The use of distinct, dedicated communication channels to transmit data and control traffic introduces a single point of failure for a denial of service attack, in that an adversary may be able to jam control channel traffic and prevent relevant data traffic. Hence, it is of interest to design control channel access schemes which are resilient to jamming. We map the problem of providing resilient control channel access under jamming to that of secure communication channel establishment. We propose the use of random key distribution to hide the location of control channels in time and/or frequency. We evaluate performance metrics of resilience to control channel jamming, identification of compromised users, and delay due to jamming as a function of the number of compromised users.
The rising demand for high-quality online services requires reliable packet delivery at the network layer. Data-plane fault localization is recognized as a promising means to this end, since it enables a source node to efficiently localize faulty links, find a fault-free path, and enforce contractual obligations among network nodes. Existing fault localization protocols cannot achieve a practical tradeoff between security and efficiency and they require unacceptably long detection delays, and require monitored flows to be impractically long-lived. In this paper, we propose an efficient fault localization protocol called Short-MAC which leverages probabilistic packet authentication and achieves 100 - 10000 times lower detection delay and overhead than related work. We theoretically derive a lower-bound guarantee on data-plane packet delivery in ShortMAC, implement a ShortMAC prototype, and evaluate its effectiveness on two platforms: SSFNet simulator and Linux/Click router. Our implementation and evaluation results show that ShortMAC causes negligible throughput and latency costs while retaining a high level of security.
Connected embedded systems in the realm of smart infrastructures comprise ubiquitous end-point devices supported by a communication infrastructure. Device, energy supply and network failures are a reality and provisioned communications could fail. Self-organization is a process where network devices cooperate with each other to restore network connectivity on detecting network connectivity failures. Self-organized networks are envisioned to be hierarchical, implying that a root device is expected to spend more energy to forward the entire network's data. This leads to battery exhaustion and therefore a single point of failure in the system. In this paper we address this problem by proposing an energy-governed resilient networking framework. Our framework enforces a policy to throttle upstream network traffic to maintain energy drain at the root device. To demonstrate the effectiveness of the proposed policy, we designed our experiment framework using Nano-RK and FireFly; a lightweight operating system and sensing platform respectively.
The Internet of Things is a paradigm that allows the interaction of ubiquitous devices through a network to achieve common goals. This paradigm like any man-made infrastructure is subject to disasters, outages and other adversarial conditions. Under these situations provisioned communications fail, rendering this paradigm with little or no use. Hence, network self-organization among these devices is needed to allow for communication resilience. This paper presents a survey of related work in the area of self-organization and discusses future research opportunities and challenges for self-organization in the Internet of Things. We begin this paper with a system perspective of the Internet of Things. We then identify and describe the key components of self-organization in the Internet of Things and discuss enabling technologies. Finally we discuss possible tailoring of prior work of other related applications to suit the needs of self-organization in the Internet of Things paradigm.
Vehicular Ad-Hoc Networks (VANETs) can potentially become a sensing platform. In-network aggregation, a fundamental primitive for querying sensory data, has been shown to reduce overall communication overhead at large. To secure data aggregation in VANETs, existing schemes mainly rely on digital signatures. However, generating and verifying such signatures can cause high computational overhead. More importantly, time-consuming verifications lead to the vulnerability to signature flooding attacks in which a receiver cannot timely verify all messages before their respective deadlines. In this paper, we propose ASIA as an Accelerated Secure In-network Aggregation strategy that can accelerate message verifications and significantly reduce computational overhead while retaining satisfactory security. We replace the most common tree graph with a directed acyclic graph as the aggregation structure. Resulting redundancy in information flow offers the opportunity for misbehavior detection. Meanwhile, by leveraging time asymmetry, upstream nodes in the structure can verify downstream messages through the modified light-weight TESLA scheme. We analyze the security properties of ASIA and provide evaluation results. We show that ASIA can largely accelerate message verifications and drastically reduce computational and communication overhead compared to existing schemes using the resource-consuming Elliptic Curve Digital Signature Algorithm.
Despite the advent of numerous Internet-of-Things (IoT) applications, recent research demonstrates potential side-channel vulnerabilities exploiting sensors which are used for event and environment monitoring. In this paper, we propose a new side-channel attack, where a network of distributed non-acoustic sensors can be exploited by an attacker to launch an eavesdropping attack by reconstructing intelligible speech signals. Specifically, we present PitchIn to demonstrate the feasibility of speech reconstruction from non-acoustic sensor data collected offline across networked devices. Unlike speech reconstruction which requires a high sampling frequency (e.g., > 5 KHz), typical applications using non-acoustic sensors do not rely on richly sampled data, presenting a challenge to the speech reconstruction attack. Hence, PitchIn leverages a distributed form of Time Interleaved Analog-Digital-Conversion (TIADC) to approximate a high sampling frequency, while maintaining low per-node sampling frequency. We demonstrate how distributed TI-ADC can be used to achieve intelligibility by processing an interleaved signal composed of different sensors across networked devices. We implement PitchIn and evaluate reconstructed speech signal intelligibility via user studies. PitchIn has word recognition accuracy as high as 79%. Though some additional work is required to improve accuracy, our results suggest that eavesdropping using a fusion of non-acoustic sensors is a real and practical threat.
Jamming broadcasting to intentionally interfere with wireless reception, has long been a problem for wireless systems. Recent research demonstrates numerous advances in jamming techniques that increase attack efficiency or reduce the probability an attack will be detected by choosing attack parameters based on a system's configuration. In this work, we extend the attacker's capabilities by modifying the attack parameters in response to the observed performance of the target system, effectively creating a feedback loop in our attack model. This framework allows for more intricate attack models that are tuned online allowing for closer to optimal attacks against legitimate systems. To show the feasibility of the listening and attacking framework we introduce an attack called Self-Tuned, Inference-based, Real-time jamming or STIR-jamming. This attack listens to legitimate communication traffic, infers the systems performance, and optimizes jamming parameters. We propose the two types of STIR-jamming, mSTIR-jamming and tSTIR-jamming, and implement these attacks against an IEEE 802.15.4 link as a case study. With the empirical results, we demonstrate the attack system adapting to various scenarios and finding stable solutions.
Multiple-path source routing protocols allow a data source node to distribute the total traffic among available paths. In this paper, we consider the problem of jamming-aware source routing in which the source node performs traffic allocation based on empirical jamming statistics at individual network nodes. We formulate this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics. We show that in multisource networks, this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). We demonstrate the network's ability to estimate the impact of jamming and incorporate these estimates into the traffic allocation problem. Finally, we simulate the achievable throughput using our proposed traffic allocation method in several scenarios.
Joint analysis of security and routing protocols in wireless networks reveals vulnerabilities of secure network traffic that remain undetected when security and routing protocols are analyzed independently.We formulate a class of continuous metrics to evaluate the vulnerability of network traffic as a function of security and routing protocols used in wireless networks.We develop two complementary vulnerability definitions using set theoretic and circuit theoretic interpretations of the security of network traffic, allowing a network analyst or an adversary to determine weaknesses in the secure network.We formalize node capture attacks using the vulnerability metric as a nonlinear integer programming minimization problem and propose the GNAVE algorithm, a Greedy Node capture Approximation using Vulnerability Evaluation.We discuss the availability of security parameters to the adversary and show that unknown parameters can be estimated using probabilistic analysis.We demonstrate vulnerability evaluation using the proposed metrics and node capture attacks using the GNAVE algorithm through detailed examples and simulation.
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