CAREER: Automatic Learning of Adaptive Network-Centric Malware Detection Models

职业:自适应网络中心恶意软件检测模型的自动学习

基本信息

项目摘要

Malicious software (a.k.a. malware) is at the basis of most cyber-criminal operations, causing significant financial loss and posing great risks to national security.This research creates novel network-centric behavior-based malware detection systems that automatically learn how to identify malware-compromised machines within a network, and that can self-tune to achieve the best possible trade-off between malware detection rate and false alarms for a given network. This self-tuning property is achieved by combining models of malware-generated network traffic with models of legitimate user-generated network activities to build hybrid detection models that can adapt to a specific network environment and accurately detect malware-generated network traffic crossing the network perimeter.This new approach to malware detection takes into account events that occur within an entire network, rather than focusing on events that occur at each single host, and focuses on adaptive detection of all types of malware, rather than being limited to a specific malware type (e.g., botnets). Therefore, the detection systems resulting from this research will provide new effective detection capabilities that can complement current anti-malware technologies and significantly contribute to a better defense-in-depth strategy against malware.
恶意软件(又称恶意软件)是大多数网络犯罪活动的基础,造成了巨大的经济损失,并对国家安全构成了巨大的风险。这项研究创建了新的以网络为中心的基于行为的恶意软件检测系统,该系统可以自动学习如何识别网络中受恶意软件危害的机器,并且可以自我调整,以实现给定网络的恶意软件检测率和误报之间的最佳权衡。这种自调整特性是通过将恶意软件生成的网络流量模型与合法用户生成的网络活动模型相结合来实现的,以构建混合检测模型,该混合检测模型可以适应特定的网络环境并准确地检测恶意软件生成的跨越网络边界的网络流量。这种新的恶意软件检测方法考虑了整个网络内发生的事件,而不是集中于在每个单个主机处发生的事件,并且集中于对所有类型的恶意软件的自适应检测,而不是限于特定的恶意软件类型(例如,僵尸网络)。因此,从这项研究中产生的检测系统将提供新的有效的检测功能,可以补充当前的反恶意软件技术,并显着有助于更好的防御恶意软件的深度策略。

项目成果

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Roberto Perdisci其他文献

Roberto Perdisci的其他文献

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{{ truncateString('Roberto Perdisci', 18)}}的其他基金

Collaborative Research: SaTC: CORE: Medium: Defending Against Social Engineering Attacks with In-Browser AI
协作研究:SaTC:核心:中:利用浏览器内人工智能防御社会工程攻击
  • 批准号:
    2126641
  • 财政年份:
    2021
  • 资助金额:
    $ 40.26万
  • 项目类别:
    Standard Grant
EAGER: Collaborative: Leveraging High-Density Internet Peering Hubs to Mitigate Large-Scale DDoS Attacks
EAGER:协作:利用高密度互联网对等中心缓解大规模 DDoS 攻击
  • 批准号:
    1741608
  • 财政年份:
    2017
  • 资助金额:
    $ 40.26万
  • 项目类别:
    Standard Grant
TWC: Medium: Collaborative: Exposing and Mitigating Cross-Channel Attacks that Exploit the Convergence of Telephony and the Internet
TWC:媒介:协作:揭露和缓解利用电话和互联网融合的跨渠道攻击
  • 批准号:
    1514052
  • 财政年份:
    2015
  • 资助金额:
    $ 40.26万
  • 项目类别:
    Standard Grant
SDCI Sec: Passive and Active DNS Monitoring Tools for Detecting and Tracking the Evolution of Malicious Domain Names
SDCI Sec:用于检测和跟踪恶意域名演变的被动和主动 DNS 监控工具
  • 批准号:
    1127195
  • 财政年份:
    2011
  • 资助金额:
    $ 40.26万
  • 项目类别:
    Standard Grant

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