Modeling and Detection of Flooding-Based Denial of Service Attacks in Wireless Ad Hoc Networks Using Uncertain Reasoning

Nishanth, N.; Mujeeb, A.

doi:10.1109/tccn.2021.3055503

Cited by 11 publications

(12 citation statements)

References 22 publications

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“…N. Nishanth et al [14] use Bayesian inference and the Dempster-Shafer evidence theory approach to defend against RREQ flood attacks in wireless ad hoc networks. The best Bayesian inference system for identifying persistent RREQ flooding attacks has been created using Bayesian inference to detect high and low-rate pulsed RREQ flooding attacks.…”

Section: Related Workmentioning

confidence: 99%

Node authentication and encrypted data transmission in mobile ad hoc network using the swarm intelligence‐based secure ad‐hoc on‐demand distance vector algorithm

Patil,

Borkar

2023

IET Wireless Sensor Systems

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In a mobile ad hoc network (MANET), all nodes are communicated with one another across wireless networks to create a temporary network without the support of centralised management. Due to dynamic topology in MANET, secure routing is a crucial issue. The existing secure routing protocol and their security concerns are analysed in this work. The suggested Swarm Intelligence‐based Secure Ad‐hoc On‐demand Distance Vector (SIS‐AODV) algorithm offers security by applying a secret key and hash mechanism to prevent the involvement of malicious nodes in routing operations. A secure routing system of MANET guards against internal and external network attacks. The proposed SIS‐AODV algorithm consists of two sections: the secret key value generated by applying Elliptical Curve Cryptography (ECC)‐based algorithm and the PRESENT algorithm to encrypt the data packets. Besides, authentication and non‐repudiation are applied using the H‐PRESENT 128 algorithm. The PRESENT algorithm and H‐PRESENT 128 hash function require less computational power. Centralised management is optional in this scheme, so overhead decreases. The second section of SIS‐AODV consists of Ant Colony Grey Wolf Optimization over the AODV algorithm to improve network performance while implementing a security algorithm over MANET. Analysis results show maximum performance with a packet delivery ratio of 98% and throughput of 85%. In addition, end‐to‐end delay is reduced by up to 25%, and routing overhead decreases by up to 20%. Keywords: AODV, Elliptical Curve, PRESENT, H‐PRESENT, Euclidean Algorithm, ACO, GWO, Blackhole attack.

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Section: Related Workmentioning

confidence: 99%

Node authentication and encrypted data transmission in mobile ad hoc network using the swarm intelligence‐based secure ad‐hoc on‐demand distance vector algorithm

Patil,

Borkar

2023

IET Wireless Sensor Systems

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“…Work in [3] focuses on the application of machine learning techniques to the detection of intrusions in CRAHNs. It proposes a classification model that employs supervised learning algorithms to determine whether network traffic is benign or malicious.…”

Section: Cqualitative Analysismentioning

confidence: 99%

“…This work distinguishes itself by proposing a novel approach for enhancing the security of CRAHNs that combines deep learning, blockchain technology, and optimization techniques. The proposed model, unlike work in [3], addresses the scalability and real-time processing limitations of existing deep learning models. In contrast to the work in [26], the proposed model not only focuses on routing but also provides a comprehensive solution that encompasses traffic pattern analysis, attack detection, data integrity, and transparency via blockchain technology.…”

Section: Qualitative Comparisonmentioning

confidence: 99%

“…Utilizing collaborative spectrum sensing is one way that may be used to improve the performance of CR-IoT. This method requires a number of BEs to sense the environment jointly and then broadcast their results to either the other BSs or a Master BS via Bayesian Inference and Dempster-Shafer (BIDS) [3]. BSs and the Master BE will then integrate the data that has been provided before reaching a conclusion on the presence or absence of PU broadcasts and determining whether or not a channel should be categorized as active or idle.…”

Section: Introductionmentioning

confidence: 99%

“…As a direct consequence, collaborative spectrum sensing is much less reliable. [3] The acronym "Spectrum Sensing Data Falsification" (SSDF) refers to this cyberattack. Applications such as the Bayesian-Inference-Based Sliding Window Trust Model (BIST WM) [4] have begun using cognitive technology to guarantee the authenticity of the transactional data they store.…”

Section: Introductionmentioning

confidence: 99%

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

Dansana,

Behera,

Darem

et al. 2023

IEEE Access

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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.

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An efficient packet dropping attack detection mechanism in wireless ad-hoc networks using ECC based AODV-ACO protocol

Kanthimathi

Rani²

2022

Wireless Netw

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Modeling and Detection of Flooding-Based Denial of Service Attacks in Wireless Ad Hoc Networks Using Uncertain Reasoning

Cited by 11 publications

References 22 publications

Node authentication and encrypted data transmission in mobile ad hoc network using the swarm intelligence‐based secure ad‐hoc on‐demand distance vector algorithm

Node authentication and encrypted data transmission in mobile ad hoc network using the swarm intelligence‐based secure ad‐hoc on‐demand distance vector algorithm

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

An efficient packet dropping attack detection mechanism in wireless ad-hoc networks using ECC based AODV-ACO protocol

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