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Study on implementation of machine learning methods combination for improving attacks detection accuracy on Intrusion Detection System (IDS)

机译:机器学习方法组合在入侵检测系统(IDS)上提高攻击检测精度的实现研究

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摘要

Many computer-based devices are now connected to the internet technology. These devices are widely used to manage critical infrastructure such energy, aviation, mining, banking and transportation. The strategic value of the data and the information transmitted over the Internet infrastructure has a very high economic value. With the increasing value of the data and the information, the higher the threats and attacks on such data and information. Statistical data shows a significant increase in threats to cyber security. The Government is aware of the threats to cyber security and respond to cyber security system that can perform early detection of threats and attacks the internet. The success of a nation's cyber security system depends on the extent to which it is able to produce independently their cyber defense system. Independence is manifested in the form of the ability to process, analyze and create an action to prevent threats or attacks originating from within and outside the country. One of the systems can be developed independently is Intrusion Detection System (IDS) which is very useful for early detection of cyber threats and attacks. The advantages of an IDS is determined by its ability to detect cyber attacks with little false. This study learn how to implement a combination of various methods of machine-learning to the IDS to improve the accuracy in detecting attacks. This study is expected to produce a prototype IDS. This prototype IDS, will be equipped with a combination of machine-learning methods to improve the accuracy in detecting various attacks. The addition of machine-learning feature is expected to identify the specific characteristics of the attacks occurred in the Indonesian Internet network. Novel methods used and techniques in implementation and the national strategic value are becoming the unique value and advantages of this research.
机译:现在,许多基于计算机的设备已连接到Internet技术。这些设备被广泛用于管理关键基础设施,例如能源,航空,采矿,银行和运输。通过Internet基础结构传输的数据和信息的战略价值具有很高的经济价值。随着数据和信息的价值不断增长,对此类数据和信息的威胁和攻击也越来越多。统计数据表明,对网络安全的威胁显着增加。政府意识到网络安全面临的威胁,并响应可以尽早发现威胁并攻击互联网的网络安全系统。一个国家的网络安全系统的成功取决于它能够独立产生其网络防御系统的程度。独立表现为具有处理,分析和制定行动的能力,可以防止来自国内或国外的威胁或攻击。可以独立开发的系统之一是入侵检测系统(IDS),它对于早期检测网络威胁和攻击非常有用。 IDS的优势取决于其检测网络攻击的可能性很小。本研究学习如何将各种机器学习方法结合到IDS中,以提高检测攻击的准确性。预期该研究将产生原型IDS。该原型IDS将配备多种机器学习方法,以提高检测各种攻击的准确性。预计将增加机器学习功能,以识别在印度尼西亚Internet网络中发生的攻击的特定特征。所采用的新颖方法和实施技术以及国家战略价值正在成为本研究的独特价值和优势。

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