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首页> 外文期刊>International journal of knowledge-based and intelligent engineering systems >A robust intrusion detection system using machine learning techniques for MANET
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A robust intrusion detection system using machine learning techniques for MANET

机译：一种利用机器学习技术的强大入侵检测系统

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摘要

Recent advancement in technologies such as Cloud, Internet of Things etc., leads to the increase usage of mobile computing. Present day mobile computing are too sophisticated and advancement are reaching great heights. Moreover, the present day mobile network suffers due to external and internal intrusions within and outside networks. The existing security systems to protect the mobile networks are incapable to detect the recent attacks. Further, the existing security system completely depends on the traditional signature and rule based approaches. Recent attacks have the property of not fluctuating its behaviour during attack. Hence, a robust Intrusion Detection System (IDS) is desirable. In order to address the above mentioned issue, this paper proposed a robust IDS using Machine Learning Techniques (MLT). The key of using MLT is to utilize the power of ensembles. The ensembles of classifier used in this paper are Random Forest (RF), KNN, Naive Bayes (NB), etc. The proposed IDS is experimentally tested and validated using a secure test bed. The experimental results also confirms that the proposed IDS is robust enough to withstand and detect any form of intrusions and it is also noted that the proposed IDS outperforms the state of the art IDS with more than 95% accuracy.
机译：最近的技术推进如云，互联网等，导致移动计算的使用增加。现在的移动计算过于复杂，进步达到了很大的高度。此外，目前的移动网络因内外网络内外的外部和内部入侵而受到影响。保护移动网络的现有安全系统无法检测到最近的攻击。此外，现有安全系统完全取决于传统的签名和规则的方法。最近的攻击具有在攻击期间不会波动其行为的财产。因此，希望稳健的入侵检测系统（ID）是理想的。为了解决上述问题，本文提出了一种使用机器学习技术（MLT）的强大ID。使用MLT的关键是利用合奏的力量。本文中使用的分类器的合奏是随机森林（RF），KNN，幼稚贝叶斯（NB）等。所提出的IDS使用安全测试床进行实验测试和验证。实验结果还证实，所提出的IDS足以承受并检测任何形式的入侵，并且还注意到所提出的IDS优于超过95％的准确度的最新状态。

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