DeepWiFi: Cognitive WiFi with Deep Learning

Davaslıoğlu, Kemal; Soltani, Sohraab; Erpek, Tugba; Sagduyu, Yalin E.

doi:10.1109/tmc.2019.2949815

Cited by 63 publications

(25 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning has been studied to secure wireless communications, such as authenticating signals [37]- [39], detecting and classifying jammers of different types [40]- [42], and controlling communications to mitigate jamming effects [32], [40]. As cognitive radio capabilities are integrated into wireless communications, adversaries such as jammers become smarter, as well [40], [43].…”

Section: Related Workmentioning

confidence: 99%

Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing

Shi

Davaslıoğlu

Sagduyu

2021

IEEE Trans. Cogn. Commun. Netw.

Self Cite

View full text Add to dashboard Cite

The spoofing attack is critical to bypass physicallayer signal authentication. This paper presents a deep learningbased spoofing attack to generate synthetic wireless signals that cannot be statistically distinguished from intended transmissions. The adversary is modeled as a pair of a transmitter and a receiver that build the generator and discriminator of the generative adversarial network, respectively, by playing a minimax game over the air. The adversary transmitter trains a deep neural network to generate the best spoofing signals and fool the best defense trained as another deep neural network at the adversary receiver. Each node (defender or adversary) may have multiple transmitter or receiver antennas. Signals are spoofed by jointly capturing waveform, channel, and radio hardware effects that are inherent to wireless signals under attack. Compared with spoofing attacks using random or replayed signals, the proposed attack increases the probability of misclassifying spoofing signals as intended signals for different network topology and mobility patterns. The adversary transmitter can increase the spoofing attack success by using multiple antennas, while the attack success decreases when the defender receiver uses multiple antennas. For practical deployment, the attack implementation on embedded platforms demonstrates the low latency of generating or classifying spoofing signals. Index Terms-Adversarial machine learning, deep learning, generative adversarial network (GAN), spoofing attack.

show abstract

Section: Related Workmentioning

confidence: 99%

Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing

Shi

Davaslıoğlu

Sagduyu

2021

IEEE Trans. Cogn. Commun. Netw.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The number of Gaussian components is set to 3. The posteriori probability of each type of structure landmark can be calculated by using Equation (12). Figure 4 shows the GMM result of LL and LT landmark, in which the X-axis is the V s value, the Y-axis is the V g value and the Z-axis represents the posteriori probability.…”

Section: Gmm-nbc Constructionmentioning

confidence: 99%

A Structure Landmark-Based Radio Signal Mapping Approach for Sustainable Indoor Localization

Liu

Zhang

et al. 2021

Sustainability

View full text Add to dashboard Cite

Low cost and high reproducible is a key issue for sustainable location-based services. Currently, Wi-Fi fingerprinting based indoor positioning technology has been widely used in various applications due to the advantage of existing wireless network infrastructures and high positioning accuracy. However, the collection and construction of signal radio map (a basis for Wi-Fi fingerprinting-based localization) is a labor-intensive and time-cost work, which limit their practical and sustainable use. In this study, an indoor signal mapping approach is proposed, which extracts fingerprints from unknown signal mapping routes to construct the radio map. This approach employs special indoor spatial structures (termed as structure landmarks) to estimate the location of fingerprints extracted from mapping routes. A learning-based classification model is designed to recognize the structure landmarks along a mapping route based on visual and inertial data. A landmark-based map matching algorithm is also developed to attach the recognized landmarks to a map and to recover the location of the mapping route without knowing its initial location. Experiment results showed that the accuracy of landmark recognition model is higher than 90%. The average matching accuracy and location error of signal mapping routes is 96% and 1.2 m, respectively. By using the constructed signal radio map, the indoor localization error of two algorithms can reach an accuracy of 1.6 m.

show abstract

“…While ML has been applied for predicting aspects related to Wi-Fi networks, such as trafic and location prediction [19,20], it has been barely applied for explicitly predicting their performance. In this context, the work in [21] provided an ML-based framework for Wi-Fi operation, which includes the application of Deep Learning (DL) for waveforms classi ication, so that WLAN devices can identify the medium as idle, busy, or jamming. Closer in spirit to our work, [22] proposed an ML-based framework for WLANs' performance prediction.…”

Section: Policies For Dynamic Channel Bondingmentioning

confidence: 99%

Machine learning for performance prediction of channel bonding in next-generation IEEE 802.11 WLANS

Wilhelmi¹,

Góez²,

Soto³

et al. 2021

ITU-J FET

View full text Add to dashboard Cite

With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and standardization organizations are progressing on the definition of mechanisms and procedures to address the increasing complexity of future 5G and beyond communications. In this context, the International Telecommunication Union (ITU) organized the First AI for 5G Challenge to bring industry and academia together to introduce and solve representative problems related to the application of Machine Learning (ML) to networks. In this paper, we present the results gathered from Problem Statement 13 (PS-013), organized by Universitat Pompeu Fabra (UPF), whose primary goal was predicting the performance of next-generation Wireless Local Area Networks (WLANs) applying Channel Bonding (CB) techniques. In particular, we provide an overview of the ML models proposed by participants (including artificial neural networks, graph neural networks, random forest regression, and gradient boosting) and analyze their performance on an open data set generated using the IEEE 802.11ax-oriented Komondor network simulator. The accuracy achieved by the proposed methods demonstrates the suitability of ML for predicting the performance of WLANs. Moreover, we discuss the importance of abstracting WLAN interactions to achieve better results, and we argue that there is certainly room for improvement in throughput prediction through ML.

show abstract

DeepWiFi: Cognitive WiFi with Deep Learning

Cited by 63 publications

References 29 publications

Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing

Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing

A Structure Landmark-Based Radio Signal Mapping Approach for Sustainable Indoor Localization

Machine learning for performance prediction of channel bonding in next-generation IEEE 802.11 WLANS

Contact Info

Product

Resources

About