Today's routing protocols critically rely on the assumption that the underlying hardware is trusted. Given the increasing number of attacks on network devices, and recent reports on hardware backdoors this assumption has become questionable. Indeed, with the critical role computer networks play today, the contrast between our security assumptions and reality is problematic. This paper presents Software-Defined Adversarial Trajectory Sampling (SoftATS), an OpenFlow-based mechanism to efficiently monitor packet trajectories, also in the presence of non-cooperating or even adversarial switches or routers, e.g., containing hardware backdoors. Our approach is based on a secure, redundant and adaptive sample distribution scheme which allows us to provably detect adversarial switches or routers trying to reroute, mirror, drop, inject, or modify packets (i.e., header and or payload). We evaluate the effectiveness of our approach in different adversarial settings, report on a proof-of-concept implementation, and provide a first evaluation of the performance overheads of such a scheme.
@cite_12 propose a hash-based delay sampling technique to detect switches misforwarding packets. Their threat model and objectives are closely related to ours. However, their sampling scheme and detection algorithm are different from ours. They require three switches to sample the same set of packets and their detection algorithm depends on the state of the switches buffers for a chosen path. We can incorporate their method of sampling i.e., choose triplets instead of pairs, but our detection is based on trajectories and not switch states.
@cite_12
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The next-generation Internet promises to provide a fundamental shift in the underlying architecture to support dynamic deployment of network protocols. With the introduction of programmability and dynamic protocol deployment in routers, potential vulnerabilities and attacks are expected to increase. In this paper, we consider the problem of detecting packet forwarding misbehavior in routers. Specifically, we focus on an attack scenario, where a router selectively drops packets destined for another node. Detecting such an attack is challenging since it requires differentiating malicious packet drops from congestion-based packet losses. We propose a controller-based misbehavior detection technique that effectively detects malicious routers using a hash-based delay sampling and verification. We provide a performance analysis of the detection accuracy and quantify the performance overhead of our system. Our results show that our technique provides accurate detection with low sampling rates. Network based intruders seldom attack directly from their own hosts, but rather stage their attacks through intermediate "stepping stones" to conceal their identity and origin. To identify attackers behind stepping stones, it is necessary to be able to correlate connections through stepping stones, even if those connections are encrypted or perturbed by the intruder to prevent traceability.The timing-based approach is the most capable and promising current method for correlating encrypted connections. However, previous timing-based approaches are vulnerable to packet timing perturbations introduced by the attacker at stepping stones. In this paper, we propose a novel watermark-based correlation scheme that is designed specifically to be robust against timing perturbations. The watermark is introduced by slightly adjusting the timing of selected packets of the flow. By utilizing redundancy techniques, we have developed a robust watermark correlation framework that reveals a rather surprising result on the inherent limits of independent and identically distributed (iid) random timing perturbations over sufficiently long flows. We also identify the tradeoffs between timing perturbation characteristics and achievable correlation effectiveness. Experiments show that the new method performs significantly better than existing, passive, timing-based correlation in the presence of random packet timing perturbations. Accurate analytical expressions of delay and packet reception probabilities, and energy consumption of duty-cycled wireless sensor networks with random medium access control (MAC) are instrumental for the efficient design and optimization of these resource-constrained networks. Given a clustered network topology with unslotted IEEE 802.15.4 and preamble sampling MAC, a novel approach to the modeling of the delay, reliability, and energy consumption is proposed. The challenging part in such a modeling is the random MAC and sleep policy of the receivers, which prevents to establish the exact time of data packet transmission. The analysis gives expressions as function of sleep time, listening time, traffic rate and MAC parameters. The analytical results are then used to optimize the duty cycle of the nodes and MAC protocol parameters. The approach provides a significant reduction of the energy consumption compared to existing solutions in the literature. Monte Carlo simulations by ns2 assess the validity of the analysis. We consider the fundamental delay bounds for scheduling packets In an N times N packet switch operating under the crossbar constraint. Algorithms that make scheduling decisions without considering queue backlog are shown to incur an average delay of at least O(N). We then prove that O(log(N)) delay is achievable with a simple frame based algorithm that uses queue backlog information. This is the best known delay bound for packet switches, and is the first analytical proof that sublinear delay is achievable in a packet switch with random inputs.