Generative Adversarial Network for Wireless Signal Spoofing

Proceedings of the ACM Workshop on Wireless Security and Machine Learning, pp.55-60, (2019)

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Abstract

The paper presents a novel approach of spoofing wireless signals by using a general adversarial network (GAN) to generate and transmit synthetic signals that cannot be reliably distinguished from intended signals. It is of paramount importance to authenticate wireless signals at the PHY layer before they proceed through the receiver chain...More

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Introduction
  • Wireless communications is susceptible to adversaries due to the open and shared nature of wireless medium.
  • The spoofing attack is launched by an adversary that aims to mimic a legitimate user in its transmissions.
  • One common approach for wireless signal spoofing is recording a legitimate user’s transmission and replaying the signal later by potentially adjusting the transmit power.
  • While this approach can represent various features in the signal at a high level, it may fall short of reliably mimicking combined waveform, channel, and device effects.
  • In this context, machine learning provides automated means to authenticate signals by analyzing wireless signals and identifying anomalies.
  • Enabled by recent advances in computational resources, deep learning can effectively process raw spectrum data and operate on latent representations, while analyzing high-dimensional spectrum dynamics that feature-based machine learning algorithms fail to achieve
Highlights
  • Wireless communications is susceptible to adversaries due to the open and shared nature of wireless medium
  • We show that the success probability increases to 36.2% against a defender that uses a deep learning classifier
  • We find that the same classifier pre-trained at R, which works very well to discriminate signals from T from random or replayed signals, cannot successfully identify synthetic signals generated by the general adversarial network (GAN)
  • We designed a GAN-based spoofing attack that generates synthetic data that is transmitted by an adversary transmitter and distinguishes real and synthetic data at an adversary receiver
  • The signals generated by the GAN generator are transmitted for spoofing attack
  • The GAN-based spoofing attack provides a major improvement in attack success probability over the random signal and replay attacks even when the node locations change from training to test time
Methods
  • Method of spoofing attack

    Random signal Replay (Amplify and forward)

    GAN-based spoofing

    Success probability

    AT ’s location (0, 10) (0, 11) (0, 15) (0, 20)

    GAN is run only for 478 epochs in this simulation setting.
  • The success probability of spoofing attack is increased to 76.2%, when the GAN is used by the adversary.
  • AT may request the collaboration of AR to retrain a GAN and use its updated generator for spoofing attack.
  • If such update is not available, AT can still use its current generator to launch the attack.
  • The authors can see that as AT moves away from T , the attack success probability decreases.
  • This is an expected result since the distribution of the received channel characteristics varies as the receiver moves to a different location.
  • The attack success probability is still significantly higher than the one achieved by the replay attack
Results
  • The authors show that the success probability increases to 36.2% against a defender that uses a deep learning classifier.
  • This probability is still much less than 50%, i.e., most of replay-based spoofing attacks based on amplifying and forwarding signals are still not successful.
  • The success probability of spoofing attack increases to 76.2%, when the GAN-based approach is used.
  • The authors check the maximum perturbation in G and D loss functions over the most recent 100 epochs of GAN training.
  • The success probability of spoofing attack is increased to 76.2%, when the GAN is used by the adversary
Conclusion
  • The authors designed a novel approach of spoofing wireless signals by generating synthetic signals by the GAN.
  • The authors considered the case that an adversary transmits synthetic signals such that they are misclassified as the intended ones.
  • The authors first showed that if there is no attack, a pre-trained deep learning-based classifier can distinguish signals reliably.
  • The authors considered a simple spoofing mechanism such as the replay attack that can only keep some pattern of intended signals.
  • The success probability of replay attack against a deep learning-based classifier remains limited.
  • The authors designed a GAN-based spoofing attack that generates synthetic data that is transmitted by an adversary transmitter and distinguishes real and synthetic data at an adversary receiver.
  • The GAN-based spoofing attack provides a major improvement in attack success probability over the random signal and replay attacks even when the node locations change from training to test time.
  • As the GAN opens them new opportunities to effectively spoof wireless signals, new defense mechanisms are called for as future work
Tables
  • Table1: Success probability of spoofing attack by different methods
  • Table2: The impact of AT ’s mobility on success probability of spoofing attack
Download tables as Excel
Related work
  • There are different types of attacks on wireless communications in the literature [13]. In particular, attacks on spectrum sensing include spectrum sensing data falsification (SSDF) attack [14, 15], primary user emulation (PUE) attack [16], eavesdropping [17], and noncooperation [18]. Attacks on data transmission include jamming [19] in form of a denial-of-service (DoS) attack [20] with different levels of prior information [21]. There are also attacks on higher layers, e.g., attacks on routing in the network layer [22] and network flow inference attacks [23]. Defense methods were developed to address these attacks. For example, an adaptive, jamming-resistant spectrum access protocol was proposed in [24] for cognitive radio ad hoc networks, where there are multiple channels that the secondary users can utilize. Jamming games between a cognitive user and a smart jammer was considered in [25], where they individually determine their transmit powers.
Funding
  • This effort is supported by the U.S Army Research Office under contract W911NF-17-C-0090
Study subjects and analysis
samples: 1000
In the simulation setting, the location of T is (0, 0), the location of R is (10, 0), the location of AT is (0, 10), the location of AR is (10, 0.1) (see Figs. 2 and 3), and the normalized transmit power at B is 1000. R collects 1000 samples, each with 400 spectrum sensing results, and a label (‘T ’ or ‘not T ’) as training data and applies the classifier on another 1000 samples to evaluate accuracy. We used the above deep neural network and tuned its parameters

signal samples: 500
We now consider the spoofing attack based on the GAN [11]. As the first step, AR collects 500 signal samples from T and 500 signal samples from AT , where AT can flag its transmissions such that AR can have ground truth. Each sample has coded data of 8 bits under the QPSK modulation, i.e., 4 signals

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