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

Abstract

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.

Design of an Incremental Graph-based Routing Model for enhancing QoS of dense IoMT Networks using Truncated Side chains

“…Where, 𝑡𝑠 𝑟𝑒𝑎𝑐ℎ &𝑡𝑠 𝑠𝑡𝑎𝑟𝑡 represents the timestamps at which the nodes reach to the destination location and start from the source locations. The delay performance was compared with TRS [6], BDDTPA [8], and DRLPB [23] in figure 2 As the number of routing communications (NM) increases, we observe that the delay in IGRMQITS consistently outperforms the other models. For instance, when NM is 25,000, IGRMQITS achieves a significantly lower delay of 8.71 seconds, compared to TRS (11.38 s), BDDTPA (12.90 s), and DRLPB (14.72 s).…”

“…This is especially important in healthcare scenarios where real-time data transmission can be a matter of life and death, making IGRMQITS a promising solution for enhancing IoMT network performance.Similar performance was estimated for energy consumption via equation 29, and tabulated in figure 3 The Energy (E) required to perform routing in Internet of Medical Things (IoMT) Networks, measured in milliwatts (mW), is a critical parameter that directly affects the power consumption and efficiency of the network devices. Here, we compare the energy consumption results between the proposed Incremental Graph-based Routing Model for QoS enhancement in dense IoMT Networks using Truncated Sidechains (IGRMQITS) and three existing models: TRS [6], BDDTPA [8], and DRLPB [23]. The results are presented for IGRMQITS consistently demonstrates lower energy consumption compared to the other models across varying NM values.…”

“…It is an important parameter because it reflects the stability and predictability of the network's performance. Here, we compare the delay jitter results between the proposed Incremental Graph-based Routing Model for QoS enhancement in dense IoMT Networks using Truncated Sidechains (IGRMQITS) and three existing models: TRS [6], BDDTPA [8], and DRLPB [23] Overall, the lower delay jitter in IGRMQITS signifies its ability to offer a more stable and predictable network performance in IoMT networks, particularly in healthcare settings where data reliability and timeliness are paramount. This makes IGRMQITS a promising solution for enhancing the QoS and ensuring the dependable transmission of critical medical data in dense IoMT environments, ultimately improving patient outcomes and healthcare quality.Similarly, the throughput performance can be observed from figure6 as follows, Figure 6.…”

“…The model is underpinned by a robust fusion of temporal and spatial information to identify optimal routes, facilitated by a graph-based Q Learning (GQL) algorithm. Recognizing the importance of an efficient optimization mechanism, we leverage the novel Teacher Learner based Firefly Optimizer (TLFFO) to further refine the route identification process [7,8,9]. An application of this is use of Blockchain-Driven Deep Traffic Pattern Analysis (BDDTPA) for real-time operations.…”

BSMACRN: Design of an Efficient Blockchain-Based Security Model for Improving Attack-Resilience of Cognitive Radio Ad-hoc Networks

“…A different layer of security attacks in CRAHNs is discussed in [44], where the authors discussed attacks like Collision, Denial-of-service (DoS), Exhaustion, Selective Forwarding, Sinkhole, Sybil, Wormhole Hello Flood, SYN Flooding, De-Synchronizing, Logical Error Buffer Overflow, Primary User Emulation Attack (PUEA), jamming, Traffic Analysis, Attack on Data privacy and location Privacy. The proof-of-trust (PoT) consensus mechanism is managed via a Genetic Algorithm (GA)--based sidechaining model that the authors used for the security in CRAHNs [45]. In the domain of smart contracts for blockchain applications, recent scholarly work has highlighted the challenges in their adaptability and the limitations in source code reusability, primarily restricted to cloning practices.…”