Analysis of Blackhole Attack in AODV and DS

Panda, Niranjan; Pattanayak, Binod Kumar

doi:10.11591/ijece.v8i5.pp3092-3102

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…Denial of Service Attack. [20] Preventing a legitimate node from access, network or services Sybil Attack [21] Malicious node creates a large number of identities of other Blackhole attack [22] Malicious node receives packets and replies with high sequence rather than discard them…”

Section: Layermentioning

confidence: 99%

A Cooperative Smart Jamming Attack in Internet of Things Networks

Sharah

Owida

Edwan

et al. 2022

JICCE

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The emerging scope of the Internet-of-Things (IoT) has piqued the interest of industry and academia in recent times. Therefore, security becomes the main issue to prevent the possibility of cyberattacks. Jamming attacks are threads that can affect performance and cause significant problems for IoT device. This study explores a smart jamming attack (coalition attack) in which the attackers were previously a part of the legitimate network and are now back to attack it based on the gained knowledge. These attackers regroup into a coalition and begin exchanging information about the legitimate network to launch attacks based on the gained knowledge. Our system enables jammer nodes to select the optimal transmission rates for attacks based on the attack probability table, which contains the most probable link transmission rate between nodes in the legitimate network. The table is updated constantly throughout the life cycle of the coalition. The simulation results show that a coalition of jammers can cause highly successful attacks.

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Section: Layermentioning

confidence: 99%

A Cooperative Smart Jamming Attack in Internet of Things Networks

Sharah

Owida

Edwan

et al. 2022

JICCE

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“…The result revealed that the proposed method was active with the different parameters such as trust level, energy consumption, false acceptance, packet loss and missing rate of detection. [25]Studied about the specificity of black hole attack in AODV and DSR. MANET did not need any fixed structure and can be arranged in an aggressive environment where the implementation of the classic network is tough.…”

Section: Black Holementioning

confidence: 99%

Host based Detection and Prevention of Black Hole attacks by AODV-ICCSO Algorithm for security in MANETs

Sathyaraj¹,

D²,

Kannan³

2021

Preprint

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Background: Mobile Ad-hoc Networks (i.e.) MANETs are gaining rapid fame in recent days and are considered as very significant because of their easier implementation and growing property. Various types of attacks are prone to damage the networks due to the elastic property possessed by the network. And among different categories of attacks that can affect MANETs, black hole attack is considered as the commonly occurring one within a MANET. Chicken Swarm Optimization (CSO) algorithm is one among the technique used for the detection of black hole attacks occurring in the MANETs. But the CSO algorithm possesses some disadvantages and necessity rises for overcoming the weakness in the CSO algorithm. Objective: Therefore, in this research paper, to address the black hole attack in MANET, an Improved Crossover Chicken Swarm Optimization (ICCSO) algorithm and the concept of Enhanced Partially-Mapped Crossover operation proposed and the best fitness values obtained. Methods: In ICCSO algorithm, parameter initialization is carried out in step 1 of the algorithm, where the attacked nodes and non-attack nodes are created separately with the aid of parameters like PDR (i.e.) Packet Delivery Ratio and RSSI (i.e.) Received Signal Strength Indicator. Further, If the node is affected by any attack, then the nodes are discarded and the data is transmitted through the non-attacked node. Routing is carried by a protocol of AODV.Results: The effectiveness of the algorithm proposed in the work is evaluated using various performance measures like packet delivery ratio (PDR), end-to-end delay (EED) and throughput. The performance measures are compared with a different state of the art routing protocols and it can be inferred that the proposed methodology comes up with improved results.

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“…-Divide the global goal "VANET availability" within sub-goals. In our case, there are three leaves attached to the root node: Black Hole (S1) [15], Denial of Service (S2) [16], Malware & spam (S3).…”

Section: Building Attack Tree For Vanet Availabilitymentioning

confidence: 99%

A risk and security assessment of VANET availability using attack tree concept

Houmer

Hasnaoui

2020

IJECE

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The challenging nature of insecure wireless channels and the open-access environment make the protection of vehicular ad hoc network (VANET) a particularly critical issue. Researchers and interested authorities have therefore paid more attention to this issue. Therefore, robust approaches to protect this network's security and privacy against adversaries and attacks need to be improved, trying to achieve an adequate level, to secure the confidential information of drivers and passengers. Accordingly, to improve the security of VANET, it is necessary to carry out a risk assessment, in order to evaluate the risk that faces this network. This paper focuses on the security threats in vehicular network especially on the availability of this network. We propose a novel risk assessment approach to evaluate the risk of the attack that the attacker can lead against the availability of VANET. We adopt a tree structure called attack tree to model the attacker's potential attack strategies. Based on this attack tree model, we can estimate the degree that a certain threat can lead to the VANET and identify possible attack sequences that an attacker may launch against the availability of this network. Then we utilize the multi-attribute utility theory to calculate the system’s total risk value also the probabilities of each attack sequence. The analysis results can provide support for decision-makers to make corresponding protection measures against the attack on the availability of this network.

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Analysis of Blackhole Attack in AODV and DS

Cited by 6 publications

References 4 publications

A Cooperative Smart Jamming Attack in Internet of Things Networks

A Cooperative Smart Jamming Attack in Internet of Things Networks

Host based Detection and Prevention of Black Hole attacks by AODV-ICCSO Algorithm for security in MANETs

A risk and security assessment of VANET availability using attack tree concept

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