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Application-Layer DDoS in Enterprise Networks and in the Cloud: Comprehensive Detection and Mitigation

Application-Layer DDoS in Enterprise Networks and in the Cloud: Comprehensive Detection and Mitigation
企业网络和云中的应用层 DDoS:全面检测和缓解
批准号:
RGPIN-2015-06159
负责人:
Vlajic, Natalija
金额:
$1.75万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2017
资助国家:
加拿大
项目状态:
已结题
起止时间:
2017-01-01 至 2018-12-31

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中文摘要
翻译
对数据可用性的攻击通常被称为拒绝服务(DoS)攻击,其中分布式拒绝服务(DDoS)攻击是其最有效的形式。如今,任何拥有计算机和互联网连接的人都可以轻松实现复杂而强大的DDoS攻击。只需花费100美元即可获得自己动手的简单部署DDoS攻击工具。僵尸网络出租也可低至每小时50美元的租赁时间。由于其巨大的可负担性和巨大的破坏潜力,DDoS攻击已成为许多不同个人/团体的首选武器,从出于经济动机的网络犯罪分子到出于政治动机的“黑客活动家”。去年,仅在美国,平均每小时就发生28次DDoS攻击,DDoS造成的停机时间平均为1小时,估计为10万美元。超过40%的受DDoS影响的公司遭受了超过100万美元的损失。
英文摘要
Attacks on data availability are commonly referred to as Denial of Service (DoS) attacks, with Distributed Denial of Service (DDoS) attacks being their most potent form. Nowadays, the means to carry out sophisticated and potent DDoS attacks are within easy reach of anyone with a computer and an Internet connection. Do-it-yourself simple-to-deploy DDoS attack tools can be obtained for only a few $100. Botnets-for-rent are also available for as little as $50 per one hour of rental time. Due to their great affordability combined with a significant damage potential, DDoS attacks have emerged as the weapon of choice for a number of different individual/groups, ranging from financially-motivated cyber criminals to politically-motivated ‘hacktivists’. Last year in the US alone an average of 28 DDoS attacks occurred every hour, with the average cost of 1 hour of DDoS-caused downtime estimated at $100,000. More than 40% of companies affected by DDoS have experienced consequent losses of over $1,000,000 (each).
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Adaptive ML-Based Techniques for Vulnerability Assessment, Threat Modeling and Risk Mitigation in Cyber Security
  • 批准号:
    DGDND-2020-06450
  • 项目类别:
    DND/NSERC Discovery Grant Supplement
  • 资助金额:
    $2.91万
  • 财政年份:
    2022
  • 负责人:
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  • 依托单位:
Adaptive ML-Based Techniques for Vulnerability Assessment, Threat Modeling and Risk Mitigation in Cyber Security
  • 批准号:
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  • 项目类别:
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  • 财政年份:
    2022
  • 负责人:
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  • 依托单位:
Adaptive ML-Based Techniques for Vulnerability Assessment, Threat Modeling and Risk Mitigation in Cyber Security
  • 批准号:
    RGPIN-2020-06450
  • 项目类别:
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  • 资助金额:
    $2.11万
  • 财政年份:
    2021
  • 负责人:
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  • 依托单位:
Adaptive ML-Based Techniques for Vulnerability Assessment, Threat Modeling and Risk Mitigation in Cyber Security
  • 批准号:
    DGDND-2020-06450
  • 项目类别:
    DND/NSERC Discovery Grant Supplement
  • 资助金额:
    $2.91万
  • 财政年份:
    2021
  • 负责人:
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