Modeling and Evaluating Denial of Service Attacks for Wireless and Mobile Applications

With the advancement with flexible and configurable wireless adapters, it becomes feasible for a node to modify its low-layer (e.g. physical or MAC layer) parameters, which can substantially affect the network performance of a wireless network. Backoff misbehavior, in which a node deliberately manipulates its backoff time, can induce significant network problems, such as severe unfairness and denial-of-service. Although great progress has been made towards the design of countermeasures to backoff misbehavior, little attention has been focused on quantifying the gain of backoff misbehaviors. In this chapter, to assess the gain that misbehaving nodes can obtain, we introduce a new metric, namely order gain, to characterize the performance benefits of misbehaving nodes in comparison to legitimate nodes in CSMA/CA-based wireless networks. We derive the order gains of a variety of misbehaviors and further investigate the relation between our metric, order gain, and the throughput gain for a misbehaving node. We show that in IEEE 802.11 networks, the throughput ratio of a backoff misbehaving node to a legitimate node is either bounded above or proportional to the number of legitimate nodes. We use both simulations and experiments to validate our theoretical analysis and to further demonstrate the impact of a wide range of backoff misbehaviors on network performance in CSMA/CA-based wireless networks.

In this chapter, we aim at modeling and detecting jamming attacks against time-critical wireless networks with applications to the smart grid. In contrast to communication networks where packets-oriented metrics, such as packet loss and throughput are used to measure the network performance, we introduce a new metric, message invalidation ratio, to quantify the performance of time-critical applications. Our modeling approach is inspired by the similarity between the behavior of a jammer who attempts to disrupt the delivery of a time-critical message and the behavior of a gambler who tends to win a gambling game. Therefore, by gambling-based modeling and real-time experiments, we find that there exist a phase transition phenomenon for successful time-critical message delivery under a variety of jamming attacks. That is, as the probability that a packet is jammed increases from 0 to 1, the message invalidation ratio first increases slightly, then increases dramatically to 1. Based on analytical and experimental results, we design the jamming attack detection based on estimation (JADE) scheme to achieve efficient and robust jamming detection, and implement the JADE system in a wireless network for power substations in the smart grid.

In the previous chapter, we offered a comprehensive study on modeling and detecting jamming attacks against time-critical wireless networks with applications to the smart grid. However, only modeling and detection cannot provide defense against jamming attacks, and is only the first setup towards anti-jamming communication for wireless smart grid applications. Hence, spread spectrum systems, which provide jamming resilience via multiple frequency and code channels, must be adapted to the smart grid for secure wireless communications, while at the same time providing latency guarantee for control messages. An open question is how to minimize message delay for timely smart grid communication under any potential jamming attack.To address this issue, we provide a paradigm shift from the case-by-case methodology, which is widely used in existing works to investigate well-adopted attack models (also used in the previous chapter), to the worst-case methodology, which offers delay performance guarantee for smart grid applications under any attack. We first define a generic jamming process that characterizes a wide range of existing attack models. Then, we show that in all strategies under the generic process, the worst-case message delay is a U-shaped function of network traffic load. This indicates that, interestingly, increasing a fair amount of traffic can in fact improve the worst-case delay performance. As a result, we demonstrate a lightweight yet promising system, TACT (transmitting adaptive camouflage traffic), to combat jamming attacks. TACT minimizes the message delay by generating extra traffic called camouflage to balance the network load at the optimum. Experiments show that TACT can decrease the probability that a message is not delivered on time in order of magnitude for smart grid applications.

In this chapter, we aim to measure the resilience of mobile cloud services to malware. Since the cloud is intended to provide real-time services to mobile users, we introduce a new metric, resilience factor, to denote the maximally allowable percentage of malware-infected nodes in the network such that a required ratio of cloud service requests can still be processed on time. We find that for mobile cloud services, there exists a cutoff point on network bandwidth B, below which the resilience factor is an increasing function of B, and beyond which the resilience factor decreases on the order of 1∕B. Such a dichotomy demonstrates another perspective on developing network infrastructures for mobile cloud computing: although low bandwidth in current cellular networks is viewed as a primary drawback to support mobile cloud services, increasing bandwidth can, on the other hand, jeopardize malware resilience. In addition, we show via experiments that in malware epidemics, if B is less than the cutoff point, service quality is deteriorated mainly by network congestion (especially at mobility hotspots), thereby leading to heterogeneous malware resilience across the network; if B is larger than the cutoff point, malware-induced cloud overload becomes the performance bottleneck that can result in global service degradation. Our results encourage deployment of countermeasures in both the network and cloud to effectively defend against malware attacks.