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    • 4. 发明授权
    • Predictive blacklisting using implicit recommendation
    • 使用隐含推荐的预测黑名单
    • US08572746B2
    • 2013-10-29
    • US12691631
    • 2010-01-21
    • Athina MarkopoulouFabio SoldoAnh Le
    • Athina MarkopoulouFabio SoldoAnh Le
    • H04L29/06
    • H04L63/1441G06F21/552H04L63/0263
    • A method is provided for determining a rating of a likelihood of a victim system receiving malicious traffic from an attacker system at a point in time. The method comprises: generating a first forecast from a time series model based on past history of attacks by the attacker system; generating a second forecast from a victim neighborhood model based on similarity between the victim system and peer victim systems; generating a third forecast from a joint attacker-victim neighborhood model based on correlation between a group of attacker systems including the attacker system and a group of victim systems including the victim system; and determining the rating of the likelihood of the victim system receiving malicious traffic from the attacker system at the point in time based on the first forecast, the second forecast, and the third forecast.
    • 提供了一种用于确定受害者系统在某个时间点从攻击者系统接收恶意流量的可能性的等级的方法。 该方法包括:基于攻击者系统的攻击历史,从时间序列模型生成第一预测; 基于受害者系统和同伴受害者系统之间的相似性,从受害者邻域模型生成第二预测; 基于包括攻击者系统在内的一组攻击者系统和包括受害者系统在内的一组受害者系统之间的相关性,从联合攻击者 - 受害者邻域模型产生第三预测; 以及基于所述第一预测,所述第二预测和所述第三预测,确定所述受害者系统在所述时间点从所述攻击者系统接收到恶意流量的可能性的等级。
    • 6. 发明申请
    • PREDICTIVE BLACKLISTING USING IMPLICIT RECOMMENDATION
    • 使用隐含建议的预测黑名单
    • US20110179492A1
    • 2011-07-21
    • US12691631
    • 2010-01-21
    • ATHINA MARKOPOULOUFabio SoldoAnh Le
    • ATHINA MARKOPOULOUFabio SoldoAnh Le
    • G06F21/00G06F17/18
    • H04L63/1441G06F21/552H04L63/0263
    • A method is provided for determining a rating of a likelihood of a victim system receiving malicious traffic from an attacker system at a point in time. The method comprises: generating a first forecast from a time series model based on past history of attacks by the attacker system; generating a second forecast from a victim neighborhood model based on similarity between the victim system and peer victim systems; generating a third forecast from a joint attacker-victim neighborhood model based on correlation between a group of attacker systems including the attacker system and a group of victim systems including the victim system; and determining the rating of the likelihood of the victim system receiving malicious traffic from the attacker system at the point in time based on the first forecast, the second forecast, and the third forecast.
    • 提供了一种用于确定受害者系统在某个时间点从攻击者系统接收恶意流量的可能性的等级的方法。 该方法包括:基于攻击者系统的攻击历史,从时间序列模型生成第一预测; 基于受害者系统和同伴受害者系统之间的相似性,从受害者邻域模型生成第二预测; 基于包括攻击者系统在内的一组攻击者系统和包括受害者系统在内的一组受害者系统之间的相关性,从联合攻击者 - 受害者邻域模型产生第三预测; 以及基于所述第一预测,所述第二预测和所述第三预测,确定所述受害者系统在所述时间点从所述攻击者系统接收到恶意流量的可能性的等级。