A Statistical Approach to Detect Jamming Attacks in Wireless Sensor Networks

Osanaiye, Opeyemi; Alfa, Attahiru Sule; Hancke, Gerhard P.

doi:10.3390/s18061691

Cited by 114 publications

(67 citation statements)

References 33 publications

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“…They deploy an exponentially-weighted moving average (EWMA) to detect anomalous changes in the intensity of a jamming attack event by using the packet inter-arrival feature of the received packets from the sensor nodes. Results obtained from a trace-driven simulation show that the proposed solution can efficiently and accurately detect jamming attacks in WSNs with little or no overhead [5]. Sousa et al extend the model of the event detection scenario to distinguish between events and faults by using a flow chart and confidence interval.…”

Section: Related Workmentioning

confidence: 99%

“…Ghorbel et al proposed an improved KPCA method based on the Mahalanobis kernel as a preprocessing step to extract relevant features for classification and to prevent abnormal events [17]. The literature [5,[8][9][10][11][12][13][14][15][16][17] does not consider the fault-tolerant mechanism of event detection. Different fault-tolerant algorithms are used in [6,7], but the influence of faulty nodes on event detection is not considered; this approach proves to be ineffective in high-fault-rate sensor networks.…”

Section: Related Workmentioning

confidence: 99%

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Fault-Tolerant Anomaly Detection Method in Wireless Sensor Networks

et al. 2018

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A key issue in wireless sensor network applications is how to accurately detect anomalies in an unstable environment and determine whether an event has occurred. This instability includes the harsh environment, node energy insufficiency, hardware and software breakdown, etc. In this paper, a fault-tolerant anomaly detection method (FTAD) is proposed based on the spatial-temporal correlation of sensor networks. This method divides the sensor network into a fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood and other regions for anomaly detection, respectively, to achieve fault tolerance. The results of experiment show that under the condition that 45% of sensor nodes are failing, the hit rate of event detection remains at about 97% and the false negative rate of events is above 92%.Information 2018, 9, 236 2 of 16 based on spatial-temporal correlation. The algorithm consists of two parts: the temporal correlation is used to obtain the probability of event and fault through the time-series data of sensor nodes, then we determine the state of sensor nodes. While according to the neighborhood definition, the sensor network is divided into the fault neighborhood, event and fault mixed neighborhoods, event boundary neighborhoods, and other areas, we use the minimum Bayesian risk decision method to distinguish the event nodes and faulty node. Fault tolerance is realized by abnormity detection to different neighborhoods, and experimental results and analysis show that the method can detect events well even under high fault rates.The contributions of this paper are summarized as follows:(i) In temporal correlation of sensor network, we propose the PCM and interval methods;(ii) In spatial correlation we divide the sensor network into fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood, and other regions for anomaly detection, respectively, to achieve fault tolerance. (iii) We conduct extensive simulations to evaluate the performance of the proposed algorithms.The results demonstrate the effectiveness of the proposed algorithms.The second section introduces the related work and research results. The third section introduces the symbol definitions and network model used in this paper. The fourth section introduces the detection method of fault-tolerance of wireless sensor networks. The fifth section offers the results and analysis of the experiment. The final section concludes the paper.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fault-Tolerant Anomaly Detection Method in Wireless Sensor Networks

et al. 2018

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“…Additionally, in such hostile environments, sensor nodes are prone to suffer damage which also reduces data gathering efficiency [4][5][6]. Furthermore, when interference levels are high or in case of a jamming attack [7], nodes cannot communicate to a sink node that may be found outside the monitored area. If emergency personnel is relying on retrieving information from nodes in the system, this becomes a major issue since some nodes may be disconnected from the sink node and cannot assist the police, military personnel, or firefighters in their specific operation.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, [6] does not consider graph theory to infer the performance of the system in terms of average buffer size nor packet delay. Finally, in [7], links are unreliable due to direct jamming cyberattacks. The authors propose a statistical approach to detect such attacks that may lead to disconnected segments in the system.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Graphs for Node Connectivity in Wireless Sensor Networks for Hostile Environments

et al. 2019

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Wireless sensor networks (WSNs) have been extensively studied in the literature. However, in hostile environments where node connectivity is severely compromised, the system performance can be greatly affected. In this work, we consider such a hostile environment where sensor nodes cannot directly communicate to some neighboring nodes. Building on this, we propose a distributed data gathering scheme where data packets are stored in different nodes throughout the network instead to considering a single sink node. As such, if nodes are destroyed or damaged, some information can still be retrieved. To evaluate the performance of the system, we consider the properties of different graphs that describe the connections among nodes. It is shown that the degree distribution of the graph has an important impact on the performance of the system. A teletraffic analysis is developed to study the average buffer size and average packet delay. To this end, we propose a reference node approach, which entails an approximation for the mathematical modeling of these networks that effectively simplifies the analysis and approximates the overall performance of the system.

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“…Although it is not within the scope of this article, it is worth mentioning that the sensors in WSNs due to their wireless communication capabilities are also vulnerable to disruptions of the radio channel. For instance, a DoS jamming attack can be easily executed by an adversary with a low-cost hardware, and ad hoc solutions, such as those presented in [11,12] or [13], are needed for its detection.…”

Section: Introductionmentioning

confidence: 99%

ECG-RNG: A Random Number Generator Based on ECG Signals and Suitable for Securing Wireless Sensor Networks

Cámara

Peris-López

Martín

et al. 2018

Sensors

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Wireless Sensor Networks (WSNs) are a promising technology with applications in many areas such as environment monitoring, agriculture, the military field or health-care, to name but a few. Unfortunately, the wireless connectivity of the sensors opens doors to many security threats, and therefore, cryptographic solutions must be included on-board these devices and preferably in their design phase. In this vein, Random Number Generators (RNGs) play a critical role in security solutions such as authentication protocols or key-generation algorithms. In this article is proposed an avant-garde proposal based on the cardiac signal generator we carry with us (our heart), which can be recorded with medical or even low-cost sensors with wireless connectivity. In particular, for the extraction of random bits, a multi-level decomposition has been performed by wavelet analysis. The proposal has been tested with one of the largest and most publicly available datasets of electrocardiogram signals (202 subjects and 24 h of recording time). Regarding the assessment, the proposed True Random Number Generator (TRNG) has been tested with the most demanding batteries of statistical tests (ENT, DIEHARDERand NIST), and this has been completed with a bias, distinctiveness and performance analysis. From the analysis conducted, it can be concluded that the output stream of our proposed TRNG behaves as a random variable and is suitable for securing WSNs.

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A Statistical Approach to Detect Jamming Attacks in Wireless Sensor Networks

Cited by 114 publications

References 33 publications

Fault-Tolerant Anomaly Detection Method in Wireless Sensor Networks

Fault-Tolerant Anomaly Detection Method in Wireless Sensor Networks

On the Use of Graphs for Node Connectivity in Wireless Sensor Networks for Hostile Environments

ECG-RNG: A Random Number Generator Based on ECG Signals and Suitable for Securing Wireless Sensor Networks

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