An Optimized Neural Framework to Detect Cryptojacking Malware

S. David Jebasingh

doi:https://doi.org/10.59232/DSM-V1I1P105

Research Article | Open Access | Download Full Text

Volume 1 | Issue 1 | Year 2024 | Article Id: DSM-V1I1P105 DOI: https://doi.org/10.59232/DSM-V1I1P105

An Optimized Neural Framework to Detect Cryptojacking Malware

S. David Jebasingh

Received	Revised	Accepted	Published
27 Oct 2023	22 Nov 2023	18 Dec 2023	22 Jan 2024

Citation

S. David Jebasingh. “An Optimized Neural Framework to Detect Cryptojacking Malware.” DS Journal of Multidisciplinary, vol. 1, no. 1, pp. 31-36, 2024.

Abstract

Cryptojacking is the process of hacking the user’s system to mine cryptocurrencies without the user’s acknowledgement through websites. The growing popularity of crypto-currencies and the increasing number of transactions over the internet demand an effective malware detection framework. Hence, a novel hybrid Improved Whale Optimization-based Modular Neural Network model was designed in this paper to detect the network and host-based cryptojacking malware effectively. Initially, the cryptojacking mining dataset was gathered from the standard site and imported into the system. To standardize the raw dataset, it is pre-processed using modular neural features. Then, the whale optimal fitness function is applied in the feature extraction module to track and extract the important data features. In the detection phase, the extracted features are matched with the trained attack features to identify the malicious data. Finally, the results are estimated and compared with the traditional schemes for verification. The performance and comparative analysis show that the presented algorithm outperforms the existing model regarding accuracy, precision, and recall.

Keywords

Cryptojacking, Malware detection, Modular Neural Network, Improved Whale Optimization Approach, Crypto-currency.

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