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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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通过自适应性特征选择,用于不平衡的多类恶意软件分类的机器学习技术.

Binayak Panda1, Sudhanshu Shekhar Bisoyi2, Sidhanta Panigrahy3

  • 1Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India.

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此摘要是机器生成的。

本研究引入了一个自适应多类恶意软件分类 (AMMC) 框架,具有自适应特征选择 (AFS),以有效检测恶意软件变体. 拟议的方法在多个数据集上取得了最先进的结果,提高了恶意软件检测性能.

关键词:
API序列的API序列是什么贪的功能选择选项机器学习 机器学习多类恶意软件分类多类恶意软件分类.跳过-克拉姆可以.国际货币基金组织-国际货币基金组织

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 机器学习 机器学习

背景情况:

  • 检测多态和变形恶意软件变体是一个重大挑战.
  • 人工智能 (AI) 与传统的基于签名的恶意软件检测方法相比,具有优势.
  • 恶意软件的扩散需要有效和准确的分类技术.

研究的目的:

  • 提出一个适应性多类恶意软件分类 (AMMC) 框架.
  • 引入一种新的自适应特征选择 (AFS) 技术,以改善恶意软件分类.
  • 为了应对不平衡的多类恶意软件检测的挑战.

主要方法:

  • 开发了一个自适应多类恶意软件分类 (AMMC) 框架.
  • 实施了适应性特征选择 (AFS) 技术,使用TF-IDF权重上的贪策略.
  • 在使用Windows API序列功能对三个不平衡的多类恶意软件数据集 (VirusShare,VirusSample,MAL-API-2019) 进行了框架评估.

主要成果:

  • 与AFS的AMMC框架在所有测试的数据集中实现了最先进的性能.
  • 针对VirusShare,VirusSample和MAL-API-2019分别获得了0.92,0.94和0.84的宏F1得分.
  • 病毒共享,病毒样本和MAL-API-2019分别获得了0.99,0.99和0.98的宏观AUC得分.

结论:

  • 拟议的AMMC框架与AFS对于不平衡的多类恶意软件分类非常有效.
  • AFS技术成功地识别了有影响力的特征,提高了分类性能.
  • 该框架在有效检测各种恶意软件变体方面显示出有希望的结果.