No / Developing advanced and efficient malware detection systems is
becoming significant in light of the growing threat landscape in cybersecurity. This work aims to tackle the enduring problem of identifying malware and protecting digital assets from cyber-attacks.
Conventional methods frequently prove ineffective in adjusting
to the ever-evolving field of harmful activity. As such, novel approaches that improve precision while simultaneously taking into
account the ever-changing landscape of modern cybersecurity problems are needed. To address this problem this research focuses on
the detection of malware in network traffic. This work proposes
a machine-learning-based approach for malware detection, with
particular attention to the Random Forest (RF), Support Vector Machine (SVM), and Adaboost algorithms. In this paper, the model’s
performance was evaluated using an assessment matrix. Included
the Accuracy (AC) for overall performance, Precision (PC) for positive predicted values, Recall Score (RS) for genuine positives, and
the F1 Score (SC) for a balanced viewpoint. A performance comparison has been performed and the results reveal that the built model
utilizing Adaboost has the best performance. The TPR for the three
classifiers performs over 97% and the FPR performs < 4% for each of
the classifiers. The created model in this paper has the potential to
help organizations or experts anticipate and handle malware. The
proposed model can be used to make forecasts and provide management solutions in the network’s everyday operational activities.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19792 |
| Date | 19 December 2023 |
| Creators | Omopintemi, A.H., Ghafir, Ibrahim, Eltanani, S., Kabir, Sohag, Lefoane, Moemedi |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Conference paper, No full-text in the repository |
| Rights | Unspecified |
| Relation | https://icfnds.org/ |
Page generated in 0.0021 seconds