Microsoft Technology Licensing, LLC

Mathias Scherman

Royi Ronen

Tomer Koren

Roy Levin

Yotam Livny

Dotan Patrich

Hanan Shteingart

Tomer Teller

Daniel Mark Edwards

Malicious computer behavior is detected automatically based on a user session. A user session comprising a sequence of process events is identified and a text-based representation is generated, wherein process events correspond to words and a sequence of words corresponds to a sentence. Subsequently, a text-based classifier classifies the session as malicious or non-malicious based on the sequence of events within the session in the text representation.

Automatic malicious session detection

Enhancements to network security are provided by identifying malicious actions taken against servers in a network environment, without having to access log data from individual servers. Seed data are collected by an administrator of the network environment, from honeypots and servers whose logs are shared with the administrator, to identify patterns of malicious actions to access the network environment. These patterns of use include ratios of TCP flags in communication sessions, entropy in the use of TCP flags over the life of a communication session, and packet size metrics, which are used to develop a model of characteristic communications for an attack. These attack models are shared with servers in the network environment to detect attacks without having to examine the traffic logs of those servers.

Network attack detection

Methods, systems, and apparatuses are provided for evaluating a chain of alerts. Historical alerts may be grouped together to form sets of alerts based on a predetermined relationship between the alerts. A score is determined for each set of alerts representing a statistical likelihood that one alert in the set is correlated to another alert in the set, generating a plurality of scores for the sets of alerts. The scores may be placed into a model containing a score for each set of alerts. After the model is formed, a received chain of alerts may be evaluated by examining whether the chain of alerts, or a sub-chain of alerts, corresponds to a score in the model through an iterative process. If the chain of alerts corresponds to a score in the model and meets a predetermined criteria, a system administrator can be alerted of the chain of alerts.

Validating correlation between chains of alerts using cloud view

One embodiment illustrated herein includes a computer implemented method. The method includes acts for training an amplification attack detection system. The method includes obtaining a plurality of samples of IPFIX data. The method further includes using the IPFIX data to create a plurality of time-based, server samples on a per server basis such that each sample corresponds to a server and a period of time over which IPFIX data in the sample corresponds. The method further includes identifying a plurality of the server samples that are labeled positive for amplification attacks. The method further includes identifying a plurality of server samples that are labeled negative for amplification attacks. The method further includes automatically labeling at least some of the remaining server samples as positive or negative based on the previously identified labeled samples. The method further includes using the automatically labeled samples to train an amplification attack detection system.

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Tel Aviv, Israel

Mathias Scherman