The Sleep Deprivation Attack in Sensor Networks: Analysis and Methods of Defense

Pirretti, Matthew; Zhu, Sencun; Narayanan, Vijaykrishnan; McDaniel, Patrick; Kandemir, Mahmut; Brooks, Richard

doi:10.1080/15501320600642718

Cited by 102 publications

(63 citation statements)

References 31 publications

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“…In sensor networks like the cloud based energy management system, the ability of the devices to enter powersaving modes (e.g various sleep and hibernation modes) is important to preserve the network's longevity, the lifetime of these devices, and reduce the overall power consumption [36,37,15]. In this section two major attacks that target this functionality of the devices will be discussed: sleep deprivation and barrage attack.…”

Section: Energy Bleedingmentioning

confidence: 99%

“…In sleep deprivation attack, malicious nodes on the network, send requests to the victim device only as often as necessary to keep it switched on. Although the goal of this attack is the same as the barrage attack, the sleep deprivation attack does not make the target nodes perform energy intense operations [36]. Barrage attack has been proven to exhaust faster the battery resources of the targeted nodes [36], but at the same time it is very easy to detect as opposed to sleep deprivation attack.…”

Section: Energy Bleedingmentioning

confidence: 99%

“…Although the goal of this attack is the same as the barrage attack, the sleep deprivation attack does not make the target nodes perform energy intense operations [36]. Barrage attack has been proven to exhaust faster the battery resources of the targeted nodes [36], but at the same time it is very easy to detect as opposed to sleep deprivation attack. For this reason we consider sleep deprivation to be more a more serious threat [21].…”

Section: Energy Bleedingmentioning

confidence: 99%

“…Pirreti et al [36] showed that sleep deprivation attack can impact severely networks like the LEMS. They demonstrated that if an attacker manages to compromise as few as 20 devices on a 400 node network, they will be able to double its power consumption.…”

Section: Energy Bleedingmentioning

confidence: 99%

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Secure Data Sharing and Analysis in Cloud-Based Energy Management Systems

Anthi

Javed

Rana

et al. 2017

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Analysing data acquired from one or more buildings (through specialist sensors, energy generation capability such as PV panels or smart meters) via a cloud-based Local Energy Management System (LEMS) is increasingly gaining in popularity. In a LEMS, various smart devices within a building are monitored and/or controlled to either investigate energy usage trends within a building, or to investigate mechanisms to reduce total energy demand. However, whenever we are connecting externally monitored/controlled smart devices there are security and privacy concerns. We describe the architecture and components of a LEMS and provide a survey of security and privacy concerns associated with data acquisition and control within a LEMS. Our scenarios specifically focus on the integration of Electric Vehicles (EV) and Energy Storage Units (ESU) at the building premises, to identify how EVs/ESUs can be used to store energy and reduce the electricity costs of the building. We review security strategies and identify potential security attacks that could be carried out on such a system, while exploring vulnerable points in the system. Additionally, we will systematically categorize each vulnerability and look at potential attacks exploiting that vulnerability for LEMS. Finally, we will evaluate current counter measures used against these attacks and suggest possible mitigation strategies.

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Section: Energy Bleedingmentioning

confidence: 99%

Section: Energy Bleedingmentioning

confidence: 99%

Section: Energy Bleedingmentioning

confidence: 99%

Section: Energy Bleedingmentioning

confidence: 99%

See 2 more Smart Citations

Secure Data Sharing and Analysis in Cloud-Based Energy Management Systems

Anthi

Javed

Rana

et al. 2017

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…If sensors use techniques to save energy by powering down components, the advantage of these techniques can't be used anymore. This sleep-deprivation attack is studied in [13], [14], [15]. Some approaches can be made to lower the impact of discovery message injection.…”

Section: B Outside Attacks Against Arpdmentioning

confidence: 99%

ARPD: Asynchronous random key predistribution in the LEAP framework for Wireless Sensor Networks

Achtzehn¹,

Rohner

Rodhe

2007

2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems

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Abstract-In the LEAP framework for wireless sensor networks a set of keys is used to secure communication. LEAP distinguishes between unicast (pairwise) communication, group (cluster) communication and global (broadcast) communication. The keys used in pairwise communication are derived from an initial key KI that nodes are equipped with prior to deployment and that is deleted after link setup. Further keys are distributed encrypted with these pairwise keys. If the initial key is ever disclosed, the whole network is compromised. To lower the threat of KI disclosure, we present a novel KI -less scheme for key predistribution. Our scheme is based on random key predistribution, and proves to perform better in medium sized networks than previous proposals. It is resilient against node capture attacks and allows node to node authentication. Attacks against overlying protocols in the network are more difficult with this scheme. We have conducted computations to show the feasibility of our scheme for networks up to a size of 1000 nodes. By introducing a key reuse system we are able to increase the probability of a successful link setup. We have included a security analysis that discusses our scheme's resistance against commonly known attacks.Index Terms-Wireless sensor networks, Network-level security and protection

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