Salvatore D'Oro

Defeating Jamming With the Power of Silence: A Game-Theoretic Analysis

The timing channel is a logical communication channel in which information is encoded in the timing between events. Recently, the use of the timing channel has been proposed as a countermeasure to reactive jamming attacks performed by an energy-constrained malicious node. In fact, while a jammer is able to disrupt the information contained in the attacked packets, timing information cannot be jammed, and therefore, timing channels can be exploited to deliver information to the receiver even on a jammed channel. Since the nodes under attack and the jammer have conflicting interests, their interactions can be modeled by means of game theory. Accordingly, in this paper, a game-theoretic model of the interactions between nodes exploiting the timing channel to achieve resilience to jamming attacks and a jammer is derived and analyzed. More specifically, the Nash equilibrium is studied in terms of existence, uniqueness, and convergence under best response dynamics. Furthermore, the case in which the communication nodes set their strategy and the jammer reacts accordingly is modeled and analyzed as a Stackelberg game, by considering both perfect and imperfect knowledge of the jammers utility function. Extensive numerical results are presented, showing the impact of network parameters on the system performance.

Exploiting Congestion Games to Achieve Distributed Service Chaining in NFV Networks

The network function virtualization (NFV) paradigm has gained increasing interest in both academia and industry as it promises scalable and flexible network management and orchestration. In NFV networks, network services are provided as chains of different virtual network functions (VNFs), which are instantiated and executed on dedicated VNF-compliant servers. The problem of composing those chains is referred to as the service chain composition problem. In contrast to centralized solutions that suffer from scalability and privacy issues, in this paper, we leverage non-cooperative game theory to achieve a low-complexity distributed solution to the above-mentioned problem. Specifically, to account for selfish and competitive behavior of users, we formulate the service chain composition problem as an atomic weighted congestion game with unsplittable flows and player-specific cost functions. We show that the game possesses a weighted potential function and admits a Nash equilibrium (NE). We prove that the price of anarchy is upper-bounded, and also propose a distributed and privacy-preserving algorithm which provably converges toward an NE of the game in polynomial time. Finally, through extensive numerical results, we assess the performance of the proposed distributed solution to the service chain composition problem.

Exploiting Object Group Localization in the Internet of Things: Performance Analysis

In the Internet of Things (IoT), localization of objects is crucial for both information delivery and support of context-aware services. Unfortunately, the huge number of mobile objects that will be included in the IoT can result in a significant amount of signaling traffic for the purpose of location discovery and update. The major contributions of this paper are based on a simple evidence: In most IoT scenarios, several objects move together as they are carried by a human or a vehicle, i.e., a phenomenon that we refer to as object group mobility (OGM) naturally emerges. OGM can be exploited to reduce signaling traffic and to improve the accuracy of object localization. More specifically, in this paper, we introduce the OGM concept and explain how, by means of a collective agent representing a group of objects as whole, it is possible to reduce signaling traffic and improve accuracy in object localization; we derive an analytical framework to assess the advantages of the proposed approach, and we validate the analytical framework through extensive simulations.

A Game Theoretic Approach for Distributed Resource Allocation and Orchestration of Softwarized Networks

Softwarization of networks allows simplifying deployment, configuration, and management of network functions. The driving force toward this evolution is represented by software defined networking that allows more flexible and dynamic network resource allocation and management. The efficient allocation and orchestration of network resources is of extreme importance for this softwarization process, and many centralized solutions have been proposed. However, they are complex and exhibit scalability issues. So, distributed solutions are to be preferred but, in order to be effective, should quickly converge towards equilibrium solutions. In this paper, we focus on making distributed resource allocation and orchestration a viable approach, and prove convergence of the relevant mechanisms. Specifically, we exploit game theory to model interactions between users requesting network functions and servers providing these functions. Accordingly, a two-stage Stackelberg game is presented, where servers act as leaders of the game and users as followers. Servers have conflicting interests and try to maximize their utility; users, on the other hand, use a replicator behavior and try to imitate other user’s decisions to improve their benefit. The framework proves the existence and uniqueness of an equilibrium, and a learning mechanism to converge to such equilibrium is proposed. Numerical results show the effectiveness of the approach.

