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Survey and Research Directions on Intrusion Detection in UNIX Environment

机译:UNIX环境下入侵检测的调查研究方向

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摘要

Although UNIX is considered a very stable and secure platform, the development of Intrusion Detection Systems is essential as current and future generations of hackers are continuously attempting to undermine its integrity. There are few intrusion detection systems in UNIX for detecting multiple threats in a distributed networking environment. Researchers have applied different statistical models that involve data fusion. The most common and popular approaches include Bayesian theory, Dempster Shafer Evidence Theory, Parametric and Non-Parametric techniques, and Markov Chain. With few exceptions, almost all these detection models cater only for single threat. Thus, there is a genuine need for research on multisensor data fusion model in intrusion detection systems that enhance its capability to detect multiple simultaneous threats, particularly in the UNIX environment. In this paper, I'll survey existing intrusion detection system s and detection models in the literature, followed by a discussion of my research directions on intrusion detection in UNIX environment.
机译:尽管UNIX被认为是非常稳定和安全的平台,但是随着当前和未来几代黑客不断尝试破坏其完整性,入侵检测系统的开发至关重要。 UNIX中很少有入侵检测系统可用于检测分布式网络环境中的多种威胁。研究人员应用了涉及数据融合的不同统计模型。最常见和流行的方法包括贝叶斯理论,Dempster Shafer证据理论,参数和非参数技术以及马尔可夫链。除少数例外,几乎所有这些检测模型都只针对单个威胁。因此,真正需要对入侵检测系统中的多传感器数据融合模型进行研究,以增强其检测多种并发威胁的能力,尤其是在UNIX环境中。在本文中,我将调查文献中现有的入侵检测系统和检测模型,然后讨论我在UNIX环境中对入侵检测的研究方向。

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