Mitigation of black hole attacks using firefly and artificial neural network

Rani, Pooja; Kavita, .; Verma, Sahil; Rawat, Danda B.; Dash, Sonali

doi:10.1007/s00521-022-06946-7

Cited by 25 publications

(8 citation statements)

References 27 publications

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“…• Based on Artificial Intelligence [26,33,34,42] A cryptographic paradigm to detect and mitigate blackhole attack is proposed in [13], the proposed method is declared efficient, by the authors, against the black hole attacks. the major inconvenient is the use of RSA Encryption/Decryption which requires high computing performance, while nodes are defined having limited performances.…”

Section: Related Workmentioning

confidence: 99%

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Mitigation of smart black hole attacks using universal sink detection method in graph theory

MEKKAOUI

TEGGAR

2023

Preprint

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Nowadays, network security has become a very important aspect due to the increasing number of connected things and the multiple threats that become more and more intelligent. Mobile Ad hoc networks (MANET), known to be non-infrastructure and self-configured peer networks, are subject to multiple types of attacks. For this reason, it is essential to implement an Intrusion Detection System that realizes fast attack detection to alert users by any malicious activity taking place on the network. Black hole is one of the most serious threats in MANETs, witch is the origin of Denial of service attack. This type of threats has been widely studied and many solutions were proposed. Unfortunately these solutions has become inefficient against the new generation of black holes, known also as smart black holes, witch can deceive most of these solutions. To overcome smart black holes, we proposed an Intrusion Detection System based on the early detecting and isolating malicious nodes by exploiting local information shared by neighbors and using universal sink detection method in graph theory. We proved that smart black holes can defeat the sequence number threshold-based detection strategy by using leastsquare method. Simulations in NS2, showed the efficiency of the proposed approach, which can quickly detect and isolate smart black holes, improve the Packet delivery ratio (PDR) and throughput by an average of 97% and 90%, respectively, thus preserving the network performances.

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Section: Related Workmentioning

confidence: 99%

“…The authors in [33] have proposed an IDS based on using firefly and artificial neural. According to the authors, the proposed approach enhances the Ad hoc On-Demand Distance Vector routing protocol for combating black hole attacks by leveraging the firefly Algorithm with Artificial Neural Network.…”

Section: Related Workmentioning

confidence: 99%

Mitigation of smart black hole attacks using universal sink detection method in graph theory

MEKKAOUI

TEGGAR

2023

Preprint

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“…The authors in [34] propose an IDS that combines the firefly algorithm with an artificial neural network to enhance the Ad hoc On-Demand Distance Vector (AODV) routing protocol for combating black hole attacks.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Security in Mobile Ad Hoc Networks: Detection and Isolation of Smart Black Hole Attacks using Universal Sink Detection Technique in Graph Theory

MEKKAOUI,

TEGGAR,

Houacine

2023

Preprint

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Network security is of utmost importance due to the ever-increasing number of connected devices and the growing sophistication of threats. Mobile Ad hoc networks (MANETs), characterized by their non-infrastructure and self-configured peer networks, are particularly vulnerable to various types of attacks. Therefore, it is imperative to implement an efficient Intrusion Detection System (IDS) capable of rapidly detecting attacks and alerting users to any malicious activities occurring on the network. Among the numerous threats faced by MANETs, the black hole attack stands out as one of the most serious. Originating from Denial of Service attacks, this type of threat has been extensively studied, and several solutions have been proposed. However, these solutions have become ineffective against the emergence of a new generation of black holes, known as smart black holes, which can circumvent most existing countermeasures. To address the challenge posed by smart black holes, we propose an IDS that focuses on early detection and isolation of malicious nodes. Our approach leverages locally shared information from neighboring nodes and utilizes the universal sink detection method derived from graph theory. Through simulations conducted in NS2, we have evaluated the effectiveness of our proposed approach. The results validate the efficiency of our system, as it enables prompt detection and isolation of smart black holes, leading to significant improvements in Packet Delivery Ratio (PDR) and throughput, with average enhancements of 97% and 90%, respectively. Consequently, our approach not only preserves network performance but also mitigates the impact of smart black hole attacks.

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“…It may be sensitive to diverse attacks like DNS cache poisoning attack. These attacks insert the fake DNS records into the cache of the users and directly affect the resolution mapping among the addressing architecture and naming architecture 17 – 19 . Thus, the entire naming architecture gets insecure owing to the lack of integrity protection of the user records.…”

Section: Introductionmentioning

confidence: 99%

A smart privacy preserving framework for industrial IoT using hybrid meta-heuristic algorithm

Kumar

Mukherjee²,

Verma

et al. 2023

Sci Rep

Self Cite

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Industrial Internet of Things (IIoT) seeks more attention in attaining enormous opportunities in the field of Industry 4.0. But there exist severe challenges related to data privacy and security when processing the automatic and practical data collection and monitoring over industrial applications in IIoT. Traditional user authentication strategies in IIoT are affected by single factor authentication, which leads to poor adaptability along with the increasing users count and different user categories. For addressing such issue, this paper aims to implement the privacy preservation model in IIoT using the advancements of artificial intelligent techniques. The two major stages of the designed system are the sanitization and restoration of IIoT data. Data sanitization hides the sensitive information in IIoT for preventing it from leakage of information. Moreover, the designed sanitization procedure performs the optimal key generation by a new Grasshopper–Black Hole Optimization (G–BHO) algorithm. A multi-objective function involving the parameters like degree of modification, hiding rate, correlation coefficient between the actual data and restored data, and information preservation rate was derived and utilized for generating optimal key. The simulation result establishes the dominance of the proposed model over other state-of the-art models in terms of various performance metrics. In respect of privacy preservation, the proposed G–BHO algorithm has achieved 1%, 15.2%, 12.6%, and 1% enhanced result than JA, GWO, GOA, and BHO, respectively.

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Mitigation of black hole attacks using firefly and artificial neural network

Cited by 25 publications

References 27 publications

Mitigation of smart black hole attacks using universal sink detection method in graph theory

Mitigation of smart black hole attacks using universal sink detection method in graph theory

Enhancing Security in Mobile Ad Hoc Networks: Detection and Isolation of Smart Black Hole Attacks using Universal Sink Detection Technique in Graph Theory

A smart privacy preserving framework for industrial IoT using hybrid meta-heuristic algorithm

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