wearable could assist users in their daily activities. As designed by commercial providers, smartglasses are sold with a very specific set of interactions capabilities. These capabilities have a strong impact on how comfortably or safely users may authenticate to their smartglasses. For this reason, we investigate in this paper the different authentication methods available for smartphones and we comparatively position the smartglasses in the design space of authentication methods. We propose a new approach based on touch input on an arbitrary surface using thermal camera input. This approach aims to circumvent the lack of touch surface provided by smartglasses, while maintaining an acceptable level of security. [less ▲]

Toward a Characterization of Human Activities using Smart Devices: A Micro/Macro Approach

in Proceedings of the 7th IEEE INFOCOM International Workshop on Mobility Management in the Networks of the Future World (2016, April)

The emergence of new connected devices has opened up new opportunities and allowed to imagine concepts that bring computer sciences and social sciences closer together. In particular, today's increasingly ... [more ▼]

The emergence of new connected devices has opened up new opportunities and allowed to imagine concepts that bring computer sciences and social sciences closer together. In particular, today's increasingly sophisticated miniature sensors allow to track and understand human activities and behavior with a great precision. Taking different approaches and perspectives, we use in this paper smartwatches and smartglasses to explore these behaviors and show that these objects, considered by many as gadgets, have an important role to play in understanding the lives of individuals. The main objective of this work is to introduce two new scales of activity detection, which lacks a formal and consistent definition in the literature. First, we propose a model that precisely detects and interprets movements made by a person wearing smart devices. Then, we use this model to show different interactions between those micro-activities and bigger chunks of behaviors we call macro-activities. Using a new concept based on 3D visualization, we finally show that combining those two scales and using a limited dataset, it is possible to distinguish between different individuals when they are performing very similar activities. The findings of this study lead the way to enhanced user profiling. [less ▲]

Connectivity Analysis of Wireless Sensor Networks Deployments in Smart Cities

in Proceedings of the 22nd IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT 2015) (2015, November 24)

Recent advances in the field of intelligent transportation systems have focused on the use of wireless networks to link vehicles and road infrastructure. Applications that might result from such networks ... [more ▼]

Recent advances in the field of intelligent transportation systems have focused on the use of wireless networks to link vehicles and road infrastructure. Applications that might result from such networks range from the adaptive management of traffic lights to the detection of traffic jams and accidents. Whatever the case may be, it seems important to explore the possibilities and limitations of such networks, which the literature often portrays in a somewhat idealistic way (e.g. no packet loss, fully connected sensors, etc.). In this paper, we study the deployment of wireless sensor networks at intersections in some of the world's major cities and characterize their topologies. Using a propagation model that corresponds to a 2.4GHz IEEE 802.15.4 network interface, we focus our study on the global connectivity of graphs resulting from different networks. By deploying this type of network over 52 city and region maps extracted from OpenStreetMap, we show that cities can reasonably be classified into three network structure categories of low connectivity (i.e. a high number of connected components) and that it should be feasible to improve the networks by adding sensors. All the tools and the complete dataset are freely available online. [less ▲]