Optimal decision rule-based ex-ante frequency hopping for jamming avoidance in wireless sensor networks

Bhavathankar, Prasenjit; Sarkar, Subhadeep; Misra, Sudip

doi:10.1016/j.comnet.2017.03.009

Cited by 10 publications

(12 citation statements)

References 22 publications

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“…One main problem of rule-based detection mechanisms is that a large number of rules may be required for each variation of the attacks which makes it a limiting constraint, particularly in the era of fast-changing network attack types. In [10], frequency hopping strategies are derived from the optimal decision rules. The model evaluates the individual decision profiles of all the nodes and finally selects a rule that yields the maximum throughput.…”

Section: ) Rule-based Jamming Attack Solutionsmentioning

confidence: 99%

Federated Learning-Based Cognitive Detection of Jamming Attack in Flying Ad-Hoc Network

et al. 2020

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Flying Ad-hoc Network (FANET) is a decentralized communication system solely formed by Unmanned Aerial Vehicles (UAVs). In FANET, the UAV clients are vulnerable to various malicious attacks such as the jamming attack. The aerial adversaries in the jamming attack disrupt the communication of the victim network through interference on the receiver side. Jamming attack detection in FANET poses new challenges for its key differences from other ad-hoc networks. First, because of the varying communication range and power consumption constraints, any centralized detection system becomes trivial in FANET. Second, the existing decentralized solutions, disregarding the unbalanced sensory data from new spatial environments, are unsuitable for the highly mobile and spatially heterogeneous UAVs in FANET. Third, given a huge number of UAV clients, the global model may need to choose a sub-group of UAV clients for providing a timely global update. Recently, federated learning has gained attention, as it addresses unbalanced data properties besides providing communication efficiency, thus making it a suitable choice for FANET. Therefore, we propose a federated learning-based on-device jamming attack detection security architecture for FANET. We enhance the proposed federated learning model with a client group prioritization technique leveraging the Dempster-Shafer theory. The proposed client group prioritization mechanism allows the aggregator node to identify better client groups for calculating the global update. We evaluated our mechanism with datasets from publicly available standardized jamming attack scenarios by CRAWDAD and the ns-3 simulated FANET architecture and showed that, in terms of accuracy, our proposed solution (82.01% for the CRAWDAD dataset and 89.73% for the ns-3 simulated FANET dataset) outperforms the traditional distributed solution (49.11% for the CRAWDAD dataset and 65.62% for the ns-3 simulated FANET dataset). Moreover, the Dempster-Shafer-based client group prioritization mechanism identifies the best client groups out of 56 client group combinations for efficient federated averaging.INDEX TERMS Unmannad aerial vehicle, flying ad-hoc network, jamming attack, federated learning, ondevice AI, Dempster-Shafer theory.

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Section: ) Rule-based Jamming Attack Solutionsmentioning

confidence: 99%

Federated Learning-Based Cognitive Detection of Jamming Attack in Flying Ad-Hoc Network

et al. 2020

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“…Jamming can target different layers: It can interfere with radio frequency signals at the physical layer, as it can take advantages of the medium access control protocol causing malicious collisions at the link layer as it can target the network layer by injecting malicious packets. Defense techniques [38][39] [40] against Jamming attack involve spread-spectrum techniques for radio communication such as frequency hopping to switch between many frequency channels, authentication mechanisms to detect malicious and replayed packets, the use of secure medium access control protocols etc.…”

Section: The Most Known Attacksmentioning

confidence: 99%

Attacks classification and security mechanisms in Wireless Sensor Networks

Kardi¹,

Zagrouba²

2019

Adv. sci. technol. eng. syst. j.

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This paper proposes a new classification model distinguishing four classes of attacks in Wireless Sensor Networks (WSNs) namely: attacks based on the protocol stack, on the capability of the attacker, on the attack impacts and on the attack target. Then, it presents and classifies the most known attacks in WSNs based the proposed model. Simulations implemented under the NS3 simulator prove that the network lifetime can decrease by more than 45% in the presence of attacks. Afterwards, it discusses the main security methods and protocols of management and distribution of encryption keys used to ward off different types of attacks. Obtained results confirm that these security methods must be adapted to the specific characteristics of WSNs to achieve the intended objectives.

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“…However, due to the silent nature or behavior of reactive jammers, they have more powers to destruct these mitigation methods. In recent times, number of approaches has been presented to a utilize spectrum efficiently and address spectrum requirement for provisioning future application [14]. These model aims to attain optimal use of available resource (spectrum).…”

Section: Introductionmentioning

confidence: 99%

“…These model aims to attain optimal use of available resource (spectrum). Thus, these model are referred as optimal resource allocation schemes [14,15]. In optimal resource allocation schemes, the sensor device, rather than avoiding a jammer by using different instance of spectrum, depends on exploring the existing spectrum in most resourceful way to mitigate jamming.…”

Section: Introductionmentioning

confidence: 99%

Maximize resource utilization based channel access model with presence of reactive jammer for underwater wireless sensor network

Bagali¹,

Sundaraguru²

2020

IJECE

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Underwater sensor networks (UWSNs) are vulnerable to jamming attacks. Especially, reactive jamming which emerged as a greatest security threat to UWSNs. Reactive jammer are difficult to be removed, defended and identified. Since reactive jammer can control and regulate (i.e., the duration of the jam signal) the probability of jamming for maintaining high vulnerability with low detection probability. The existing model are generally designed considering terrestrial wireless sensor networks (TWSNs). Further, these models are limited in their ability to detect jamming correctly, distinguish between the corrupted and uncorrupted parts of a packet, and be adaptive with the dynamic environment. Cooperative jamming model has presented in recent times to utilize resource efficiently. However, very limited work is carried out using cooperative jamming detection. For overcoming research challenges, this work present Maximize Resource Utilization based Channel Access (MRUCA). The MRUCA uses cross layer design for mitigating reactive jammer (i.e., MRUCA jointly optimizes the cooperative hopping probabilities and channel accessibility probabilities of authenticated sensor device). Along with channel, load capacity of authenticated sensor device is estimated to utilize (maximize) resource efficiently. Experiment outcome shows the proposed MRUCA model attain superior performance than state-of-art model in terms of packet transmission, BER and Detection rate.

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Optimal decision rule-based ex-ante frequency hopping for jamming avoidance in wireless sensor networks

Cited by 10 publications

References 22 publications

Federated Learning-Based Cognitive Detection of Jamming Attack in Flying Ad-Hoc Network

Federated Learning-Based Cognitive Detection of Jamming Attack in Flying Ad-Hoc Network

Attacks classification and security mechanisms in Wireless Sensor Networks

Maximize resource utilization based channel access model with presence of reactive jammer for underwater wireless sensor network

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