International Business Machines Corporation

Mihai Christodorescu

Reiner Sailer

Douglas Lee Schales

Ting Wang

Xin Hu

Marc Ph Stoecklin

Andrew Maxwell White

Dimitrios Pendarakis

Ashish Kundu

Ajay Mohindra

Marc Philippe Stoecklin

Josyula R Rao

Kapil Kumar Singh

Marc Stoecklin

Wietse Z Venema

Srinivasan Parthasarathy

Lisa Amini

Mitchell Adam Cohen

Andrew Joseph Davidson

Oliver Verscheure

Najwa Aaraj

Dmytro Korzhyk

The present principles are directed to identifying and classifying web traffic inside encrypted network tunnels. A method includes analyzing network traffic of unencrypted data packets to detect packet traffic, timing, and size patterns. The detected packet, timing, and size traffic patterns are correlated to at least a packet destination and a packet source of the unencrypted data packets to create at least one of a training corpus and a model built from the training corpus. The at least one of the corpus and model is stored in a memory device. Packet traffic, timing, and size patterns of encrypted data packets are observed. The observed packet traffic, timing, and size patterns of the encrypted data packets are compared to at least one of the training corpus and the model to classify the encrypted data packets with respect to at least one of a predicted network host and predicted path information.

Identification and classification of web traffic inside encrypted network tunnels

Systems and methods for protection from buffer overflow vulnerability due to placement new constructs in C++ are provided. A system for protecting from buffer overflow vulnerability due to placement new constructs, comprises a compiler which is capable of receiving a program including a placement new instruction, and runtime which is capable of receiving binary code from the compiler and determining whether the program includes the placement new instruction and whether the placement new instruction would lead to buffer overflow, wherein the runtime is linked to a library including methods for preventing the buffer overflow, and selects a method for preventing the buffer overflow if the runtime determines that the placement new instruction would lead to the buffer overflow.

System and method for protection from buffer overflow vulnerability due to placement new constructs in C++

A distributed feature collection and correlation engine is provided, Feature extraction comprises obtaining one or more data records; extracting information from the one or more data records based on domain knowledge; transforming the extracted information into a key/value pair comprised of a key K and a value V, wherein the key comprises a feature identifier; and storing the key/value pair in a feature store database if the key/value pair does not already exist in the feature store database using a de-duplication mechanism. Features extracted from data records can be queried by obtaining a feature store database comprised of the extracted features stored as a key/value pair comprised of a key K and a value V, wherein the key comprises a feature identifier; receiving a query comprised of at least one query key; retrieving values from the feature store database that match the query key; and returning one or more retrieved key/value pairs.

Distributed feature collection and correlation engine

A method of converting an original application into a cloud-hosted application includes splitting the original application into a plurality of application components along security relevant boundaries, mapping the application components to hosting infrastructure boundaries, and using a mechanism to enforce a privacy policy of a user. The mapping may include assigning each application component to a distinct virtual machine, which acts as a container for its assigned component.

Protection of user data in hosted application environments

A method for identifying an unknown user according to a plurality of facets of user activity in a plurality of contexts includes receiving a plurality of priors for the facets with respect to the contexts, receiving a plurality of footprints of known users, aggregating the footprints of the users to determine an ensemble prior, receiving a plurality of network traces relevant to an unknown user in a computer environment, matching the network traces against each of the footprints to determine a plurality of matches, aggregating the matches using the ensemble prior according to the facets and the contexts, and outputting a probable user identity for the unknown user.

User identification using multifaceted footprints

Performing adaptive cyber-security analytics including a computer implemented method that includes receiving a report on a network activity. A score responsive to the network activity and to a scoring model is computed at a computer. The score indicates a likelihood of a security violation. The score is validated and the scoring model is automatically updated responsive to results of the validating. The network activity is reported as suspicious in response to the score being within a threshold of a security violation value.

Adaptive cyber-security analytics

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Briarcliff Manor, NY, United States of America

Mihai Christodorescu