Simulating and imitating RF communications signals and systems is a core function of jammers, spoofers, and other attacks in wireless radio environments which seek to confuse spectrum users as to what is occurring in the spectrum around them. Replay attacks and "DRFMs" have long been commonly used to deceive and probe radio systems, however generative models introduce an interesting new angle wherein generative replay can now produce examples of signals of similar structure and properties to arbitrary signals which are not verbatim replays and which may be varied in an infinite number of ways. Further, as GANs have demonstrated a strong ability to learn distributions from complex scenes and datasets, we consider the task of full-band spectral generation in addition to single signal generation to validate and demonstrate the feasibility of such an approach, to refine the algorithmic approach, and to quantify and illustrate the capabilities of such an approach on modern day signal sets.
{"title":"Generative Adversarial Radio Spectrum Networks","authors":"Tamoghna Roy, Tim O'Shea, Nathan E. West","doi":"10.1145/3324921.3328782","DOIUrl":"https://doi.org/10.1145/3324921.3328782","url":null,"abstract":"Simulating and imitating RF communications signals and systems is a core function of jammers, spoofers, and other attacks in wireless radio environments which seek to confuse spectrum users as to what is occurring in the spectrum around them. Replay attacks and \"DRFMs\" have long been commonly used to deceive and probe radio systems, however generative models introduce an interesting new angle wherein generative replay can now produce examples of signals of similar structure and properties to arbitrary signals which are not verbatim replays and which may be varied in an infinite number of ways. Further, as GANs have demonstrated a strong ability to learn distributions from complex scenes and datasets, we consider the task of full-band spectral generation in addition to single signal generation to validate and demonstrate the feasibility of such an approach, to refine the algorithmic approach, and to quantify and illustrate the capabilities of such an approach on modern day signal sets.","PeriodicalId":435733,"journal":{"name":"Proceedings of the ACM Workshop on Wireless Security and Machine Learning","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130432007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intrusion detection systems (IDSs) today face key limitations with respect to detection and prevention of challenging IoT-empowered attacks. We address these limitations by proposing a novel IDS called RAPID, which is based on an online scalable anomaly detection and localization approach. We show that the anomaly detection algorithm is asymptotically optimal under certain conditions, and comprehensively analyze its computational complexity. Considering a real dataset and an IoT testbed we demonstrate the use of RAPID in two different IoT-empowered cyber-attack scenarios, namely high-rate DDoS attacks and low-rate DDoS attacks. The experiment results show the quick and accurate detection and prevention performance of the proposed IDS.
{"title":"RAPID: Real-time Anomaly-based Preventive Intrusion Detection","authors":"Keval Doshi, Mahsa Mozaffari, Y. Yilmaz","doi":"10.1145/3324921.3328789","DOIUrl":"https://doi.org/10.1145/3324921.3328789","url":null,"abstract":"Intrusion detection systems (IDSs) today face key limitations with respect to detection and prevention of challenging IoT-empowered attacks. We address these limitations by proposing a novel IDS called RAPID, which is based on an online scalable anomaly detection and localization approach. We show that the anomaly detection algorithm is asymptotically optimal under certain conditions, and comprehensively analyze its computational complexity. Considering a real dataset and an IoT testbed we demonstrate the use of RAPID in two different IoT-empowered cyber-attack scenarios, namely high-rate DDoS attacks and low-rate DDoS attacks. The experiment results show the quick and accurate detection and prevention performance of the proposed IDS.","PeriodicalId":435733,"journal":{"name":"Proceedings of the ACM Workshop on Wireless Security and Machine Learning","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114398489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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. For that purpose, various waveform, channel, and radio hardware features that are inherent to original wireless signals need to be captured. In the meantime, adversaries become sophisticated with the cognitive radio capability to record, analyze, and manipulate signals before spoofing. Building upon deep learning techniques, this paper introduces a spoofing attack by an adversary pair of a transmitter and a receiver that assume the generator and discriminator roles in the GAN and play a minimax game to generate the best spoofing signals that aim to fool the best trained defense mechanism. The output of this approach is two-fold. From the attacker point of view, a deep learning-based spoofing mechanism is trained to potentially fool a defense mechanism such as RF ingerprinting. From the defender point of view, a deep learning-based defense mechanism is trained against potential spooing attacks when an adversary pair of a transmitter and a receiver cooperates. The probability that the spooing signal is misclassified as the intended signal is measured for random signal, replay, and GAN-based spoofing attacks. Results show that the GAN-based spooing attack provides a major increase in the success probability of wireless signal spoofing even when a deep learning classifier is used as the defense.
{"title":"Generative Adversarial Network for Wireless Signal Spoofing","authors":"Yi Shi, Kemal Davaslioglu, Y. Sagduyu","doi":"10.1145/3324921.3329695","DOIUrl":"https://doi.org/10.1145/3324921.3329695","url":null,"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. For that purpose, various waveform, channel, and radio hardware features that are inherent to original wireless signals need to be captured. In the meantime, adversaries become sophisticated with the cognitive radio capability to record, analyze, and manipulate signals before spoofing. Building upon deep learning techniques, this paper introduces a spoofing attack by an adversary pair of a transmitter and a receiver that assume the generator and discriminator roles in the GAN and play a minimax game to generate the best spoofing signals that aim to fool the best trained defense mechanism. The output of this approach is two-fold. From the attacker point of view, a deep learning-based spoofing mechanism is trained to potentially fool a defense mechanism such as RF ingerprinting. From the defender point of view, a deep learning-based defense mechanism is trained against potential spooing attacks when an adversary pair of a transmitter and a receiver cooperates. The probability that the spooing signal is misclassified as the intended signal is measured for random signal, replay, and GAN-based spoofing attacks. Results show that the GAN-based spooing attack provides a major increase in the success probability of wireless signal spoofing even when a deep learning classifier is used as the defense.","PeriodicalId":435733,"journal":{"name":"Proceedings of the ACM Workshop on Wireless Security and Machine Learning","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124023259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the ACM Workshop on Wireless Security and Machine Learning","authors":"Acm Sigmobile","doi":"10.1145/3324921","DOIUrl":"https://doi.org/10.1145/3324921","url":null,"abstract":"","PeriodicalId":435733,"journal":{"name":"Proceedings of the ACM Workshop on Wireless Security and Machine Learning","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127408817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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