A novel approach for early malware detection,Transactions on Emerging Telecommunications Technologies

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A novel approach for early malware detection
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2020-04-28 , DOI: 10.1002/ett.3968
Anshul Sharma ₁ , Sanjay Kumar Singh ₁

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Early classification of time series is valuable in many real‐world applications such as early disease prediction, early disaster prediction, and patient monitoring where data are generated over time. The main objective of early classification is to provide a reliable class prediction earliest in time. In general, whenever the early prediction time improves, the prediction accuracy decreases. Thus, the trade‐off between earliness and accuracy needs to be addressed. In this article, we proposed an optimization‐based early classification model for time series data using early stopping rules (ESRs) and a series of probabilistic classifiers. ESRs are developed through particle swarm optimization by minimizing the well‐defined cost function that considers the missclassification cost and delaying decision cost simultaneously. The experimental results on 30 standard datasets demonstrate good performance for early classification in comparison to state of the art methods. Also, the proposed model is tested for early malware detection on a real dataset and shows decent performance by balancing the accuracy and earliness.

中文翻译：

一种早期恶意软件检测的新颖方法

时间序列的早期分类在许多实际应用中都很有价值，例如早期疾病预测，早期灾难预测以及随时间生成数据的患者监控。早期分类的主要目的是尽早提供可靠的分类预测。通常，每当早期预测时间改善时，预测精度就会下降。因此，需要解决早期与准确性之间的权衡问题。在本文中，我们使用早期停止规则（ESR）和一系列概率分类器为时间序列数据提出了基于优化的早期分类模型。通过最小化定义明确的成本函数（通过同时考虑错分类成本和延迟决策成本）来最小化粒子群优化来开发ESR。与现有技术方法相比，在30个标准数据集上的实验结果证明了早期分类的良好性能。此外，所提出的模型已经过测试，可以在真实数据集上进行早期恶意软件检测，并且可以通过平衡准确性和早期性来显示良好的性能。

更新日期：2020-04-28

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