DDoS attacks in WSNs: detection and countermeasures

Abidoye, Ademola P.; Obagbuwa, Ibidun Christiana

doi:10.1049/iet-wss.2017.0029

Cited by 29 publications

(15 citation statements)

References 16 publications

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“…The authors of References [14,15] illustrated the current state of research review on WSNs in general. In a survey [16] on detection and countermeasures against Distributed Denial of Service (DDoS). attacks in WSNs was presented in detail.…”

Section: Related Workmentioning

confidence: 99%

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AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks

Shahzad

Islam

Kwak

et al. 2019

Sensors

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Static sink-based wireless sensor networks (WSNs) suffer from an energy-hole problem. This incurs as the rate of energy consumption on sensor nodes around sinks and on critical paths is considerably faster. State-of-the-art en-routing filtering schemes save energy by countering false report injection attacks. In addition to their unique limitations, these schemes generally do not examine energy awareness in underlying routing. Mostly, these security methods are based on a fixed filtering capacity, unable to respond to changes in attack intensity. Therefore, these limitations cause network partition(s), exhibiting adverse effects on network lifetime. Extending network lifetime while preserving energy and security thus becomes an interesting challenge. In this article, we address the aforesaid shortcomings with the proposed adaptive en-route filtering (AEF) scheme. In energy-aware routing, the fitness function, which is used to select forwarding nodes, considers residual energy and other factors as opposed to distance only. In pre-deterministic key distribution, keys are distributed based on the consideration of having paths with a different number of verification nodes. This, consequently, permits us to have multiple paths with different security levels that can be exploited to counter different attack intensities. Taken together, the integration of the special fitness function with the new key distribution approach enables the AEF to adapt the underlying dynamic network conditions. The simulation experiments under different settings show significant improvements in network lifetime.

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Section: Related Workmentioning

confidence: 99%

“…A number of research works [14,15,16,17,18,19,20] emphasize attacks and countermeasures. In the past, energy-hole problem mitigation was discussed in Reference [21].…”

Section: Introductionmentioning

confidence: 99%

AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks

Shahzad

Islam

Kwak

et al. 2019

Sensors

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“…Contrary to researching the problem of detecting and managing the DDoS traffic that such devices can generate are very rare (especially in the smart home environment), Table 1 shows examples of the detection of DDoS traffic in the IoT environment. There have been researches on protection the IoT environment from DDoS attacks as shown in [23] and [24].…”

Section: Detection Of Iot Generated Ddos Trafficmentioning

confidence: 99%

Smart Home IoT Traffic Characteristics as a Basis for DDoS Traffic Detection

Cvitić¹,

Peraković²,

Periša³

et al. 2018

Proceedings of the 3rd EAI International Conference on Management of Manufacturing Systems

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Distributed denial of Service (DDoS) attack is a continuous threat to the availability of information and communication resources. The development and growth of acceptance and the continuous increase in the number of devices within the IoT concept provides the platform for the implementation of DDoS attacks of significantly greater traffic intensity than is currently possible. Numerous botnet networks, where the most prominent representative is Mirai botnet, use the inadequate protection of IoT devices in the smart home environment for generating illegitimate DDoS traffic. To further development of the timely DDoS traffic detection generated in the aforementioned environment, this research seeks to establish the diversity of traffic generated by IoT devices in a smart home environment with respect to the traffic generated through human type communication. Research results will represent base for the future development of new models aimed at detecting this specific DDoS traffic type.

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“…Since then, the topic of DDoS has been an intense field of research, where new approaches for detection, mitigation, prevention or defense have been proposed, following the evolution of DDoS attack mechanisms (see e.g. surveys [10,16,[18][19][20][21][22][23]) and/or new application domains such as mobile/wireless [24,25], Internet of Things [26], Software-defined networks (SDN) [27,28], cloud [21,22] or fog computing [29,30]. Nevertheless, DDoS attacks have continued to increase and to be successful causing service interruptions that lead to huge financial losses, as was the case of the Dyn attack which took place on October 21, 2016 and was initiated by a DNS operator bringing down major websites including PayPal, Amazon, Airbnb, Visa, The New York Times, Netflix, GitHub, Reddit, Twitter, Spotify, the Guardian, and CNN [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Detection of reduction-of-quality DDoS attacks using Fuzzy Logic and machine learning algorithms

et al. 2021

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Distributed Denial of Service (DDoS) attacks are still among the most dangerous attacks on the Internet. With the advance of methods for detecting and mitigating these attacks, crackers have improved their skills in creating new DDoS attack types with the aim of mimicking normal traffic behaviour therefore becoming silently powerful. Among these advanced DDoS attack types, the so-called lowrate DoS attacks aim at keeping a low level of network traffic. In this paper, we study one of these techniques, called Reduction of Quality (RoQ) attack. To investigate the detection of this type of attack, we evaluate and compare the use of four machine learning algorithms: Multi-Layer Perceptron (MLP) neural network with backpropagation, K-Nearest Neighbors (K-NN), Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB). We also propose an approach for detecting this kind of attack based on three methods: Fuzzy Logic (FL), MLP and Euclidean Distance (ED). We evaluate and compare the approach based on FL, MLP and ED to the above machine learning algorithms using both emulated and real traffic traces. We show that among the four Machine Learning algorithms, the best classification results are obtained with MLP, which, for emulated traffic, leads to a F1-score of 98.04% for attack traffic and 99.30% for legitimate traffic, while, for real traffic, it leads to a F1-score of 99.87% for attack traffic and 99.95% for legitimate traffic. Regarding the approach using FL, MLP and EC, for classification of emulated traffic, we obtained a F1-score of 98.80% for attack traffic and 99.60% for legitimate traffic, while, for real traffic, we obtained a F1-score of 100% for attack traffic and 100% for legitimate traffic. However, the better performance of the approach based on FL, MLP and ED is obtained at the cost of larger execution time, since MLP required 0.74 ms and 0.87ms for classification of the emulated and real traffic datasets, respectively, where as the approach using FL, MLP and ED required 11'46" and 46'48" to classify the emulated and real traffic datasets, respectively.

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DDoS attacks in WSNs: detection and countermeasures

Cited by 29 publications

References 16 publications

AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks

AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks

Smart Home IoT Traffic Characteristics as a Basis for DDoS Traffic Detection

Detection of reduction-of-quality DDoS attacks using Fuzzy Logic and machine learning algorithms

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