International Business Machines Corporation

Hcl Technologies Limited

Xin Hu

Ting Wang

Jiyong Jang

Douglas Lee Schales

Marc Philippe Stoecklin

Reiner Sailer

Marc Ph Stoecklin

Mihai Christodorescu

Theodoros Salonidis

Wentao Huang

Andrew Maxwell White

Josyula R Rao

Teryl Paul Taylor

Fabian Newman Monrose

Stefan Berger

Jialong Zhang

Yangyi Chen

Dimitrios Pendarakis

Suresh N Chari

Lawrence H Koved

Kapil Kumar Singh

Pau-Chen Cheng

Dimitrious Pendarakis

An encoder includes a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generate a key, estimate a network capacity, and encode each bit of the key using a random matrix of a selected rank and the estimated network capacity for secure transmission of the key through a network.

Method for efficient and practical key distribution in network coding systems

Unknown and reference signatures are accessed. The unknown and reference signatures indicate patterns that correspond to known threats to resources (such as computer systems and/or computer networks) in a computer environment and comprise a multitude of descriptive elements having information describing different aspects of a corresponding signature. A set of similarity measures is created of the unknown and reference signatures from different perspectives, each perspective corresponding to a descriptive element. The set of similarity measures are integrated to generate an overall similarity metric. The overall similarity metric is used to find appropriate categories in the reference signatures into which the unknown signatures should be placed. The unknown signatures are placed into the appropriate categories to create a mapping from the unknown signatures to the reference signatures. The mapping is output for use by an IDPS for determining whether a threat has occurred to the resources in the computer environment.

Automatic categorization of IDPS signatures from multiple different IDPS systems

An encoder including a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to construct an encoded message using a message and a random element, construct a hash using a shared secret, and transmit the encoded message and the hash to a destination, through a network.

Method and system for rateless and pollution-attack-resilient network coding

A method for improving a detection of beaconing activity includes receiving input data into a computer-implemented processing procedure at least one listing of at least one of time series data and candidate periods of potential beaconing activity. The input data is processed, to detect candidates of potential beaconing activity. By further evaluating the time series data using techniques used for evaluating an analog signal, the performance of detecting of potential beaconing activity is improved to eliminate false positive indications of beaconing activity and/or to provide indication of multiple interleaved periodicities of beaconing.

Methods and systems for improving beaconing detection algorithms

A command endpoint used by Domain Generation Algorithm (DGA) malware is identified using machine learning-based clustering. According to this technique, at least one attribute associated with a candidate resolved DNS name is identified. The candidate resolved DNS name has associated therewith a set of names that are failed DNS lookups but that cluster with the candidate resolved DNS name. A set of additional names that share the at least one attribute with the candidate resolved DNS name are then identified. For the set of additional names, an extent to which the set of additional names also clusters with the set of names that are failed DNS lookups is then determined. The candidate resolved DNS name is characterized as associated with the command endpoint when the set of additional names cluster with the set of names that are failed DNS lookups to a configurable degree.

Identifying command and control endpoint used by domain generation algorithm (DGA) malware

A decoder deployed in one or more terminals, includes a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to receiving a noisy message and a noisy hash from the network, searching for a pair of matching candidates for the hash and message from two row spaces of noisy message vectors using a shared secret with an encoder, and outputting, by the decoder, a decoded message if the searching is successful.

Method and system for rateless and pollution-attack-resilient network coding including decoder(s)

A method includes receiving, at an input port of a computer, indication of HTTP (Hypertext Transfer Protocol) traffic and clustering, using a processor on the computer, the HTTP traffic according to a client IP (Internet Protocol) into a web session tree. A client tree structure of the web session tree is generated and the client tree structure is compared with tree structures of exploit kit samples.

Detecting web exploit kits by tree-based structural similarity search

An encoder including a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generating a message by aggregating a plurality of incoming packets, constructing an encoded message using the message and a random matrix, constructing of a hash using a shared secret, and transmitting the encoded message and the hash to a destination, through a network that performs network coding operations.

An encoder including a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generate a k-bit key, where k is a positive integer, estimate an upper bound of a number of eavesdropped links, encode each bit of the k-bit key using a random matrix of a selected rank, and transmit the encoded k-bit key through a network that performs linear operations on packets.

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

A method includes collecting system calls and call parameters invoked by monitored applications for target computer systems. The system calls and call parameters are received from operating system kernels on the plurality of target computer systems. Sequences of systems calls and call parameters of the monitored applications are correlated among different target computer systems to deduce malicious activities. Remedial action(s) are performed in response to malicious activities being deduced as being malicious by the correlating. Another method includes determining that network activity at a specific time is deemed to be suspicious. Using IP addresses involved in the suspicious network activity, computer system(s) are determined that are sources of the suspicious network activity. Based on the specific time and the determined computer system(s), application(s) are determined that are executing on the determined computer system(s) that are causing the suspicious network activity. Remedial action(s) are performed for the determined computer system(s).

Computer implemented techniques for detecting, investigating and remediating security violations to IT infrastructure

A method (and structure) includes receiving, as input data into a computer-implemented processing procedure, at least one listing of at least one of time series data and potential candidate periods of potential beaconing activity. The input data is processed, using a processor on a computer, to evaluate the input data as if the input data represents data points of an input analog signal subject to principles of communication theory and having determinable statistical characteristics.

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White Plains, NY, United States of America

Xin Hu