Verint Systems, Inc.

Assaf Yosef Keren

Yuval Altman

Ido Krupkin

Pinhas Rozenblum

Malware detection techniques that detect malware by identifying the C&C communication between the malware and the remote host, and distinguish between communication transactions that carry C&C communication and transactions of innocent traffic. The system distinguishes between malware transactions and innocent transactions using malware identification models, which it adapts using machine learning algorithms. However, the number and variety of malicious transactions that can be obtained from the protected network are often too limited for effectively training the machine learning algorithms. Therefore, the system obtains additional malicious transactions from another computer network that is known to be relatively rich in malicious activity. The system is thus able to adapt the malware identification models based on a large number of positive examples&#x2014;The malicious transactions obtained from both the protected network and the infected network. As a result, the malware identification models are adapted with high speed and accuracy.

System and method for malware detection learning

Methods and systems for automated generation of malicious traffic signatures, for use in Intrusion Detection Systems (IDS). A rule generation system formulates IDS rules based on traffic analysis results obtained from a network investigation system. The rule generation system then automatically configures the IDS to apply the rules. An analysis process in the network investigation system comprises one or more metadata filters that are indicative of malicious traffic. An operator of the rule generation system is provided with a user interface that is capable of displaying the network traffic filtered in accordance with such filters.

Growing community of inventors

Ramat Gan, Israel

Assaf Yosef Keren