Inferring the Purpose of Network Activities

推断网络活动的目的

基本信息

  • 批准号:
    EP/N008448/1
  • 负责人:
  • 金额:
    $ 12.52万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2015
  • 资助国家:
    英国
  • 起止时间:
    2015 至 无数据
  • 项目状态:
    已结题

项目摘要

The sophistication of attacks targeting computer networks is constantly increasing. Recently, we have witnessed multiple sophisticated targeted attacks against governments and companies. Such attacks are much different than traditional network attacks, because attackers have virtually unlimited resources and can tailor their operation to the victim's network, making these attacks very difficult to detect. In fact, current state of the art detection techniques are inadequate to protect computer networks against targeted attacks.In this proposal, we aim to make some fundamental steps towards being able to reliably detect targeted attacks on computer networks. To this end, we plan to abstract the observation from the actual manifestation of an attack, and focus on the purpose behind network activities instead. We believe that modern machine learning techniques such as deep belief networks can be used to automatically learn high-level features from network data. Such features are indicative of the purpose for which the network activity is performed, rather than of the specific techniques and tools used to accomplish that purpose. These high-level features can then be used in traditional supervised machine learning to detect whether a network activity is being performed with a malicious intention or a benign one.
以计算机网络为目标的攻击的复杂性不断增加。最近,我们目睹了针对政府和公司的多起复杂的有针对性的攻击。这种攻击与传统的网络攻击有很大的不同,因为攻击者几乎拥有无限的资源,可以根据受害者的网络定制他们的操作,使得这些攻击非常难以检测。事实上,目前最先进的检测技术是不足以保护计算机网络免受有针对性的attacks.In这个建议,我们的目标是能够可靠地检测到有针对性的攻击计算机网络上的一些基本步骤。为此,我们计划从攻击的实际表现中提取观察结果,并专注于网络活动背后的目的。我们相信深度信念网络等现代机器学习技术可以用于从网络数据中自动学习高级特征。这些特征指示执行网络活动的目的,而不是用于实现该目的的特定技术和工具。然后,这些高级特征可以用于传统的监督机器学习,以检测网络活动是以恶意还是善意的意图执行的。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
What's in a name?
名字里有什么?
  • DOI:
    10.7554/elife.32437
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    7.7
  • 作者:
    Schaller,MichaelD;McDowell,Gary;Porter,André;Shippen,Dorothy;Friedman,KatherineL;Gentry,MatthewS;Serio,TriciaR;Sundquist,WesleyI
  • 通讯作者:
    Sundquist,WesleyI
What's Your Major Threat? On the Differences between the Network Behavior of Targeted and Commodity Malware
您的主要威胁是什么?
  • DOI:
    10.1109/ares.2016.36
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mariconti E
  • 通讯作者:
    Mariconti E
Permissions snapshots: Assessing users' adaptation to the Android runtime permission model
Towards Detecting Compromised Accounts on Social Networks
  • DOI:
    10.1109/tdsc.2015.2479616
  • 发表时间:
    2015-09
  • 期刊:
  • 影响因子:
    7.3
  • 作者:
    Manuel Egele;G. Stringhini;C. Kruegel;Giovanni Vigna
  • 通讯作者:
    Manuel Egele;G. Stringhini;C. Kruegel;Giovanni Vigna
Human Aspects of Information Security, Privacy and Trust - 5th International Conference, HAS 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, Proceedings
信息安全、隐私和信任的人为方面 - 第五届国际会议,HAS 2017,作为 HCI International 2017 的一部分举行,加拿大不列颠哥伦比亚省温哥华,2017 年 7 月 9-14 日,会议记录
  • DOI:
    10.1007/978-3-319-58460-7_19
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Al Moubayed N
  • 通讯作者:
    Al Moubayed N
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Gianluca Stringhini其他文献

A Data Donation Approach for Youth Online Safety
青少年在线安全的数据捐赠方法
  • DOI:
    10.2139/ssrn.4627341
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Afsaneh Razi;Ashwaq Alsoubai;J. Park;Xavier V. Caddle;Shiza Ali;Seunghyun Kim;Gianluca Stringhini;Munmun De Choudhury;Pamela J. Wisniewski
  • 通讯作者:
    Pamela J. Wisniewski
Enabling Contextual Soft Moderation on Social Media through Contrastive Textual Deviation
通过对比文本偏差在社交媒体上实现上下文软审核
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Pujan Paudel;Mohammad Hammas Saeed;Rebecca Auger;Chris Wells;Gianluca Stringhini
  • 通讯作者:
    Gianluca Stringhini
In the Press
在新闻界
  • DOI:
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Gianluca Stringhini
  • 通讯作者:
    Gianluca Stringhini
Edinburgh Research Explorer International comparison of bank fraud reimbursement: customer perceptions and contractual terms
爱丁堡研究探索者银行欺诈报销的国际比较:客户认知和合同条款
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ingolf Becker;Alice Hutchings;Ruba Abu;Ross Anderson;Nicholas Bohm;S. Murdoch;M. A. Sasse;Gianluca Stringhini
  • 通讯作者:
    Gianluca Stringhini
Enabling Privacy-preserving Multidimensional Network Telemetry with Autoencoders
使用自动编码器实现保护隐私的多维网络遥测

Gianluca Stringhini的其他文献

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{{ truncateString('Gianluca Stringhini', 18)}}的其他基金

Collaborative Research: SaTC: TTP: Medium: iDRAMA.cloud: A Platform for Measuring and Understanding Information Manipulation
协作研究:SaTC:TTP:中:iDRAMA.cloud:测量和理解信息操纵的平台
  • 批准号:
    2247868
  • 财政年份:
    2023
  • 资助金额:
    $ 12.52万
  • 项目类别:
    Continuing Grant
Collaborative Research: SaTC: CORE: Small: Flanker: Automatically Detecting Lateral Movement in Organizations Using Heterogeneous Data and Graph Representation Learning
协作研究:SaTC:核心:小型:侧翼:使用异构数据和图表示学习自动检测组织中的横向运动
  • 批准号:
    2127232
  • 财政年份:
    2021
  • 资助金额:
    $ 12.52万
  • 项目类别:
    Standard Grant
Collaborative Research: SaTC: CORE: Small: Detecting Accounts Involved in Influence Campaigns on Social Media
协作研究:SaTC:核心:小型:检测参与社交媒体影响力活动的帐户
  • 批准号:
    2114407
  • 财政年份:
    2021
  • 资助金额:
    $ 12.52万
  • 项目类别:
    Standard Grant
CAREER: Towards Data-Driven Methods to Counter Online Aggression
职业:寻找数据驱动的方法来对抗网络攻击
  • 批准号:
    1942610
  • 财政年份:
    2020
  • 资助金额:
    $ 12.52万
  • 项目类别:
    Continuing Grant

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