Maneuvering Black-Hole Attack Using Different Traffic Generators in MANETs

Taranum, Fahmina; Khan, Khaleel Ur Rahman

doi:10.1007/978-981-13-6095-4_8

Cited by 3 publications

(3 citation statements)

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“…The Authors in [3] have worked on validating the accuracy of the results generated. Author in [4] has worked on multiple traffic generators and has evaluated performance in normal and malicious scenarios, the extension to the work would be to coordinate the attackers. In [5] Rmayti et al accords a scenario of using a coordinated behavior through tunnel for worm hole attack, which is adapted here to check the performance degradation in coordination.…”

Section: Literature Surveymentioning

confidence: 99%

“…The node further affects the network transmission by retaining or by dropping the data, which results in the drop of packet forwarding rate. In [4] author has selected the height of antenna as ≥3 meter to capture the traffic along with increasing the storage of the buffer size of the node as parameters to inhibit malicious behavior. Figure 6 depicts the results obtained with different classifiers on varying speed of the mobile nodes to analyze the accuracy and it has been found that SVM performs the best in all the considered classifiers.…”

Section: Pdr% =mentioning

confidence: 99%

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Securing Mobile Adhoc Networks from Black-Hole Attacks

Taranum

Khan

2021

Frontiers in Artificial Intelligence and Applications

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MANETs are in-secure and vulnerable to attacks, as it lacks a central trusted authority. Providing security to such a network becomes an essential task and formulate the origin of the proposed work. The proposed system attempts to secure MANETs using route validation and cryptographic techniques. One of the most crucial, and primary concerns in today’s times is to be able to detect an attacker at its initial stages. With this focus point in mind, the approach used is to prevent such an attacks by detecting in initial stage and preventing from network degradation. The novelty of the proposal is the use of cryptographic techniques for improving the security, along a reverse-AODV for reducing path fail correction, and machine learning concepts for validation of results. Many of the existing malware detection techniques proposed by the researchers were executed either on machine independent platform, or on an available dataset with machine dependent approaches. This drawback has been addressed in the existing proposal, where in machine learning is used with self-generated data-set, to eliminate contingent problems. The proposed system includes a network that is free from malware. Justification of the results were generated using classifier tools that were trained with the obtained dataset. For secured communication, an Elliptical curve cryptographic algorithm is applied to reverse ad-hoc on-demand distance vector with reverse multiple route replies that have been generated from the destination to the source node. An investigation to ensure the correct delivery of data can be done by diverting the traffic through the shortest alternative secured path. The metrics used for statistical analysis include average transmission-delay, overhead, packet forwarding rate and packet- delivery-rate, based on which the conclusion is theorized for the detection. The major finding includes the process of selecting an appropriate condition for detecting a malicious node, observing the network behavior with varying number of suspicious nodes and then validating the correctness. The implementation gives us varied results, both when the suspicious node is deferred for some time, and then on its complete elimination. The limitation of the proposal is that the suspicious nodes are uncoordinated.

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Section: Literature Surveymentioning

confidence: 99%

Section: Pdr% =mentioning

confidence: 99%

Securing Mobile Adhoc Networks from Black-Hole Attacks

Taranum

Khan

2021

Frontiers in Artificial Intelligence and Applications

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“…After reaching the RREQ packet to the destination node, the destination regenerates the RREP packet. The regenerated RREP packet along with the FRREP with the highest hop count is sent by the black hole node towards the source node (Taranum and Khan;. As the source node received the packet with the highest hope count then it starts sending data towards the black hole node by believing that it is a destination node (Majumderand Bhattacharyya; 2020).…”

Section: Figure 1 Sending Frrep By Black Hole Nodementioning

confidence: 99%

Mitigation of Black Hole and Gray Hole Attack Using Swarm Inspired Algorithm With Artificial Neural Network

et al. 2020

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Wireless technology and the latest developments in a mobile object, has led to a Mobile Ad Hoc network (MANET), which is a collection of mobile nodes that are communicating with each other without requiring any fixed infrastructure. Due to the dynamic nature with a decentralized system, these networks are susceptible to different attacks such as Black Hole Attack (BHA), Gray Hole Attack (GHA), Sink Hole Attack (SHA) and many more. Several researchers have worked for the detection and mitigation of individual attacks, either GHA or BHA nodes. But the protection of MANET against a dual-threat is scarce. In this paper, the protection against dual attacks has been presented for BHA and GHA by using the concept of Artificial Neural Network (ANN) as a deep learning algorithm along with the swarm-based Artificial Bee Colony (ABC) optimization technique. The performance of the system has been increased by the selection of appropriate and best nodes for data packets transmission which is explained in the result section of this paper. For the network designing and simulation purposes, MATLAB software is used with communication and neural network toolboxes. The examined results show that the presented protocol performs better in contrast to the existing work under black hole as well as gray hole attack condition A mobile ad hoc network (MANET) is a collection of mobile nodes that dynamically form a temporary network without using any existing network infrastructure.

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