SatCache: a profile-aware caching strategy for information-centric satellite networks

Information-centric networking ICN is a new networking paradigm, which is attracting increasing attention from the scientific community. ICN focuses on content handling and distribution rather than host-to-host communications. Satellite systems have always played a key role in content distribution, and therefore, they are expected to be fundamental components of the ICN. However, it is not obvious whether solutions that are being developed for terrestrial ICNs are effective in satellite scenarios as well. More specifically, the focus of this paper is on in-network content caching, which is an important feature characterising all ICN proposals but, as we will show, should be redesigned to address the specific characteristics of satellite networks. Indeed, in this paper, a novel caching scheme named SatCache is proposed, which exploits the broadcast nature of the satellite communication medium and creates a profile of the preferences of network users in order to estimate their potential interest in a given content. The proposed approach is based on a simple model of the user behaviour. By aggregating the content requests generated by a large number of users, a collective behaviour can be observed, which fits the output of large measurement campaigns discussed in the literature. Simulation results given in the paper show the effectiveness of the proposed scheme. Copyright

Auction-based resource allocation in OpenFlow multi-tenant networks

In this paper, we investigate the allocation of network resources (such as FlowTable entries and bandwidth) in multi-tenant Software-Defined Networks (SDNs) that are managed by a FlowVisor. This resource allocation problem is modeled as an auction where the FlowVisor acts as the auctioneer and the network Controllers act as the bidders. The problem is analyzed by means of non-cooperative game theory, and it is shown that the auction admits a unique Nash Equilibrium (NE) under suitable conditions. Furthermore, a novel distributed learning procedure is provided that allows each Controller to reach the games unique NE in a few iterations by exploiting only locally available information. An implementation in OpenFlow-compliant SDNs is also proposed in a way that exploits native procedures already offered by OpenFlow. Finally, simulation results show that the proposed auction-based resource management scheme leads to significant improvements in network performance (for instance, achieving gains of up to 5 reduction in transmission delays).

Efficiency analysis of jamming-based countermeasures against malicious timing channel in tactical communications

A covert channel is a communication channel that creates a capability to transfer information between entities that are not supposed to communicate. A relevant instance of covert channels is represented by timing channels, where information is encoded in timing between events. Timing channels may result very critical in tactical scenarios where even malicious nodes can communicate in an undisclosed way. Jamming is commonly used to disrupt this kind of threatening wireless covert communications. However jamming, to be effective, should guarantee limited energy consumption. In this paper, an analysis of energy-constrained jamming systems used to attack malicious timing channels is presented. Continuous and reactive jamming systems are discussed in terms of their effect on the achievable covert channel capacity and jammer energy consumption. Also, a simple experimental set up is illustrated and used to identify proper operating points where jamming against malicious timing channels is effective while achieving limited energy consumption.

A marketplace as a scalable solution to the orchestration problem in SDN/NFV networks

In the SDN/NFV ecosystem, network services are provided as single Virtual Network Functions (VNFs) or chains of them, each instantiated and executed on dedicated servers. So far, the chaining of those virtual functions, which is also known as the service chain composition problem, has been mostly performed by Telco Operators (TOs) for the great advantages they receive in terms of Capex and OpEX. However, such a fully centralized approach generally results in solutions which do not scale well with the number of customers. The aim of this paper is to provide a distributed, scalable and efficient solution to the service chaining problem. Specifically, we develop an VNF marketplace system where third-party VNF providers sell VNF as a service (VNFaaS) and adapt their pricing policies according to network dynamics. Also, we leverage on game theory to provide a (theoretically proven) efficient distributed solution which accounts for monetary costs, communication latencies and congestion of computational resources.

Optimal Power Allocation and Scheduling Under Jamming Attacks

In this paper, we consider a jammed wireless scenario where a network operator aims to schedule users to maximize network performance while guaranteeing a minimum performance level to each user. We consider the case where no information about the position and the triggering threshold of the jammer is available. We show that the network performance maximization problem can be modeled as a finite-horizon joint power control and user scheduling problem, which is NP-hard. To find the optimal solution of the problem, we exploit dynamic programming techniques. We show that the obtained problem can be decomposed, i.e., the power control problem and the user scheduling problem can be sequentially solved at each slot. We investigate the impact of uncertainty on the achievable performance of the system and we show that such uncertainty leads to the well-known exploration-exploitation tradeoff. Due to the high complexity of the optimal solution, we introduce an approximation algorithm by exploiting state aggregation techniques. We also propose a performance-aware online greedy algorithm to provide a low-complexity sub-optimal solution to the joint power control and user scheduling problem under minimum quality-of-service requirements. The efficiency of both solutions is evaluated through extensive simulations, and our results show that the proposed solutions outperform other traditional scheduling policies.

Rate maximization under reactive jamming attacks: poster

Jamming attacks are able to partially or completely disrupt wireless communications. To overcome such a harmful attack, optimal scheduling of user transmissions should be achieved. Providing effective scheduling policies is a hard task which is made more complicated when reactive jamming attacks triggered by user transmissions are considered and no information about the jammer is available, e.g., the triggering threshold is not known. In this paper, we address the problem of maximizing network performance and guaranteeing minimum QoS requirements when reactive jamming attacks are ongoing. Specifically, to maximize network performance and avoid the triggering of the jammer, we formulate and solve a joint user scheduling and power control problem. The proposed solution is then assessed through numerical simulations.