Fabio Soldo

Performance of Network Coding in Ad Hoc Networks

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

PiggyCode: A MAC Layer Network Coding Scheme to Improve TCP Performance Over Wireless Networks

In this paper we propose PiggyCode, a network-coding based scheme specifically designed to enhance TCP performance over IEEE 802.11 multi-hop wireless networks. The root of this approach is a network coding module operating between the network and the MAC layer. Each node running PiggyCode encodes, whenever it is possible, TCP-DATA and TCP-ACK packets belonging to the same information flow. The coding approach is conceptually analogous to piggyback the TCP- ACK packet within the TCP-DATA packet, with the substantial difference that, by performing network coding operations, the actual packet size remains unchanged. The proposed scheme is simple and effective. It leverages the benefits of network coding in the wireless environment, to jointly reduce the overall number of transmissions on the channel and speed up the delivery process of TCP-ACK packets, thus achieving significant improvements in terms of TCP performance.

Optimal Filtering of Source Address Prefixes: Models and Algorithms

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 operators 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.

Streaming Media Distribution in VANETs

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 support streaming traffic in vehicular networks (VANETs). This problem significantly differs from previous work on broadcast and multicast in ad hoc networks, because of the highly-dynamic topology of VANETs and the strict delay requirements of streaming applications. Our solution, completely relies on inter-vehicular communication for the distribution of multimedia content in a urban environment and it has the following appealing features: (i) it is fully distributed and dynamically adapts to topology changes; (ii) it leverages the characteristics of streaming applications to yield a highly-efficient, cross-layer solution. After optimally dimensioning the network system, we compare the performance of our solution against theoretical results for broadcast capacity in multihop networks.

Blacklisting Recommendation System: Using Spatio-Temporal Patterns to Predict Future Attacks

In this paper, we study the problem of forecasting attack sources based on past attack logs from several contributors. We formulate this problem as an implicit recommendation system, and we propose a multi-level prediction model to solve it. Our model evaluates and combines various factors, namely: (i) attacker-victim history using time-series, (ii) attackers and/or victims interactions using neighborhood models and (iii) global patterns using singular value decomposition. We evaluate our combined method, referred to as Blacklisting Recommendation System (or BRS), on one month of logs from Dshield, and we demonstrate that it improves significantly the prediction rate over state-of-the-art methods as well as the robustness against poisoning attacks. Along the way, we analyze the Dshield dataset, and we reveal dominant patterns of malicious traffic.

A Simple Optimization Model for Wireless Opportunistic Routing with Intra-Session Network Coding

In this paper, we consider the optimization of multi-path opportunistic routing and congestion control in wireless mesh networks with intra-session network coding. We model this as a network utility maximization problem and we design a simple distributed solution that can be obtained by solving the dual problem. This formulation provides useful insight into the complex cross-layer interaction between intra-session network coding, multi-path routing and transport layer and a mathematical foundation for protocol design in this scenario.

Cooperative Synchronous Broadcasting in Infrastructure-to-Vehicles Networks

This paper presents a novel protocol for the support of audio/video streaming in a vehicular network. The protocol is based on localization and timing, both of them obtained via satellite positioning. Transmissions are coordinated to avoid collisions. The protocol is evaluated using an innovative semi-analytic technique based on stochastic graph processes that allows powerful parametric analysis in scenarios with hundreds of cars and complex mobility models. This is a very important point to gain insight in the fundamental behavior of vehicular networking. Results presented set the requirements for the system deployment, and analyze the behavior of the system changing scenarios (e.g., car density) and protocol parameters. They show the feasibility of the system and basic performances like service availability.

Filtering sources of unwanted traffic

There is a large and increasing amount of unwanted traffic on the Internet today, including phishing, spam, and distributed denial-of-service attacks. One way to deal with this problem is to filter unwanted traffic at the routers based on source IP addresses. Because of the limited number of available filters in the routers today, aggregation is used in practice: a single filter describes and blocks an entire range of IP addresses. This results in blocking of all (unwanted and wanted) traffic generated from hosts with IP addresses in that range. In this paper, we develop a family of algorithms that, given a blacklist containing the source IP addresses of unwanted traffic and a constraint on the number of filters, construct a set of filtering rules that optimize the tradeoff between the unwanted and legitimate traffic that is blocked. We show that our algorithms are optimal and also computationally efficient. Furthermore, we demonstrate that they are particularly beneficial when applied to realistic distributions of sources of unwanted traffic, which are known to exhibit spatial and temporal clustering.