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Network Behavioral Analysis for Zero-Day Malware Detection - A Case Study

机译:零日恶意软件检测的网络行为分析-案例研究

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摘要

The number of cyber threats is constantly increasing. In 2013, 200,000 malicious tools were identified each day by antivirus vendors. This figure rose to 800,000 per day in 2014 and then to 1.8 million per day in 2016! The bar of 3 million per day will be crossed in 2017. Traditional security tools (mainly signature-based) show their limits and are less and less effective to detect these new cyber threats. Detecting never-seen-before or zero-day malware, including ransomware, efficiently requires a new approach in cyber security management. This requires a move from signature-based detection to behavior-based detection. We have developed a data breach detection system named CDS using Machine Learning techniques which is able to identify zero-day malware by analyzing the network traffic. In this paper, we present the capability of the CDS to detect zero-day ransomware, particularly WannaCry.
机译:网络威胁的数量正在不断增加。 2013年,防病毒供应商每天发现200,000个恶意工具。这个数字在2014年上升到每天80万,然后在2016年上升到每天180万!每天300万的门槛将在2017年突破。传统的安全工具(主要是基于签名的工具)显示出其局限性,并且越来越难以检测到这些新的网络威胁。有效地检测出前所未有的或零日恶意软件,包括勒索软件,需要一种新的网络安全管理方法。这需要从基于签名的检测到基于行为的检测的转变。我们已经使用机器学习技术开发了一个名为CDS的数据泄露检测系统,该系统能够通过分析网络流量来识别零日恶意软件。在本文中,我们介绍了CDS检测零日勒索软件(尤其是WannaCry)的功能。

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