https://www.lk.cs.ucla.edu/data/files/Gerla/Vehicular%20networks%20by%20gerla%20and%20kleinrock%202011.pdf

Vehicular networks and the future of the mobile internet

With the fast development in ad hoc wireless communications and vehicular technology, it is foreseeable that, in the near future, traffic information will be collected and disseminated in real-time by mobile sensors instead of fixed sensors used in the current infrastructure-based traffic information systems. A distributed network of vehicles such as a vehicular ad hoc network (VANET) can easily turn into an infrastructure-less self-organizing traffic information system, where any vehicle can participate in collecting and reporting useful traffic information such as section travel time, flow rate, and density. Disseminating traffic information relies on broadcasting protocols. Recently, there have been a significant number of broadcasting protocols for VANETs reported in the literature. In this paper, we classify and provide an in-depth review of these protocols.

A Review of Information Dissemination Protocols for Vehicular Ad Hoc Networks

http://bbcr.uwaterloo.ca/~wzhuang/papers/MSSP_vanet_survey_2010.pdf

Infotainment and road safety service support in vehicular networking: From a communication perspective

A method for identifying a botnet in a network, including analyzing historical network data using a pre-determined heuristic to determine values of a feature in the historical network data, obtaining a ground truth data set having labels assigned to data units in the historical network data identifying known malicious nodes in the network, analyzing the historical network data and the ground truth data set using a machine learning algorithm to generate a model representing the labels as a function of the values of the feature, analyzing real-time network data using the pre-determined heuristic to determine a value of the feature for a data unit in the real-time network data, assigning a label to the data unit by applying the model to the value of the feature, and categorizing the data unit as associated with the botnet based on the label.

Machine learning based botnet detection using real-time extracted traffic features

Keywords: DLR Reference EPFL-CONF-155461 Record created on 2010-11-23, modified on 2016-08-08

/pdf/simulation-of-urban-mobility-3yunecxhhi.pdf

Simulation of Urban Mobility

A widely used defense practice against malicious traffic on the Internet is through blacklists: lists of prolific attack sources are compiled and shared. The goal of blacklists is to predict and block future attack sources. Existing blacklisting techniques have focused on the most prolific attack sources and, more recently, on collaborative blacklisting. In this paper, we formulate the problem of forecasting attack sources (also referred to as "predictive blacklisting") based on shared attack logs, as an implicit recommendation system. We compare the performance of existing approaches against the upper bound for prediction and we demonstrate that there is much room for improvement. Inspired by the recent NetFlix competition, we propose a multi-level collaborative filtering model that is adjusted and tuned specifically for the attack forecasting problem. Our model captures and combines various factors namely: attacker-victim history (using time-series) and attackers and/or victims interactions (using neighborhood models). We evaluate our combined method on one month of logs from Dshield.org and demonstrate that it improves significantly the prediction rate over state-of-the-art methods as well as the robustness against poisoning attacks.

Predictive Blacklisting as an Implicit Recommendation System

Network coding, the notion of performing coding operations on the contents of packets while in transit through the network, was originally developed for wired networks; recently, however, it has been also applied with success also to wireless ad hoc networks. In fact, it has been shown that network coding can yield substantial performance gains, e.g., reduced energy consumption, in ad hoc networks. In this paper, we compare, using linear programming formulations, the maximum throughput that a multicast application can achieve with and without network coding in unreliable ad hoc networks; we show that network coding achieves 65% higher throughput than conventional multicast in a typical ad hoc network scenario. The superiority of network coding, already established by the analytic results, is confirmed by simulation experiments.

/pdf/performance-of-network-coding-in-ad-hoc-networks-2wdke0atli.pdf

Performance of Network Coding in Ad Hoc Networks

Streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. A problem, scarcely addressed so far, is how to distribute video streaming traffic from one source to all nodes in an urban vehicular network. This problem significantly differs from previous work on broadcast and multicast in ad hoc networks because of the highly dynamic topology of vehicular networks and the strict delay requirements of streaming applications. We present a solution for intervehicular communications, called Streaming Urban Video (SUV), that 1) is fully distributed and dynamically adapts to topology changes, and 2) leverages the characteristics of streaming applications to yield a highly efficient, cross-layer solution.

Video Streaming Distribution in VANETs

Vehicle communications are becoming a reality, driven by navigation safety requirements and by the investments of car manufacturers and Public Transport Authorities. As a consequence many of the essential vehicle grid components (radios, Access Points, spectrum, standards, etc.) will soon be in place (and paid for) paving the way to unlimited opportunities for other car-to-car applications beyond safe navigation, for example, from news to entertainment, mobile network games and civic defense. In this study, we take a visionary look at these future applications, the emerging "Vehicular Grid" that will support them and the interplay between the grid and the communications infrastructure.In essence, the Vehicular Grid is a large scale ad hoc network. However, an important feature of the Vehicular Grid, which sets it apart from most instantly-deployed ad hoc networks, is the ubiquitous presence of the infrastructure (and the opportunity to use it). While the Vehicular Grid must be entirely self-supporting for emergency operations (natural disaster, terrorist attack, etc), it should exploit the infrastructure (when present) during normal operations. In this paper we address the interaction between vehicles and Internet servers through Virtual Grid and Internet Infrastructure. This includes transparent geo-route provisioning across the Internet, mobile resource monitoring, and mobility management (using back up services in case of infrastructure failure). We then focus on routing and show the importance of Infrastructure cooperation and feedback for efficient, congestion free routing.

/pdf/vehicular-grid-communications-the-role-of-the-internet-3r0fxvjdwb.pdf

Vehicular grid communications: the role of the internet infrastructure

How can we protect the network infrastructure from malicious traffic, such as scanning, malicious code propagation, and distributed denial-of-service (DDoS) attacks? One mechanism for blocking malicious traffic is filtering: access control lists (ACLs) can selectively block traffic based on fields of the IP header. Filters (ACLs) are already available in the routers today but are a scarce resource because they are stored in expensive ternary content addressable memory (TCAM). In this paper, we develop, for the first time, a framework for studying filter selection as a resource allocation problem. Within this framework, we study four practical cases of source address/prefix filtering, which correspond to different attack scenarios and operator's policies. We show that filter selection optimization leads to novel variations of the multidimensional knapsack problem and we design optimal, yet computationally efficient, algorithms to solve them. We also evaluate our approach using data from Dshield.org and demonstrate that it brings significant benefits in practice. Our set of algorithms is a building block that can be immediately used by operators and manufacturers to block malicious traffic in a cost-efficient way.

/pdf/optimal-filtering-of-source-address-prefixes-models-and-1hat3fhpq7.pdf

Fabio Soldo

Papers

Predictive Blacklisting as an Implicit Recommendation System

Performance of Network Coding in Ad Hoc Networks

Video Streaming Distribution in VANETs

Vehicular grid communications: the role of the internet infrastructure

Optimal Filtering of Source Address Prefixes: Models and Algorithms