Deep Reinforcement Learning based reliable spectrum sensing under SSDF attacks in Cognitive Radio networks

Paul, Anal; Mishra, Aneesh Kumar; Shreevastava, Shivam

doi:10.1016/j.jnca.2022.103454

Cited by 11 publications

(5 citation statements)

References 33 publications

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“…The need to learn complex and noisy spectrum data for tasks that require long sequences with longterm dependencies. [104,106,[108][109][110] DQN It allows the system to make more accurate decisions in dynamic environments and improves the robustness and accuracy of sensing models.…”

Section: Performance Comparison Of Conventional Methods and Deep-lear...mentioning

confidence: 99%

“…Addressing the potential danger of malicious SUs sending forged data to data fusion centers to jam CSS systems, Anal Paul et al [109] tried to use agent intelligence in DRL to be able to effectively avoid the fake data sent to the FC. The proposed model is also based on a deep Q-network, which is made robust to fake data attacks by the experience replay (ER) algorithm.…”

Section: Applications Of Deep Reinforcement Learning To Cooperative S...mentioning

confidence: 99%

“…For example, in [114], in order to defend against primary user adversarial attacks (PUAAs), it is proposed to apply self-encoders in LSTM and CNN to extract temporal and local features for effective defense. Another example is the above-mentioned work [109], that bypasses malicious data in FC by using agents in deep reinforcement learning, which can effectively improve the error caused by SSDF in sensing. However, due to the variety of malicious attacks and gaps in application practice, future research and the selection of appropriate strategies or new techniques based on specific application scenarios and threat models in the spectrum-sensing domain are needed by scholars.…”

Section: Investigating the Robustness Of Sensing Systems Under Variou...mentioning

confidence: 99%

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A Review of Research on Spectrum Sensing Based on Deep Learning

Zhang,

Luo

2023

Electronics

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In recent years, with the rapid development in wireless communication and 5G networks, the rapid growth in mobile users has been accompanied by an increasing demand for the electromagnetic spectrum. The birth of cognitive radio and its spectrum-sensing technology provides hope for solving the problem of low utilization of the wireless spectrum. Artificial intelligence (AI) has been widely discussed globally. Deep learning technology, known for its strong learning ability and adaptability, plays a significant role in this field. Moreover, integrating deep learning with wireless communication technology has become a prominent research direction in recent years. The research objective of this paper is to summarize the algorithm of cognitive radio spectrum-sensing technology combined with deep learning technology. To review the advantages of deep-learning-based spectrum-sensing algorithms, this paper first introduces the traditional spectrum-sensing methods. It summarizes and compares the advantages and disadvantages of each method. It then describes the application of deep learning algorithms in spectrum sensing and focuses on the typical deep-neural-network-based sensing methods. Then, the existing deep-learning-based cooperative spectrum-sensing methods are summarized. Finally, the deep learning spectrum-sensing methods are discussed, along with challenges in the field and future research directions.

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Section: Performance Comparison Of Conventional Methods and Deep-lear...mentioning

confidence: 99%

Section: Applications Of Deep Reinforcement Learning To Cooperative S...mentioning

confidence: 99%

Section: Investigating the Robustness Of Sensing Systems Under Variou...mentioning

confidence: 99%

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A Review of Research on Spectrum Sensing Based on Deep Learning

Zhang,

Luo

2023

Electronics

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“…Similarly, Ghaznavi and Jamshidi [21] proposed a reliable method based on clustering the cooperating sensors, which significantly improves the performance of cognitive radio networks with attackers. Paul et al [22] proposed a CSS model based on deep Q-learning, which outperforms the widely popular SVM-based classification methods and traditional CSS methods under SSDF attacks. Yang and Tong [23] proposed a spectrum sensing algorithm using an SVM-optimized RBF neural network, and the results show that the detection performance of spectrum sensing can be further improved by an ML-optimized RBF neural network algorithm, which opens up a new direction for the application of ML and neural network.…”

Section: Introductionmentioning

confidence: 99%

CSL-SFNet for Cooperative Spectrum Sensing in Cognitive Satellite Network with GEO and LEO Satellites

Yang,

Hu,

Zhang

et al. 2024

IET Signal Processing

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In a cognitive satellite network (CSN) with GEO and LEO satellites, there is a large propagation losses between the sensing satellite and the ground station. The results of spectrum sensing from a single satellite may be inaccurate, which will create serious interference in the primary satellite system. Cooperative spectrum sensing (CSS) has become the key technology for solving the above problems in recent years. However, most of the current CSS techniques are model-driven. They are difficult to model and implement in CSNs since their detection performance is strongly dependent on an assumed statistical model. Thus, we propose a novel CSS scheme, which uses convolutional neural networks (CNNs), self-attention (SA) modules, long short-term memory networks (LSTMs), and soft fusion networks, called CSL-SFNet. This scheme combines the advantages of CNNs, SA modules, and LSTMs to extract the features of the input signals from the spatial and temporal domains. Additionally, the CSL-SFNet makes use of a novel soft fusion technique that improves detection performance while also considerably reducing communication overhead. The simulation results demonstrate that the proposed algorithm can achieve a detection probability of 90% when the signal-to-noise ratio is −20 dB; it has a shorter running time and always outperforms the other CSS algorithms.

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“…In reference 17, a hybrid boosted tree algorithm‐based solution was proposed to suppress malicious user in a CSS system. A deep Q‐learning based CSS model was proposed to provide reliable sensing results under SSDF attack in reference 18. A hierarchical cat and mouse learning machine was proposed to minimize Byzantine attack in reference 19.…”

Section: Introductionmentioning

confidence: 99%

Less sample‐cooperative spectrum sensing against large‐scale Byzantine attack in cognitive wireless sensor networks

Wu,

Dai,

Teng

et al. 2024

Trans Emerging Tel Tech

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Cooperative spectrum sensing (CSS) has emerged as a promising strategy for identifying available spectrum resources by leveraging spatially distributed sensors in cognitive wireless sensor networks (CWSNs). Nevertheless, this open collaborative approach is susceptible to security threats posed by malicious sensors, specifically Byzantine attack, which can significantly undermine CSS accuracy. Moreover, in extensive CWSNs, the CSS process imposes substantial communication overhead on the reporting channel, thereby considerably diminishing cooperative efficiency. To tackle these challenges, this article introduces a refined CSS approach, termed weighted sequential detection (WSD). This method incorporates channel state information to validate the global decision made by the fusion center and assess the trust value of sensors. The trust value based weight is assigned to sensing samples, which are then integrated into a sequential detection framework within a defined time window. This sequential approach prioritizes samples based on descending trust values. Numerical simulation results reveal that the proposed WSD outperforms conventional fusion rules in terms of the error probability, sample size, achievable throughput, and latency, even under varying degrees of Byzantine attack. This innovation signifies a substantial advancement in enhancing the reliability and efficiency of CSS.

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Deep Reinforcement Learning based reliable spectrum sensing under SSDF attacks in Cognitive Radio networks

Cited by 11 publications

References 33 publications

A Review of Research on Spectrum Sensing Based on Deep Learning

A Review of Research on Spectrum Sensing Based on Deep Learning

CSL-SFNet for Cooperative Spectrum Sensing in Cognitive Satellite Network with GEO and LEO Satellites

Less sample‐cooperative spectrum sensing against large‐scale Byzantine attack in cognitive wireless sensor networks

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