Adversarial Machine Learning for 5G Communications Security

Sagduyu, Yalin E.; Erpek, Tugba; Shi, Yi

doi:10.1002/9781119723950.ch14

Cited by 42 publications

(11 citation statements)

References 54 publications

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“…Different types of attacks built upon adversarial machine learning have been studied in wireless communications [21], [22] such as exploratory (inference) attacks [23], [24], evasion (adversarial) attacks [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39] and their extensions to secure and covert communications against eavesdroppers [40], [41], [42], causative (poisoning) attacks [43], [44], [45], membership inference attacks [46], [47], Trojan attacks [48], and spoofing attacks [49], [50], [51] that have been launched against various spectrum sensors and wireless signal (such as modulation) classifiers. Adversarial machine learning has also been considered for NextG by studying evasion and spoofing attacks on deep neural networks (without reinforcement learning) used for NextG spectrum sharing and NextG signal authentication [52]. In addition, flooding attacks have been considered for NextG network slicing with reinforcement learning [53].…”

Section: B Adversarial Machine Learning Based Attack On Nextg Radio A...mentioning

confidence: 99%

How to Attack and Defend NextG Radio Access Network Slicing With Reinforcement Learning

Shi

Sagduyu

Erpek

et al. 2023

IEEE Open J. Veh. Technol.

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In this paper, reinforcement learning (RL) for network slicing is considered in next generation (NextG) radio access networks, where the base station (gNodeB) allocates resource blocks (RBs) to the requests of user equipments and aims to maximize the total reward of accepted requests over time. Based on adversarial machine learning, a novel over-the-air attack is introduced to manipulate the RL algorithm and disrupt NextG network slicing. The adversary observes the spectrum and builds its own RL based surrogate model that selects which RBs to jam subject to an energy budget with the objective of maximizing the number of failed requests due to jammed RBs. By jamming the RBs, the adversary reduces the RL algorithm's reward. As this reward is used as the input to update the RL algorithm, the performance does not recover even after the adversary stops jamming. This attack is evaluated in terms of both the recovery time and the (maximum and total) reward loss, and it is shown to be much more effective than benchmark (random and myopic) jamming attacks. Different reactive and proactive defense schemes such as suspending the RL algorithm's update once an attack is detected, introducing randomness to the decision process in RL to mislead the learning process of the adversary, or manipulating the feedback (NACK) mechanism such that the adversary may not obtain reliable information are introduced to show that it is viable to defend NextG network slicing against this attack, in terms of improving the RL algorithm's reward.

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Section: B Adversarial Machine Learning Based Attack On Nextg Radio A...mentioning

confidence: 99%

How to Attack and Defend NextG Radio Access Network Slicing With Reinforcement Learning

Shi

Sagduyu

Erpek

et al. 2023

IEEE Open J. Veh. Technol.

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“…In particular, cognitive radio capabilities empowered by machine learning allow for performing spectrum aware-ness and spectrum sharing. For example, in study [32], an AI/ML model at an environmental sensing capability (ESC) station detects citizens broadband radio service (CBRS) as an incumbent user. If the incumbent user is not detected in a channel of interest, the ESC allows a gNB to communicate to UEs.…”

Section: Adversarial ML In 5g a Ai/ml In 5gmentioning

confidence: 99%

“…In [32], the adversary aims to compromise the integrity of the target AI/ML model deployed for intelligent spectrum sharing during the sensing periods to force the ESC into making wrong transmit decisions. In particular, the adversary attempts to fool the ESC to allow the gNB to transmit when an incumbent user is present, and vice versa, to fool the ESC to stop the gNB transmissions even though there are no CBRS users.…”

Section: B Adversarial Examples In 5gmentioning

confidence: 99%

“…Furthermore, the adversary most of the time cannot directly obtain the output label of the target model, since it is used internally by the model and it is not available to any other wireless node outside of the network. Finally, the adversary is not able to directly manipulate the input data to the target model, it can only add its own transmissions on top of existing transmissions over the air to change the input data indirectly [32].…”

Section: Introductionmentioning

confidence: 99%

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On Assessing Vulnerabilities of the 5G Networks to Adversarial Examples

Zolotukhin

Miraghaie

Zhang³

et al. 2022

IEEE Access

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The use of artificial intelligence and machine learning is recognized as the key enabler for 5G mobile networks which would allow service providers to tackle the network complexity and ensure security, reliability and allocation of the necessary resources to their customers in a dynamic, robust and trustworthy way. Dependability of the future generation networks on accurate and timely performance of its artificial intelligence components means that disturbance in the functionality of these components may have negative impact on the entire network. As a result, there is an increasing concern about the vulnerability of intelligent machine learning driven frameworks to adversarial effects. In this study, we evaluate various adversarial example generation attacks against multiple artificial intelligence and machine learning models which can potentially be deployed in future 5G networks. First, we describe multiple use cases for which attacks on machine learning components are conceivable including the models employed and the data used for their training. After that, attack algorithms, their implementations and adjustments to the target models are summarised. Finally, the attacks implemented for the aforementioned use cases are evaluated based on deterioration of the objective functions optimised by the target models.

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“…Overall, AML is an emerging field that studies machine learning (ML) in the presence of adversaries that may aim to manipulate the test and/or training pipelines of ML algorithms [ 7 , 8 , 9 ]. While the applications of AML have originated in the computer vision domain, there has been a growing interest in applying AML to wireless communications [ 10 , 11 , 12 ], including exploratory (inference) attacks [ 13 , 14 ], evasion (adversarial) attacks [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ] and their extensions to secure and covert communications against eavesdroppers [ 34 , 35 , 36 , 37 ], causative (poisoning) attacks [ 38 , 39 , 40 ], membership inference attacks [ 41 , 42 ], Trojan attacks [ 43 ], and spoofing attacks [ 44 , 45 , 46 , 47 ].…”

Section: Introductionmentioning

confidence: 99%

Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas

Kim

Sagduyu

Davaslıoğlu

et al. 2022

Entropy

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This paper studies the privacy of wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect transmissions of interest. There exists one transmitter that transmits to its receiver in the presence of an eavesdropper. In the meantime, a cooperative jammer (CJ) with multiple antennas transmits carefully crafted adversarial perturbations over the air to fool the eavesdropper into classifying the received superposition of signals as noise. While generating the adversarial perturbation at the CJ, multiple antennas are utilized to improve the attack performance in terms of fooling the eavesdropper. Two main points are considered while exploiting the multiple antennas at the adversary, namely the power allocation among antennas and the utilization of channel diversity. To limit the impact on the bit error rate (BER) at the receiver, the CJ puts an upper bound on the strength of the perturbation signal. Performance results show that this adversarial perturbation causes the eavesdropper to misclassify the received signals as noise with a high probability while increasing the BER at the legitimate receiver only slightly. Furthermore, the adversarial perturbation is shown to become more effective when multiple antennas are utilized.

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Adversarial Machine Learning for 5G Communications Security

Cited by 42 publications

References 54 publications

How to Attack and Defend NextG Radio Access Network Slicing With Reinforcement Learning

How to Attack and Defend NextG Radio Access Network Slicing With Reinforcement Learning

On Assessing Vulnerabilities of the 5G Networks to Adversarial Examples

Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas

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