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| 1 | 基于人工智能的网络空间安全防御战略研究显示文摘网络空间是继陆、海、空、天之后的第五大活动空间,维护网络空间安全是事关国家安全、国家主权和人民群众合法权益的重大问题。随着人工智能技术的飞速发展和在各领域的应用,网络空间安全面临着新的挑战。本文分析了人工智能时代网络空间安全面临的新风险,包括网络攻击越来越智能化,大规模网络攻击越来越频繁,网络攻击的隐蔽性越来越高,网络攻击的对抗博弈越来越强,重要数据越来越容易被窃取等;介绍了人工智能技术在处理海量数据、多源异构数据、实时动态数据时具有显著的优势,能大幅度提升网络空间防御能力;基于人工智能的网络空间防御关键问题及技术,重点分析了网络安全知识大脑的构建及网络攻击研判,并从构建动态可扩展的网络安全知识大脑,推动有效网络攻击的智能化检测,评估人工智能技术的安全性三个方面提出了针对性的发展对策和建议。 | 贾焰 方滨兴 李爱平 顾钊铨 | 2021 | 中国工程科学2021，23，3： | 22 |
| 2 | Marginal Attacks of Generating Adversarial Examples for Spam Filtering显示文摘Digit information has been used in many areas and has been widely spread in the Internet era because of its convenience.However,many ill-disposed attackers,such as spammers take advantage of such convenience to send unsolicited information,such as advertisements,frauds,and pornographic messages to mislead users and this might cause severe consequences.Although many spam filters have been proposed in detecting spams,they are vulnerable and could be misled by some carefully crafted adversarial examples.In this paper,we propose the marginal attack methods of generating such adversarial examples to fool a naive Bayesian spam filter.Specifically,we propose three methods to select sensitive words from a sentence and add them at the end of the sentence.Through extensive experiments,we show that the generated adversarial examples could largely reduce the filter's detecting accuracy,e.g.by adding only one word,the accuracy could be reduced from 93.6%to 55.8%.Furthermore,we evaluate the transferability of the generated adversarial examples against other traditional filters such as logic regression,decision tree and linear support vector machine based filters.The evaluation results show that these filters'accuracy is also reduced dramatically;especially,the decision tree based filter's accuracy drops from 100%to 1.51%by inserting only one word. | GU Zhaoquan XIE Yushun HU Weixiong YIN Lihua HAN Yi TIAN Zhihong | 2021 | Chinese Journal of Electronics2021，30，4： | 0 |
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