An Emergent Self-Awareness Module for Physical Layer Security in Cognitive UAV Radios

Krayani, Ali; Alam, Atm Shafiul; Nallanathan, Arumugam; Regazzoni, Carlo S.

doi:10.1109/tccn.2022.3161937

Cited by 11 publications

(3 citation statements)

References 39 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their approach focused on minimizing the impact of adversaries and maintaining network performance [24]. Krayani et al (2022) presented an emergent self-awareness module for physical layer security in cognitive UAV radios. Their approach improved the safety of UAV (Unmanned Aerial Vehicle) communications in cognitive radio networks [25].…”

Section: Literature Surveymentioning

confidence: 99%

“…Krayani et al (2022) presented an emergent self-awareness module for physical layer security in cognitive UAV radios. Their approach improved the safety of UAV (Unmanned Aerial Vehicle) communications in cognitive radio networks [25]. Ayanoglu et al (2022) explored the application of generative adversarial networks (GANs) for machine learning in next-generation (NextG) networks.…”

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

BDDTPA: Blockchain-Driven Deep Traffic Pattern Analysis for Enhanced Security in Cognitive Radio Ad-Hoc Networks

Dansana,

Behera,

Darem

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Cognitive Radio Ad-hoc Networks (CRAHNs) combines characteristics of ad-hoc networks with cognitive radios to facilitate a variety of communication scenarios. However, these networks are subject to persistent attacks from internal and external adversaries, such as Masquerading, Spoofing, Spying, and Distributed Denial of Service (DDoS). Existing deep learning models proposed to counter these attacks suffer from complexity, real-time processing limitations, and a lack of network scalability. In addition, their limited IP tracing capabilities make them unsuitable for real-time deployments. To address these issues, this paper proposes a novel blockchain-based model for deep traffic pattern analysis and mitigation of malicious attacks in CRAHNs. The proposed model employs a multi-step methodology. The collected traffic patterns are then used to train a deep Convolutional Neural Network (dCNN) model. This trained model permits the temporal classification of incoming real-time traffic packets. The packets are stored on a distributed network based on blockchain technology to ensure data integrity, transparency, and traceability. This blockchain implementation renders the packets immutable and easily accessible, while also facilitating the recommendation of improved mitigation strategies. The blockchain employs a Proof-of-Trust (PoT) consensus mechanism and is managed via a Genetic Algorithm (GA)-based sidechaining model, which significantly reduces access and writing delays for various packets. The proposed model achieves a significant reduction in communication delay in comparison to existing models, with decreases of 35.4%, 19.5%, and 23.5% for BIDS, BIST WM, and GAN, respectively. Additionally, energy consumption is reduced by 18.5%, 18.3%, and 24.5%, respectively. In addition, the model exhibits an increase in throughput of 14.5%, 16.4%, and 15.5%, respectively. Lastly, it improves the accuracy of attack detection during various communications by 8.3%, 23.2%, and 8.3%, respectively. This paper presents a promising solution for enhancing the security of CRAHNs by providing a robust defense against malicious attacks and real-time protection for dynamic ad hoc networks. The integration of deep learning, blockchain technology, and optimization techniques results in significant improvements in performance metrics, demonstrating the potential of the proposed model for ensuring the security and integrity of CRAHNs.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

BDDTPA: Blockchain-Driven Deep Traffic Pattern Analysis for Enhanced Security in Cognitive Radio Ad-Hoc Networks

Dansana,

Behera,

Darem

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…On the other hand, artificial intelligence (AI) techniques, such as machine learning (ML) and reinforcement learning (RL), have proven to be effective in addressing challenges related to sequential decision making. By equipping UAVs with AI capabilities (AI-enabled UAVs), they can attain a remarkable level of self-awareness, transforming wireless communications [ 28 ]. With AI, UAVs can effectively comprehend the radio environment by discerning and segregating the explanatory factors that are concealed in low-level sensory signals [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

Krayani

Khan

Marchese

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Deploying unmanned aerial vehicles (UAVs) as aerial base stations is an exceptional approach to reinforce terrestrial infrastructure owing to their remarkable flexibility and superior agility. However, it is essential to design their flight trajectory effectively to make the most of UAV-assisted wireless communications. This paper presents a novel method for improving wireless connectivity between UAVs and terrestrial users through effective path planning. This is achieved by developing a goal-directed trajectory planning method using active inference. First, we create a global dictionary using traveling salesman problem with profits (TSPWP) instances executed on various training examples. This dictionary represents the world model and contains letters representing available hotspots, tokens representing local paths, and words depicting complete trajectories and hotspot order. By using this world model, the UAV can understand the TSPWP’s decision-making grammar and how to use the available letters to form tokens and words at various levels of abstraction and time scales. With this knowledge, the UAV can assess encountered situations and deduce optimal routes based on the belief encoded in the world model. Our proposed method outperforms traditional Q-learning by providing fast, stable, and reliable solutions with good generalization ability.

show abstract

Secrecy Outage Probability and Strictly Positive Secrecy Capacity of Cognitive Radio Networks with Adaptive Transmit Power

Alanazi

2023

Wireless Pers Commun

View full text Add to dashboard Cite

An Emergent Self-Awareness Module for Physical Layer Security in Cognitive UAV Radios

Cited by 11 publications

References 39 publications

BDDTPA: Blockchain-Driven Deep Traffic Pattern Analysis for Enhanced Security in Cognitive Radio Ad-Hoc Networks

BDDTPA: Blockchain-Driven Deep Traffic Pattern Analysis for Enhanced Security in Cognitive Radio Ad-Hoc Networks

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

Secrecy Outage Probability and Strictly Positive Secrecy Capacity of Cognitive Radio Networks with Adaptive Transmit Power

Contact Info

Product

Resources

About