Conference Paper

papers/176 Quantifying Eavesdropping Vulnerability in Sensor Networks

DOI: 10.1145/1080885.1080887 Conference: Proceedings of the 2nd Workshop on Data Management for Sensor Networks, in conjunction with VLDB, DMSN 2005, Trondheim, Norway, August 30, 2005
Source: DBLP

ABSTRACT

With respect to security, sensor networks have a number of considerations that separate them from traditional distributed systems. First, sensor devices are typically vulnerable to physical compromise. Second, they have significant power and processing constraints. Third, the most critical security issue is protecting the (statistically derived) aggregate output of the system, even if individual nodes may be compromised. We suggest that these considerations merit a rethinking of traditional security techniques: rather than depending on the resilience of cryptographic techniques, in this paper we develop new techniques to tolerate compromised nodes and to even mislead an adversary. We present our initial work on probabilistically quantifying the security of sensor network protocols, with respect to sensor data distributions and network topologies. Beginning with a taxonomy of attacks based on an adversary's goals, we focus on how to evaluate the vulnerability of sensor network protocols to eavesdropping. Different topologies and aggregation functions provide different probabilistic guarantees about system security, and make different trade-offs in power and accuracy.

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    • "There raise a number of security threats in IoT, especially in WNoT, where the conventional security countermeasures used in wired networks may not work well in WNoT due to the following inherent constraints of WNoT: (i) the wireless medium is open for any nodes [15]; (ii) it is extremely difficult to deploy centralized control mechanisms in such distributed WNoT [2] [16] [17]. Eavesdropping attack, as one of typical security threats in wireless communication systems, has attracted considerable attention recently [18] [19] [20] [21] [22] [23] [24] since many adversary attacks often follow the eavesdropping activity, for example, the man-in-the-middle attack [25] and the hearand-fire attack [19]. Figure 1 shows a typical example of eavesdropping attacks in a warehouse environment, where each product is attached with an RFID tag, which can passively communicate with loss, the shadowing effect, and the multipath effect [43]. "
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    ABSTRACT: The security of Internet of Things (IoT) has received extensive attention recently. This paper presents a novel analytical model to investigate the eavesdropping attacks in Wireless Net of Things (WNoT). Our model considers various channel conditions, including the path loss , the shadow fading effect , and Rayleigh fading effect . Besides, we also consider the eavesdroppers in WNoT equipped with either omnidirectional antennas or directional antennas. Extensive simulation results show that our model is accurate and effective to model the eavesdropping attacks in WNoT. Besides, our results also indicate that the probability of eavesdropping attacks heavily depends on the shadow fading effect, the path loss effect, Rayleigh fading effect, and the antenna models. In particular, we find that the shadow fading effect is beneficial to the eavesdropping attacks while both the path loss effect and Rayleigh fading effect are detrimental. Besides, using directional antennas at eavesdroppers can also increase the eavesdropping probability. Our results offer some useful implications on designing antieavesdropping schemes in WNoT.
    Full-text · Article · Jan 2016 · Mobile Information Systems
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    • "The eavesdropping security [1]–[6] of wireless ad hoc networks has received extensive attentions recently since many malicious attacks often follow the eavesdropping activities [7]. However, most of the current studies have only concentrated on either mitigating the eavesdropping activities [2]–[6] or protecting the communications between the transmitters and the receivers (also named as good nodes) by using encryption algorithms [8]. "
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    ABSTRACT: This paper concerns the eavesdropping problem from the eavesdroppers' perspective, which is new since most of previous studies only concentrate on the good nodes. We propose an analytical framework to investigate the eavesdropping attacks, taking account into various channel conditions and antenna models. Our extensive numerical results show that the probability of eavesdropping attacks heavily depends on the shadow fading effect, the path loss effect and the antenna models; particularly, they imply that using directional antennas at eavesdroppers can increase the probability of eavesdropping attacks when the path loss effect is less notable. This study is helpful for us to prevent the eavesdropping attacks effectively and economically.
    Full-text · Conference Paper · Nov 2014
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    • "Eavesdropping attack is a typical passive attack in AHNets. The eavesdropping security [2] [3] [4] [5] [6] [7] of AHNets has received extensive attentions recently since many malicious attacks often follow the eavesdropping activities [8]. However, most of the current studies have only concentrated on either mitigating the eavesdropping activities [3] [4] [5] [6] [7] or protecting the communications between the transmitters and the receivers by using encryption algorithms [9]. "
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    ABSTRACT: This paper concerns the eavesdropping attacks from the eavesdroppers’ perspective, which is new since most of current studies consider the problem from the good nodes’ perspective. In this paper, we originally propose an analytical framework to quantify the effective area and the probability of the eavesdropping attacks. This framework enables us to theoretically evaluate the impact of node density, antenna model, and wireless channel model on the eavesdropping attacks. We verify via extensive simulations that the proposed analytical framework is very accurate. Our results show that the probability of eavesdropping attacks significantly vary, depending on the wireless environments (such as shadow fading effect, node density, and antenna types). This study lays the foundation toward preventing the eavesdropping attacks in more effective and more economical ways.
    Full-text · Article · Nov 2014 · Journal of Computational Science
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