Fireeye, Inc.

Ali Islam

Zheng Bu

Ashar Aziz

Todd Rosenberry

Atif Mushtaq

A method for detecting a cyber-attack by performing a first analysis on content within a first portion of a communication to determine whether the content includes a first high quality indicator. The first high quality indicator identifies a correlation of the content with a malicious activity. Subsequent to the first analysis, performing a second analysis on a second portion of the communication to determine one or more supplemental indicators. Thereafter, the communication is classified as part of a cyber-attack when (i) a value associated with the first high quality indicator exceeds a first threshold without consideration of the one or more supplemental indicators, or (ii) upon failing to exceed the first threshold and being greater than a second threshold, using the values representing the one or more supplemental indicators with the first value to classify the communication as being part of the cyber-attack.

Classifying sets of malicious indicators for detecting command and control communications associated with malware

A system and method for detecting malicious activity through one or more local analyzers and a central analyzer. The local analyzer captures packets that are part of communications over a network, generates a signature from information obtained from one or more of the captured packets, and determines whether the signature matches any signature of a first plurality of signatures stored in a first storage device that is accessible to the first local analyzer. The central analyzer remotely receives a portion of the information and the signature from the first local analyzer in response to the signature failing to match any of the signatures stored in the first storage device. The central analyzer determines whether the signature matches any global signature stored within a second storage device that is accessible to the central analyzer.

Distributed systems and methods for automatically detecting unknown bots and botnets

One embodiment of a method for detecting a cyber-attack features first and second analyzes. The first analysis is conducted on content of a communication to determine at least a first high quality indicator. The first high quality indicator represents a first probative value for classification. The second analysis is conducted on metadata related to the content to determine supplemental indicator(s). Each of the supplemental indicator(s) is represented by a probative value for classification. The communication is classified as being part of the cyber-attack when the first probative value exceeds a predetermined threshold without consideration of the corresponding probative values for the supplemental indicator(s). In response to the first high quality indicator failing to classify the network communication, using the corresponding probative values associated with the one or more supplemental indicators with at least the first probative value to classify the network communication as being part of the cyber-attack.

System and method for detecting malicious activity and classifying a network communication based on different indicator types

Techniques may automatically detect bots or botnets running in a computer or other digital device by detecting command and control communications, called 'call-backs,' from malicious code that has previously gained entry into the digital device. Callbacks are detected using an approach employing both a set of high quality indicators and a set of supplemental indicators. The high quality indicators are selected since they provide a strong correlation with callbacks, and may be sufficient for the techniques to determine that the network outbound communications actually constitute callbacks. If not, the supplemental indicators may be used in conjunction with the high quality indicators to declare the outbound communications as callbacks.

A decryption scheme for recover of a decrypted object without a cryptographic key is described. First, logical operation(s) are conducted on data associated with a first data string expected at a first location within an object having the predetermined format and data within the encrypted object at the first location to recover data associated with a portion of a cryptographic key from the encrypted object. Thereafter, logical operation(s) are conducted on that data and a first portion of the encrypted object at a second location to produce a result. Responsive to the result including data associated with the plaintext version of the second data string, logical operation(s) are conducted on a second portion of the encrypted object and the data associated with the plaintext version of the second data string to recover data associated with the cryptographic key. Thereafter, the encrypted object may be decrypted using the cryptographic key.

System, apparatus and method for conducting on-the-fly decryption of encrypted objects for malware detection

Techniques may automatically detect bots or botnets running in a computer or other digital device by detecting command and control communications, called 'call-backs,' from malicious code that has previously gained entry into the digital device. Callbacks are detected using a distributed approach employing one or more local analyzers and a central analyzer. The local analyzers capture packets of outbound communications, generate header signatures, and analyze the captured packets using various techniques. The techniques may include packet header signature matching against verified callback signatures, deep packet inspection. The central analyzer receives the header signatures and related header information from the local analyzers, may perform further analysis (for example, on-line host reputation analysis); determines using a heuristics analysis whether the signatures correspond to callbacks; and generally coordinates among the local analyzers.

According to one embodiment, a computerized method comprises receiving an encrypted object and conducting a first static scanning operation on the encrypted object to decrypt the encrypted object in real-time. Thereafter, a second static scanning operation is conducted on the decrypted object to determine whether the decrypted object is suspected of including malware. Based on the results of the second static scanning operation, the decrypted object may be classified to identify a suspected presence of malware.

Growing community of inventors

Milpitas, CA, United States of America

Ali Islam