Reinforcement learning based anti-jamming with wideband autonomous cognitive radios

Machuzak, Stephen; Jayaweera, Sudharman K.

doi:10.1109/iccchina.2016.7636793

Cited by 52 publications

(54 citation statements)

References 10 publications

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“…Along the same direction, two previous studies [212,213] proposed RL-based anti-jamming schemes for WACRs. In [212], the authors used information about sweeping jammer signal and unintentional interference to distinguish it from those of other WACRs.…”

Section: Edlnsmentioning

confidence: 93%

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A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

838

333

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The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems.IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

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Section: Edlnsmentioning

confidence: 93%

“…However, sensing all required frequencies in real time is a challenging task, specifically with the existence of jamming attacks. CRs can become increasingly useful and reliable communication systems if they can eliminate the incidence of accidental interference or deliberate jamming attacks [213].…”

Section: Edlnsmentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

838

333

View full text Add to dashboard Cite

show abstract

“…Fortunately, many approaches have also been proposed to learn how to act in an unknown communication environment. The classical theory of reinforcement learning (RL), in which an agent learns and adapts its strategy by using the feedback of its actions that have been used in the past, has received much attention. Specifically, this theory learns an optimal strategy by repeatedly interacting with the environment.…”

Section: Introductionmentioning

confidence: 99%

Apprenticeship learning in cognitive jamming

ZhuanSun

Yang

Hui

2019

Optim Control Appl Methods

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In an electronic warfare-type scenario with a partially known environment, the advantages of trial and error in the reinforcement learning algorithm adopted by a cognitive jammer can disrupt communications between transmitter-receiver pairs adaptively and optimally. However, complete information regarding the Markov decision process and its thousands of interactions seem to be unreliable, especially in applications that require timeliness, such as cognitive jamming.Faced with uncooperative or hostile transmitter-receiver pairs, it is a challenging control problem for the jammer to select the proper actions to realize communication denials. This paper addresses apprenticeship learning in cognitive jamming, where inverse reinforcement learning is used by the jammer to acquire skilled behaviors from expert demonstrations. Without a known prior reward function, the jammer can obtain high jamming performance under the guidance of the given expert strategy. More specifically, the number of iterations needed in apprenticeship learning is much less than that needed in Q learning, which is a vital advantage in a fast-changing environment. Numerous results demonstrate that it is feasible and realistic to use apprenticeship learning to learn the jamming strategy since its performance meets or exceeds those of existing methods. KEYWORDScognitive jamming, inverse reinforcement learning, jamming strategy, Markov decision process, reinforcement learning Optim Control Appl Meth. 2019;40:647-658.wileyonlinelibrary.com/journal/oca

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“…To address this problem, some artificial intelligence technologies, such as reinforcement learning technology and deep learning technology [6,7], have also been widely applied for wireless communications, due to its dynamic adaptability on anti-jamming [8]. One of the most commonly used method in AI fields is the reinforcement learning method, which has been utilized to analyze the jamming policy [9], and make anti-jamming decision [10]. So that users adaptively adjust actions according to the jamming policy and establish secure communication.…”

Section: Introductionmentioning

confidence: 99%

“…In [13], a cross-layer resource allocation approach based on Q learning was proposed to effectively utilize unused spectrum opportunities. In [10], an anti-jamming Q learning algorithm was proposed to avoid sweeping jamming.…”

Section: Introductionmentioning

confidence: 99%

Multi-regional Anti-jamming Communication Scheme Based on Transfer Learning and Q Learning

Niu¹

2019

KSII TIIS

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The smart jammer launches jamming attacks which degrade the transmission reliability. In this paper, smart jamming attacks based on the communication probability over different channels is considered, and an anti-jamming Q learning algorithm (AQLA) is developed to obtain anti-jamming knowledge for the local region. To accelerate the learning process across multiple regions, a multi-regional intelligent anti-jamming learning algorithm (MIALA) which utilizes transferred knowledge from neighboring regions is proposed. The MIALA algorithm is evaluated through simulations, and the results show that the it is capable of learning the jamming rules and effectively speed up the learning rate of the whole communication region when the jamming rules are similar in the neighboring regions. . His research interests include learning theory, satellite communication, and communication anti-jamming technology. Yingtao Niu received his M.S. degree from PLA Commanding Communication Academy, China, in 2005, and received his Ph.D. degree from Institute of Communication Engineering, PLA University of Science and Technology Institute, China. He has authored more than 30 journal and conference papers. His main research interests are spread-spectrum communication, cognitive radio theory and techniques, with particular emphasis on algorithms of wireless communication signal processing and decision-making in cognitive radio systems.

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Reinforcement learning based anti-jamming with wideband autonomous cognitive radios

Cited by 52 publications

References 10 publications

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Apprenticeship learning in cognitive jamming

Multi-regional Anti-jamming Communication Scheme Based on Transfer Learning and Q Learning

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